Emerging Technologies for Precision Crop Management Towards Agriculture 5.0: A Comprehensive Overview

Abstract

Agriculture 5.0 (Ag5.0) represents a groundbreaking shift in agricultural practices, addressing the global food security challenge by integrating cutting-edge technologies such as artificial intelligence (AI), machine learning (ML), robotics, and big data analytics. To adopt the transition to Ag5.0, this paper comprehensively reviews the role of AI, machine learning (ML) and other emerging technologies to overcome current and future crop management challenges. Crop management has progressed significantly from early agricultural methods to the advanced capabilities of Ag5.0, marking a notable leap in precision agriculture. Emerging technologies such as collaborative robots, 6G, digital twins, the Internet of Things (IoT), blockchain, cloud computing, and quantum technologies are central to this evolution. The paper also highlights how machine learning and modern agricultural tools are improving the way we perceive, analyze, and manage crop growth. Additionally, it explores real-world case studies showcasing the application of machine learning and deep learning in crop monitoring. Innovations in smart sensors, AI-based robotics, and advanced communication systems are driving the next phase of agricultural digitalization and decision-making. The paper addresses the opportunities and challenges that come with adopting Ag5.0, emphasizing the transformative potential of these technologies in improving agricultural productivity and tackling global food security issues. Finally, as Agriculture 5.0 is the future of agriculture, we highlight future trends and research needs such as multidisciplinary approaches, regional adaptation, and advancements in AI and robotics. Ag5.0 represents a paradigm shift towards precision crop management, fostering sustainable, data-driven farming systems that optimize productivity while minimizing environmental impact.

1. Introduction

Population statistics indicate a substantial rise in the global population, projected to reach 10 billion by 2050. As a result, the global challenges of energy and water scarcity, food security, and climate change are expected to intensify [1,2]. Agricultural industries, as one of the primary sectors addressing these challenges, must undergo both quantitative and qualitative development. In addition to these challenges, the agricultural sector faces the migration of workers to more profitable industries, coupled with the increasing average age of farmers [3]. Consequently, there is an urgent need for scientists and agricultural experts to develop more innovative and sustainable food production systems to address these issues and meet the growing population’s nutritional needs. Agricultural experts have developed innovative monitoring strategies to address these challenges, including the optimal monitoring of nutrients, remote sensing systems, global information, global positioning, and variable-rate applications. These strategies are collectively known as precision agriculture (PA) or precision farming. Precision agriculture involves applying the right actions—such as irrigation or fertilization—in the right way, at the right place, and at the right time, to increase productivity and quality while minimizing waste [4]. Furthermore, the concept of “smart agriculture” has emerged, which integrates digitization and artificial intelligence (AI) technologies into agriculture [5]. The application of precision and smart farming techniques not only enhances productivity but also improves profit margins.

AI applications, particularly those related to machine learning (ML) technologies and the IoT, have seen remarkable growth in recent years. The emergence of various epidemics and pandemics, such as COVID-19, has accelerated the global prominence of these technologies [6,7]. Machine learning refers to AI methods that enable machines to perform specific tasks and solve problems without explicit programming [6,8]. In essence, these techniques allow a machine (or computer) to be “trained” on data related to a particular problem or task, enabling it to perform or solve that problem effectively.

The significant advancements in computing power and big data have facilitated the development of a more complex and applied form of machine learning—“deep learning” [9,10]. Deep-learning (DL) applications have revolutionized fields such as object detection [11,12], powdery food identification [13], plant nutrient status detection [14], leaf area measurement [15], spore detection [16], water stress [17,18], fruit quality [19], and other complex tasks involving large-scale data analysis [20]. The performance of computer systems in these applications has reached exceptional levels, enabling them to either fully replace or significantly assist human involvement in certain operations with remarkable success. These technologies have permeated various sectors, including agriculture, where AI, ML, DL, Internet of Things (IoT), and other digital technologies have been widely adopted, giving rise to the concept of “smart agriculture” and the aspiration to provide the fifth generation of agriculture (Agriculture 5.0 (Ag5.0)) [21].

Ag5.0 is an emerging innovative agricultural version that aims to address issues that Agriculture 4.0 failed to address. Ag5.0 is a subcategory of Industry 5.0 [22], an emerging concept to adopt that entails the integration of emerging technologies such as artificial intelligence, IoT, big data analytics, digital twins, blockchain, 6G networks, robotics, and human–machine interaction to handle various agricultural processes. One of the most important agricultural practices that are expected to benefit from Agriculture 5.0 technologies is crop monitoring and management.

Tracking and monitoring crops from germination to harvest is a key aspect of smart agriculture, crucial for optimizing both the quantitative and qualitative productivity of crops. To improve the efficiency of food production systems, assessing seed germination is essential for comparing the performance of different seed batches and selecting the most effective one for sowing [23]. Furthermore, continuous monitoring of various stages of plant development is vital. The existing literature provides extensive insights into the application of AI, ML, and DL techniques in crop monitoring, including growth assessment, monitoring of nutritional status, disease detection, crop water status, and yield prediction. Accordingly, this article offers a comprehensive review of the role of AI, being one of the backbones of Ag5.0 in addressing current and future challenges related to detecting, tracking, and managing crops throughout their growth stages.

This paper provides a comprehensive exploration of the role of AI in advancing agriculture towards Ag5.0, focusing on the integration of ML, DL, and AI-driven robotics. It begins by tracing the evolution of crop detection from the traditional methods of Agriculture 1.0 to the cutting-edge technologies of smart farming and Ag5.0. The paper then reviews the enabling technologies that facilitate the transition to Ag5.0, offering insights into the critical technological infrastructure required for this shift. It further examines how AI, ML, and contemporary agricultural technologies can be leveraged to monitor, analyze, and optimize key stages of crop growth and management, highlighting their role in improving efficiency and precision. The manuscript includes a detailed evaluation of the practical applications of machine learning in crop monitoring and protection, demonstrating its tangible impact on agricultural practices. It also discusses emerging technologies, such as smart sensors, advanced devices, communication systems, and AI-based robotics, which are driving the next generation of digitalization, decision-making, and automation in agriculture. In conclusion, the paper addresses the opportunities and challenges associated with adopting Ag5.0, outlining both the potential benefits and the obstacles to its widespread implementation. Ultimately, this paper emphasizes the transformative potential of Ag5.0 and underscores the importance of embracing AI-driven technologies to promote sustainability, productivity, and food security in the agricultural sector.

To accomplish this review, we focused on international and reliable scientific databases to ensure the comprehensiveness and quality of the selected literature. The databases used included Web of Science, IEEE Xplore, MDPI, and Science Direct. Regarding the selection of literature, studies published between 2015 and 2024 were included unless we had to use a more ancient study. All the selected studies focused on the applications of agricultural technologies in agriculture and crop management, especially artificial intelligence applications, as it is one of the most critical backbones for adopting the trend towards Agriculture 5.0. Keywords such as “agriculture”, “artificial intelligence”, “Internet of Things”, “agricultural collaborative robots”, “machine learning applications in agriculture”, “deep learning applications in agriculture”, and so on were used to ensure the improvement of search results. The criteria for the selected studies focused on articles published in international scientific journals and conference papers that were peer-reviewed to ensure scientific accuracy and research authority, while the literature that was not directly related to the review topic or of low scientific quality, such as preprints that were not peer-reviewed, were excluded.

2. From Traditional Agriculture to Smart Farming and Agriculture 5.0

Figure 1 shows the development roadmap for the agricultural revolution from Agriculture 1.0 to Ag5.0. Over time, humans have developed various methods to manage and monitor crops during growing seasons, addressing both positive (growth, fruiting) and negative (diseases, pests, nutrient deficiencies) changes. From the 19th century until around 1950, “Agriculture 1.0” relied on manual labor and traditional tools, which limited productivity and efficiency in crop monitoring [24]. In the 1950s, the introduction of mechanized farming equipment and synthetic pesticides marked the transition to “Agriculture 2.0” [25]. This shift resulted in increased productivity and lower production costs but also led to environmental concerns. By the late 20th century, the development of embedded systems, communication technologies, and data modeling led to “Agriculture 3.0” [26]. Precision agriculture emerged, with technologies like global navigation satellite systems (GNSS), remote sensing, and geographic information systems (GIS) enabling site-specific monitoring that optimized resource use and improved sustainability. Recently, the integration of IoT, big data, and autonomous machines has given rise to “Agriculture 4.0” [27]. This phase focuses on precision crop monitoring with minimal human intervention, supported by AI, machine learning, and robotics.

Looking ahead, Ag5.0 is set to revolutionize crop monitoring further through autonomous systems, AI-driven decision support, and sustainable practices [28]. While its precise definition varies, Ag5.0 is widely understood as integrating AI, big data, robotics, and cloud computing to enhance crop monitoring and sustainability [29]. Hence, we can conclude that Ag5.0 includes the principles of precision agriculture and the use of AI algorithms and advanced robotic structures, including unmanned operations and autonomous decision support systems [30]. Ag5.0 aims to address emerging challenges such as climate change, irregular cropping seasons, and pest emergence [31]. With global population growth and the need for sustainable food production, AI, machine learning, and innovative technologies will be key to achieving efficient, eco-friendly crop monitoring [32]. The next phase must minimize chemical inputs and introduce new, sustainable methods to ensure long-term food security.

Table 1. The main differences between the different agriculture versions.

Version	Features	Focus and Major Issues	Major Driving Factors	Information and Cybersecurity Issues
Agriculture 1.0	Traditional Agriculture dominated by manpower and animal forces	Human-centric, unsustainable low performance, and not resilient	N/A	N/A
Agriculture 2.0	Agriculture mechanization	Machine-focused, machinery and chemicals usage, unsustainable not resilient	Industrial revolution	N/A
Agriculture 3.0	Automatic agriculture with high-speed development	Technology-focused, Computers, programs, Unsustainable not resilient, and cybersecurity issues	Invention of computers, robotics, programming	Systems security Network security Devices security
Agriculture 4.0	Smart agriculture featured by AI and IoT	Automation-focused Smart systems/devices, renewable energies, sustainable, not resilient, efficient, and cybersecurity issues	Introduction of AI, IoT, cloud computing, and big data	Data security, systems security, network security, security devices, cloud security
Agriculture 5.0	Human-focused agriculture featured AI, IoT, robotics and Human–machine interactions	Human-centered highly sustainable resilient societal well-being cybersecurity issues	Chronic social issues. resilience need, increasing, consumer demands, and unsustainable production	Data security, systems security, network security, security devices, cloud security, and Human–machine security

shows the main differences between the different agriculture versions.

3. Enabling Technologies for Agriculture 5.0

The enabling technologies of Agriculture 5.0 (Ag5.0), which build on advances in Agriculture 4.0, will contribute to addressing some of the current global challenges of producing sufficient, affordable, and healthy food while protecting ecosystems [33]. Ag5.0 integrates a range of advanced technologies and biotechnologies to promote sustainability, enhance human–machine collaboration, and develop circular agricultural production systems. Among the key enabling technologies is collaborative robotics (cobots), which are designed to perform labor-intensive, repetitive, and hazardous tasks. By automating these processes, cobots not only improve production efficiency but also make agriculture more appealing to younger generations, helping to address labor shortages in the sector [34]. Moreover, 6G technology, with its capability of delivering up to 1 Tbps throughput, extends IoT connectivity through higher data rates, minimal latency, and broader coverage. This allows for enhanced AI-based monitoring and precision farming techniques, improving the management of agricultural resources [34]. Digital twin technologies integrate data from weather, farm operations, environmental conditions, and supply chains, facilitating predictive analysis. This data integration supports informed decision-making, ultimately fostering more sustainable and resilient agricultural systems [35]. The Internet of Things (IoT) plays a central role in driving data-driven, automated farming practices. By optimizing resource utilization, reducing waste, and boosting productivity, IoT technologies contribute significantly to the efficiency and sustainability of modern farming practices [33,36]. AI further enhances agriculture by supporting intelligent automation, enabling rapid decision-making, ensuring quality assurance, and improving operational efficiency across various stages of farming. Big data analytics aid in real-time monitoring, enhancing decision-making capabilities and enabling the development of tailored solutions to meet specific agricultural needs. Edge computing reduces latency and enhances cybersecurity, ensuring seamless data flow and improving the interoperability of systems. Meanwhile, blockchain technologies ensure secure and transparent monitoring of IoT systems, allowing for traceability and accountability in agricultural practices. Cloud computing enables improved data storage and monitoring, supports collaboration among stakeholders, and offers cost-effective solutions to manage agricultural operations on scale. Finally, quantum technologies, with their potential for ultra-fast data transmission and enhanced security, promise to push the boundaries of data processing and encryption, offering unprecedented capabilities for Ag5.0 applications. These technologies enable Ag5.0 to revolutionize sustainable agricultural practices through advanced automation, data integration, and intelligent decision-making [22].

4. Perception, Analysis, and Actuation of Precision Crop Monitoring

Figure 2 shows the perception, analysis, and actuation stages of precision crop monitoring. Emerging crop monitoring technologies aim to identify plant conditions and symptoms during different growth stages, enabling site-specific chemical or mechanical control actions [37]. This process involves three main stages: Perception Stage: Crop-related data are collected through sensors and cameras, either on-ground or via remote sensing platforms like UAVs. UAVs, equipped with digital, multispectral, hyperspectral, thermal, light detection and ranging (LiDAR), radar, or sonar sensors, enable large-scale, rapid crop monitoring. These technologies detect symptoms and disorders at different stages of growth, including diseases and nutrient deficiencies. Analysis Stage: Acting as the critical link between perception and actuation, this stage involves processing crop data using advanced AI, machine learning, and deep-learning algorithms. These techniques evaluate plant conditions in real-time, such as growth stages, nutrient deficiencies, diseases, and environmental factors like weather and soil properties [38]. Machine learning leverages high-level data features to build predictive models, classify images, and propose solutions, with CPU, graphics processing units (GPU), and tensor processing units (TPU) technology advancements enabling more complex analyses. Actuation Stage: This stage involves applying precision crop monitoring strategies based on insights from the analysis phase. Smart devices with GNSS support execute site-specific actions, including autonomous crop service machines such as smart sprayers and agricultural robots. Prescription maps further enhance accuracy in crop management [39]. These technologies have revolutionized crop monitoring and management, providing effective solutions for detecting and addressing crop conditions, optimizing resource use, and enhancing precision agriculture.

5. Machine Learning Applications for Precision Crop Monitoring

AI is integral to Ag5.0, with its key subfields, ML and DL, playing critical roles. ML enables machines to learn automatically from data without explicit programming. ML is crucial for Ag5.0, aiding in crop yield prediction, plant disease monitoring, nutritional and water status assessment, weed detection, and forecasting adverse climatic conditions like rainfall, drought, and wind [40]. DL, a subset of both AI and ML, uses artificial neural networks (ANNs) inspired by human brain function for complex computations. DL methods such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and generative adversarial networks (GANs) are applied to specific agricultural challenges. CNNs, the most common DL architecture in precision agriculture, excel in computer vision tasks like image classification and object detection for crop protection [41]. Unlike traditional ML, CNNs autonomously extract features from image data, improving accuracy. Their performance depends on network depth, computational resources, and optimization algorithms like gradient descent and ReLU activation [42]. CNNs consist of three core layers. The input layer processes the raw data, which is split for training and testing [43,44]. The second layer includes convolutional, pooling, and fully connected layers. The convolutional layer extracts data and optimizes weights, while the pooling layer filters and selects the maximum value for the next layer. The fully connected layer processes the output from previous layers, applying complex functions to produce the final result. The output layer then converts this information into an equivalent score using statistical methods. Figure 3 illustrates the general architecture of CNN.

5.1. Germination Assessment

Precision crop monitoring begins at the germination stage, as seed germination assessment is essential for determining seed quality and predicting the crop’s initial yield. Traditional manual evaluation is time-consuming and prone to errors [45,46]. ML and computer vision techniques have recently been applied to automate seed monitoring. ElMasry et al. presented a computer-integrated multichannel spectral imaging system as a high-throughput phenotyping tool for the analysis of individual cowpea seeds harvested at different developmental stages. The developed linear discriminant analysis (LDA) was robust in classifying the seeds based on their germination capacity, with overall correct classification of 96.33% and 95.67% in the training and validation datasets, respectively [47]. Peng et al. developed an automated system for seed germination assessment using deep learning, incorporating a modified thermostat, digital camera, monitoring software, and a dense small-target detection algorithm (DDST-CenterNet). Their system demonstrated high efficiency, unaffected by seed background, lighting, or environmental factors, with strong scalability [48]. Genze et al. employed CNNs to evaluate maize, rye, and pearl millet germination, achieving predictive efficiencies of over 94% for rye and pearl millet and 97.9% for maize [45]. Similarly, Awty-Carroll et al. used the K-Nearest Neighbors (K-NN) algorithm to analyze diverse germination patterns in Miscanthus sinensis. K-NN achieved a validity score of 0.69–0.7 and an optimized ROC area of 0.89, demonstrating its reliability compared to traditional scoring methods [49]. Despite promising applications of machine learning and computer vision in seed germination assessment, significant research gaps remain. These include limited exploration of AI techniques and the need for more robust models capable of handling diverse environmental conditions and seed types. To advance with Ag5.0, the scientific community must prioritize developing scalable, efficient smart seed monitoring systems for broader agricultural use.

5.2. Diseases Detection and Crop Protection

Plant diseases cause significant global economic losses, accounting for 13% of crop yield reductions and 20–40% of food production losses [50,51]. Innovative methods for monitoring and diagnosing plant diseases are crucial to mitigate these losses. ML has recently emerged as a powerful tool for detecting plant diseases and safeguarding crops from threats like weeds and epidemics [52]. ML methodologies analyze plant biochemical changes and sensory data related to pathogen spread. Ground-based measurements using digital or spectral cameras and spectral fingerprint analysis are commonly employed. Classification and regression algorithms, combined with dimensionality reduction techniques like principal component analysis (PCA), are used to distinguish healthy plants from diseased ones and predict spectral responses of infected plants. Notable ML applications include Abdalla et al.’s integration of CNNs with Bidirectional Long Short-Term Memory (BiLSTM) networks using UAV images, achieving 89.7% accuracy with the ResNet101-BiLSTM model [53]. Ahmed and Yadav developed an automated system employing CNNs and support vector machines (SVMs) to identify nine plant diseases, including bacterial, viral, and fungal infections [54]. Orchi et al. conducted a comparative study between conventional machine-learning algorithms (SVM, LDA, KNN, CART, RF, and NB) and deep-learning algorithms (VGG16, VGG19, InceptionV3, ResNet50, and CNN) using PlantVillage Dataset to classify diseased and healthy leaves of crops. InceptionV3 achieved the best performance and the highest classification accuracy of 98.01% [55]. Wang et al. proposed a hybrid CNN-LSTM model to detect cucumber downy mildew, achieving a high predictive accuracy (R² = 91%) (Wang et al., 2024a) [16]. Nagachandrika et al. introduced a multi-scale fused feature approach using VGG16, Variational Autoencoders, and Visual Transformers, optimizing parameters with the Enhanced Gannet Optimization Algorithm, achieving over 94% classification accuracy for leaf diseases [56]. Guerrero-Ibañez and Reyes-Muñoz combined CNNs with generative adversarial networks to identify tomato leaf diseases, avoiding overfitting [57]. Tamilvizhi et al. introduced a quantum-behaved particle swarm optimization based on deep transfer learning (QBPSO-DTL) to detect and classify sugarcane leaf diseases with high accuracy, achieving 99.48% accuracy in validation and 89.65% accuracy in training. The integration of quantum computing and deep learning significantly improves disease classification accuracy [58]. For pest monitoring, Zhao et al. designed an automated system using deep learning and cameras, achieving a detection quality of 0.93 [59]. Lee et al. implemented a web application with the EfficientNet-b0 model for citrus insect classification, achieving 97% accuracy [60]. Dai et al. introduced a digital twin (DT) system for managing aphid pests through the integration of data and models. The framework provides a comprehensive approach to aphid management throughout the crop growth cycle. A predictive digital twin model was developed using a random forest algorithm optimized with a genetic algorithm to forecast aphid populations. Based on the twin data, a search strategy was employed to design pest control interventions, and decision optimization was applied to improve management efficiency. The system demonstrated a prediction accuracy of 85.73% when evaluated on a test dataset [61]. Despite significant progress in machine learning and deep learning for plant disease detection, several key challenges remain. These include limited data availability, difficulties in generalizing models across different crops and environments, and the integration of diverse data sources. To meet the demands of Ag5.0, the scientific community must focus on developing more adaptable, scalable, and data-efficient models. Overcoming these challenges will unlock the full potential of AI technologies for creating sustainable and efficient agricultural systems.

Table 2. An overview of the key research on ML applications in disease detection and crop protection.

Application	ML Algorithm	Dataset Type	Acc, %	Ref.
Coton diseases	CNN-BiLSTM	RGB	89.7	[53]
Detect diseases	CNN	RGB	94	[56]
Diagnosing plant diseases	CNN	RGB	90	[62]
Adzuki bean rust disease	CNN, ResNet-ViT, RMT	Multi-source	99	[63]
Cucumber downy mildew prediction	CNN-LSTM	RGB	91	[64]
Diagnosing plant diseases	SVM and CNN	RGB	99	[54]
Detection of plant diseases	ML and DL models	RGB	98	[55]
Rice diseases	GA and CNN	RGB	95	[65]
Tomato leaf disease	CNN	RGB	99	[57]
Tomato gray mold, cucumber downy mold, and cucumber powdery mildew spores	SVM	Fingerprint characteristics of diffraction–polarized	95	[66]
Detect brown spots in rice	CNN	RGB	97	[43]
Detection of apple diseases	CNN	RGB	97	[67]
Detection of cassava diseases	CNN	RGB	98	[68]
Wheat diseases	CNN	RGB	97	[69]
Detection of fusarium head blight disease	CNN	Hyperspectral	75	[60]
Tomato spotted wilt virus	CNN	Hyperspectral	96	[60]
Diseases and pests in tomatoes	ANN for regression, SVM	Spectral	99	[70]
Powdery mildew in wheat	PLSR, SVM, RF	Hyperspectral	85	[71]
Northern leaf blight in maize	CNNs	RGB	96	[72]
Tomato water stress	DNNs	RGB	Performed well	[73]
Tomato diseases and pests	Faster R-CNN, R-FCN, SSD, ResNet	RGB	90	[74]
Disease in Eggplant	CNN	RGB	99	[75]
Plant disease identification	CNN	RGB	99	[76]
Detection of grapevine esca disease	SIFT encoding and CNN	RGB	90	[77]
Grapevine yellows symptoms	CNN	RGB	99	[78]
Plant disease	CNN	RGB	99	[79]
Late blight in potato	RF and PLS-DA	Spectral	83	[80]
Laurel wilt	DT and MLP	Spectral	100	[81]
Bacterial spots in tomato	PCA and k-NN	Spectral	100	[82]
Anthracnose crown rot in strawberry	FDA, SDA, and kNN	Spectral	73	[83]
Rhizoctonia root and crown rot in sugar beet	PLS, RF, k-NN, and SVM	Hyperspectral	72	[84]
Potato virus y	CNN	Hyperspectral	88	[85]
Tobacco mosaic virus in tobacco	PLS-DA, RF, SVM, BPNN, and ELM	Hyperspectral	95	[86]
Bacterial blight in coffee	RF, SVM, and Naïve Bayes	Multi-spectral and thermal	75	[87]
Xylella fastidiosa infection in olive	LDA, SVM, RBF, and ensemble classifier	Hyperspectral and thermal	80	[88]
Fall armyworm (Spodoptera frugiperda) in cotton	Multiple ML algorithms	Hyperspectral	91	[89]
Pest monitoring	DPeNet, Faster R-CNN, SSD, and Yolov3	RGB	93	[59]
Citrus pest	EfficientNet-b0	RGB	97	[60]
Spotted spider mite in cotton	SVM	Multispectral	85	[90]
Plague species	CNN	RGB	75	[91]
Plague species in insect images	CNN	RGB	89	[92]

summarizes key research advancements in ML applications for disease detection and crop protection.

5.3. Weeds Detection

Weeds account for over 40% of global crop yield losses annually [93]. Managing weeds on a large scale is crucial to minimizing environmental and economic impacts, making automated weed monitoring systems essential for enhancing global food production. Recent advancements in automated systems have focused on differentiating crops from weeds. Almalky and Ahmed developed a deep-learning-based approach to classify the growth stages of Consolida regalis weeds, using YOLOv5, RetinaNet, and Faster R-CNN models. Their YOLOv5-small model achieved the best performance with a recall of 0.794 [94]. Similarly, Mu et al. hybridized ResNeXt and Faster R-CNN, achieving 95% accuracy in distinguishing between crop seedlings and weeds [95]. Zhu et al. introduced a YOLOX CNN-based weeding robot employing a blue laser, achieving 88.94% weed identification accuracy [96]. Drones are emerging as an efficient alternative to robotic and satellite imaging for weed monitoring, offering cost-effective, high-resolution field data collection. Islam et al. utilized drones equipped with RGB cameras to capture images and calculate vegetation indices (e.g., normalized red, green, and blue bands), mitigating the effects of varying lighting conditions [97]. Agricultural robots play a pivotal role in modern farming, performing tasks like laser weeding [98], pesticide spraying [99], spot picking [100], and fertilizer application. These robots range from modified tractors to aerial and small service robots [99]. Although automated weed monitoring systems have advanced significantly, important scientific gaps remain. These include the development of more robust models capable of handling diverse environmental conditions and crop variations, as well as the integration of various sensor technologies for real-time decision-making. To align with Ag5.0, the scientific community must enhance the accuracy, efficiency, and scalability of weed management systems, enabling the full potential of AI and robotics to boost global food production.

Table 3. An overview of the most significant studies conducted on crop weed detection using ML.

Crop	ML Algorithm	Dataset Type	Acc, %	Ref.
Peanuts	YOLOv4-Tiny	RGB	96.7	[101]
Sunflower	U-Net	Multispectral	90	[102]
Soybean	ML	Thermal	82	[103]
Tobacco, tomato, and sorghum	PlantNet	High Precision 3D Laser	>95	[104]
Carrot	ANN with 15 units in ensemble	Multispectral	83.5	[105]
Chilli	RF and SVM	RGB	96 and 94	[97]
Rice	SVM	RGB	73	[106]
Wheat	Wheat-V2	Spectral	>96.7	[107]
Tobacco	Faster R-CNN and YOLOv5	RGB	98.43 and 94.45	[108]
Pea and Strawberry	Faster R-CNN	RGB	95.3	[109]
Sugar Beet and Oilseed	An encoder–decoder network with atrous separable convolution	RGB	96.12	[110]
Soybean	CNN	RGB and spectral	99.66	[111]
Soybean	CNNLVQ	RGB	99.44	[112]
Bean and Spinach	RF	RGB	96.99	[113]
Carrot	ANN	Multispectral	83.5	[114]

summarizes key applications of machine learning and robotics in weed monitoring.

5.4. Nutrient Stress Detection and Chlorophyll Estimation

Accurate assessment of crop nutritional status and nutrient requirements is critical for effective farm monitoring, influencing both environmental sustainability and economic viability [115]. Nutrient excess or deficiency can result in yield losses, inefficient resource use, reduced soil organic carbon, and associated challenges [116]. Precise diagnosis enhances crop yields, optimizes fertilizer use, and increases revenue. Traditional methods like visual assessments and chemical analyses are often destructive, costly, and labor-intensive. Recent ML advancements have enabled automated diagnostic systems that effectively monitor and manage plant nutrients. For instance, Bera et al. proposed PND-Net, a hybrid graph convolutional network (GNN) and CNN for classifying plant nutrient deficiencies and diseases across multiple crops, achieving 90.54% accuracy for coffee and 96.18% for potato diseases [117]. To efficiently assess the nutritional status of oilseed rape, Abdalla et al. encoded the spatiotemporal information of plants in a single time-series model. They conducted a comparative study between SVM and deep-learning models (VGG16, VGG19, InceptionV3, ResNet50) hybridized with LSTM. The hybridization of InceptionV3 and LSTM achieved the highest diagnostic accuracy of 95% [118]. Similarly, Taha et al. achieved over 95% accuracy in assessing nitrogen, phosphorus, potassium, and chlorophyll levels in aquaponic plants using deep models [119,120]. Kou et al. used UAV-based digital cameras and CNNs to estimate nitrogen levels in cotton canopies, achieving a prediction accuracy of R² = 0.80 [121]. Yu et al. combined hyperspectral imaging and deep-learning techniques, such as CNN regression and stacked autoencoders (SAE), to estimate nitrogen levels in canola leaves with R² = 0.903 [122]. Anami et al. utilized VGG-16 to detect biotic and abiotic stresses in rice, achieving classification accuracy exceeding 95% [123]. The integration of sensors, smart irrigation systems, and remote sensing techniques has generated large datasets that, combined with ML advancements, enable precise nutrient monitoring. These breakthroughs enhance decision-making, support sustainable agricultural practices, and improve crop yields, contributing to global food security [124,125].

Table 4. An overview of the most significant research conducted in the field of nutrient stress detection and chlorophyll estimation using ML.

Crop	Dataset	ML Algorithm	Detected Nutrient	Ref.
Banana, coffee and potatoes	RGB	CNN—Graph convolutional networks (GCN	Br, Ca, Fe, Mn, Mg, N, K, P, and more deficiencies	[117]
oilseed rape	RGB	CNN-LSTM	N-P-K	[118]
Coton	RGB	CNN-based regression	Nitrogen	[121]
Lettuce	RGB	CNN	NPK	[119]
Lettuce	Spectral data and RGB	SVM, PLSR, BPNN, RF, and AutoML	Chlorophyll content	[120]
Tomato	RGB	Pre-trained deep-learning model	Ca and Mg	[126]
Wheat	RGB	BP-ANN and KNN—stepwise-based ridge regression (SBRR)	Chlorophyll content	[127]
Rice	RGB	Ensembling of various Transfer Learning (TL) architectures	Multiple deficiencies	[128]
Soybean	RGB	Deep CNN Model framework	Multiple stress, and potassium deficiency	[129]
Rice	RGB	CNN, pre-eminent classifier-SVM	Nitrogen	[130]
Black gram	RGB	Image Segmentation and CNN	Multiple deficiencies	[131]
Paddy	RGB	Deep CNN with pre-trained VGG 16	Various classes of Biotic and Abiotic stress	[123]
Rice	RGB	CNN and using Edge as a service	Biotic stress	[132]
Muskmelon	RGB	CNN, BPNN, DCNN, LSTM	Nitrogen	[133]
Guava, Groundnut	RGB	CNN, RCNN	N-P-K	[134]
Sorghum Plant	RGB	Multilayered Deep Learning	Nitrogen	[135]
Sugar beet	RGB	CNN using RGB images	N, P, K, Calcium and fertilization status	[136]
Lettuce	RGB	CNN	Nitrogen	[137]

summarizes significant research in nutrient stress detection and chlorophyll estimation using ML.

5.5. Water Status

Droughts, exacerbated by global climate change, are increasingly impacting agriculture and raising concerns about food security. Irregular rainfall limits water availability, causing water stress in crops—one of the most critical abiotic stresses affecting plant growth. Early detection of water stress is essential for sustainable agricultural productivity. Akbari et al. developed a neural network-based GenPhenML approach to predict barley resistance to drought, achieving over 97% classification accuracy [138]. Gupta et al. evaluated nine machine-learning models for automated wheat water stress detection, with Random Forest (RF) achieving the highest accuracy of over 91% [139]. Okyere et al. combined vegetation indices (VIs) from hyperspectral images with ML models, finding RF regression most effective for monitoring wheat drought stress [140]. Banerjee et al. used the Vision Transformer classifier to analyze NIR reflectance in maize, achieving 85% accuracy for early water stress detection. For rice water condition prediction, Wu et al. utilized thermal imaging-based temperature indicators with RF, achieving an R² = 0.78 [141]. Jin et al. evaluated multiple deep-learning models to predict cotton water status using 5200 thermal images under submembrane drip irrigation. MobilenetV3 outperformed other models, achieving an F1 score of 0.9990 [142]. Although machine learning has made considerable strides in early water stress detection, significant gaps remain in the research. Key challenges include the need for more adaptable models that can generalize across various crops and environmental contexts, along with the integration of diverse sensor data to refine prediction accuracy. To align with Ag5.0, the scientific community must prioritize enhancing the scalability, flexibility, and precision of these models to drive sustainable agricultural productivity amid escalating drought risks.

Table 5. An overview of the most significant research conducted in the field of water status of crops using ML.

Crop	ML Algorithm	Dataset Type	Acc.	Ref.
Barley	ANN	Phenotype and genotype features	R2 = 0.99	[138]
Wheat	RF	Fluorescence	91%	[139]
Maize	ResNet50	RGB	98%	[143]
Wheat	SVM, RF, and DNN	Hyperspectral	94%	[140]
Rice	RF	Thermal	R2 = 0.78	[141]
Maize	CNN+SVM	RGB	94%	[144]
Rice	ML models	Spectral	R2 = 0.87	[145]
Cotton	Multi CNN models	Thermal	F1 = 0.99	[142]
Tomato	VGG-16 and Resnet-50	RGB, NIR and depth images	99.18%	[146]

summarizes key studies on monitoring crop water status using ML applications.

5.6. Prediction of Crop Yield

The technological revolution, particularly deep learning, has significantly improved agricultural yield forecasting by overcoming the limitations of traditional methods. Deep learning enables predictive models that are highly accurate and adaptable to dynamic agricultural conditions, addressing the growing demand for precise yield predictions. Li et al. used UAV multispectral data to construct vegetation indices for predicting winter wheat yield across growth stages. Their CNN model outperformed other algorithms, achieving the highest prediction accuracy [147]. Similarly, Li and Wu developed a lightweight CNN model (SqueezeNet) for tiger nut yield estimation, achieving R² = 0.78 using multispectral UAV data [148]. Tanaka et al. employed CNNs with over 22,000 RGB images from six countries to estimate rice yields, explaining 68% yield variation with a root mean square error of 0.22 [149]. Mia et al. combined UAV imagery and meteorological data in a multimodal deep-learning model for rice yield forecasting, achieving 86% accuracy [150]. Zhou et al. compared CNN, ConvLSTM, and CNN-LSTM models for rice yield prediction using remote sensing and geographical heterogeneity variables, with the CNN-LSTM model demonstrating the best performance [151]. Sarr and Sultan used SVM, Random Forest, and Neural Networks to predict staple crop yields in Senegal, finding optimal performance when combining vegetation and climate data, although soil conditions were not considered [152]. Surana and Khandelwal applied a three-layer ANN with ReLU activation to predict crop yields in Maharashtra, India, achieving 82% accuracy [153]. Kuradusenge et al. used Random Forest models to predict potato and maize yields in Rwanda, with R² scores of 0.875 and 0.817, though the study omitted key meteorological variables [154]. In smart agriculture, quantum-enhanced AI technologies optimize crop yields, improve fertilizer efficiency, and support pest control through advanced robotics and automation [155]. Despite progress, challenges remain in standardizing input data, integrating findings across crop types, and evaluating the comparative efficacy of deep-learning architectures.

Table 6. An overview of the most significant research conducted in the field of crop yield prediction using ML.

Crop	ML Algorithm	Dataset Type	Acc. %	Ref.
Wheat	Traditional ML and 1D-CNN	Multispectral data	R2 = 0.703	[147]
Tiger nut	SqueezeNet	Multispectral	78	[148]
Rice	CNN	RGB	68	[149]
Rice	CNN (AlexNet)	Meteorological	86	[150]
Rice	CNN-LSTM	Meteorological	93.4	[151]
Multiple	LR, NB, and RF	Meteorological	92.81	[156]
Peanut, maize, millet and sorghum	SVM, RF, and ANN	Meteorological	R2 ≥ 0.50	[152]
Multiple	RF, Adaboost, Gradient Boost, and (SVM)	Meteorological	82	[153]
Irish potatoes and Maize	RF and SVM	Meteorological	87.5	[154]
Wheat	LSTM-RF	Multispectral	71	[157]
Soybean	PLSR, RF, SVM, DNN-F1 and DNN (DNN-F2)	RGB, multispectral and thermal	72	[158]
Wheat	OLS and LASSO, SVM, RF, AdaBoost, and DNN	Satellite images, climate data, soil maps, and historical yield records.	84	[159]
Wheat	RF, DNN, 1D-CNN and LSTM	Climate, satellite, soil properties, and spatial information data	90	[160]
Corn	1D-CNN	Hyperspectral	75.50	[161]
Wheat and Barley	CNN	RGB	MAPE = 8.8	[162]
Almond	CNN (U-Net)	RGB	78	[163]

summarizes key research in crop yield prediction using ML.

6. Innovative Technologies Associated with Ag5.0 for Precision Crop Monitoring

Crop monitoring is increasingly adopting emerging technologies such as AI and ML to automate tasks and make data-driven decisions, aligning with precision agriculture and Agriculture 4.0. Recent advancements in ML have accelerated AI applications in agriculture, enabling the development of innovative approaches suitable for Ag5.0. Key challenges in crop monitoring include monitoring nutritional deficiencies, irrigation issues, weeds, diseases, and pests. Emerging technologies focus on two objectives: (1) enabling rapid, early crop monitoring to implement proactive strategies before conditions deteriorate, and (2) developing autonomous, real-time multi-tasking systems. These goals rely on integrating diverse data sources, such as climate data, sensors, soil information, and farm monitoring systems, to improve early detection and monitoring capabilities [68,164,165]. Autonomous systems aim to perform three critical crop monitoring phases in real-time: monitoring crop conditions (e.g., nutrient stress, irrigation issues, pests), analyzing collected data, and making informed decisions. Ag5.0 technologies are expected to address these challenges by leveraging ML, hardware, drones, robotics, and telecommunications advancements.

6.1. Innovative Hardware-Based Crop Monitoring

The industrial revolution in microprocessors has driven the development of innovative electronic devices, such as smart sensors, IoT technologies, and multi-core embedded systems, transforming agriculture through digitalization. Modern smart sensors, like Sony’s IMX500 and IMX501, integrate AI processing capabilities directly onto image sensors, enabling real-time data analysis with minimal latency, reduced power consumption, and enhanced privacy. These sensors process pixel data through an image signal processor (ISP) and perform AI tasks at the logic chip stage, outputting metadata for efficient crop monitoring. They facilitate the detection and diagnosis of disturbances (e.g., nutrient deficiencies, diseases, pests) and provide data formats like ISP images and region-specific outputs for optimized crop monitoring (Sony Group Corporation, Tokyo, Japan). The pixel chip captures the signals and then processes them through the ISP (Image Signal Processor) and AI processing is performed in the processing step on the logical chip and the information is output as metadata, which reduces the size and amount of processed data. Users can specify the image output format according to the application requirements, including ISP (YUV/RGB) output images and ROI (region of interest) region extraction images, as shown in Figure 4.

Advancements in GPU, TPU, Radeon DNA (RDNA), and protocols like non-volatile memory express (NVMe) have enhanced programmability, enabling Ag5.0 applications such as virtual modeling, digital twins, and supercomputer-based crop monitoring analysis. Supercomputers improve CNN model training by accelerating data augmentation and reducing computational time, enabling precise crop monitoring and classification. field programmable gate arrays (FPGAs) further enhance CNN-based designs, supporting multi-core embedded systems and mobile-compatible Ag-IoT crop detectors [166,167]. IoT devices pose security risks, addressed through application-specific integrated circuits (ASICs) for edge gateways, essential for secure Ag-IoT systems [168,169]. ML-optimized gateways improve task efficiency under resource constraints, reducing latency and enhancing privacy in Ag5.0 edge computing systems.

In the medium term, hardware and software advancements, particularly memristor-based devices, are poised to revolutionize agriculture. Memristors integrate storage and processing capabilities, mimicking neuronal synapses and enabling neuromorphic computing with spiking neural networks (SNNs). SNNs outperform traditional artificial neural networks (ANNs) with lower latency, reduced training times, higher accuracy, and lower energy consumption, making them critical for future Ag5.0 systems [170,171,172]. Neuromorphic computing and SNNs will drive innovative computational systems with significant impacts on Ag5.0.

6.2. Crop Monitoring Through Communication Technology

Precision crop monitoring increasingly utilizes a system-of-systems approach, integrating various interconnected technologies to establish effective crop monitoring strategies. Essential technologies, including on-ground, proximal, and remote sensing, monitor factors affecting crop health, while telecommunications connect devices such as platforms, processors, and actuators to facilitate data transfer. This interconnected framework supports improved data processing, pest forecasting, and decision-making. Wireless Sensor Networks (WSNs) (Figure 5) play a central role in agricultural communication systems, employing technologies like Bluetooth and Zigbee. Bluetooth offers faster data transfer rates (1–24 Mbps) but a shorter range (<10 m), whereas Zigbee supports a longer range (up to 100 m) but slower transfer speeds (40–240 Kbps) [173]. Wi-Fi extends operational range to 50–100 m or more, while WiMAX covers distances up to 50 km. IoT and low-power wide area networks (LPWAN) have enabled technologies like LoRa and LoRaWAN, which are well-suited for rural agriculture due to their long-range (dozens of kilometers), low power consumption, and secure connectivity [174]. LoRaWAN uses the ISM frequency bands, varying regionally [175].

Efficient telecommunications are vital for real-time operations on actuator platforms such as unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and self-propelled sprayers, designed to analyze and respond to pest occurrences in milliseconds. Cloud computing, edge computing, and edge AI are key technologies enabling real-time precision crop monitoring in the context of Ag5.0. Cloud computing, supported by companies like Amazon and Microsoft, processes sensor data through machine learning and sends prescriptions back to devices. These operations require low-latency networks like 5G, with advancements toward 6G supporting UAV-based precision applications [176]. Undoubtedly, Ag5.0 will significantly benefit from the development of 6G wireless networks as they are expected to bring several features such as increased network speed and improved data throughput while enhancing time efficiency. Moreover, 6G networks are also expected to serve as the backbone for the development of connectivity in wireless network architecture and IoT sensors. The adoption of 6G networks will significantly facilitate the deployment of smart agricultural applications. These applications include real-time crop monitoring, disease and nutrient deficiency diagnosis in plants, as well as reliable and decentralized agricultural operations [177]. The integration of IoT sensors with blockchain-based systems enables new types of decentralized applications. IoT sensors collect agricultural information about plants such as monitoring plant diseases, insects, nutritional disorders, and water status, and then store and analyze it with blockchain-enabled devices and its trustless, decentralized environment, enabling new types of business processes across more transparent organizations. Figure 6 illustrates the integration of blockchain and IoT in agricultural operations.

Security and privacy remain critical challenges in cloud computing, prompting interest in decentralized fog computing to reduce latency and enhance bandwidth efficiency [178,179]. Edge computing further addresses these challenges by enabling localized data processing near the source, reducing communication costs, and improving privacy [180]. The next step in this evolution is edge AI, which integrates machine learning algorithms into embedded systems for autonomous crop monitoring. While still in early research [181], edge AI and AI chips capable of executing CNNs show significant promise for advancing agricultural robotics and automation [182]. This multi-layered technological framework ensures robust, efficient, and secure solutions for modern agriculture, emphasizing real-time precision and autonomous operations.

6.3. Advancements in Robotics Towards Ag5.0

Autonomous mobile robots (AMRs) hold great potential to significantly boost industry productivity, particularly within the context of Ag5.0, which focuses on improving precision crop monitoring. These agricultural robots are purpose-built for farming tasks [183]. As integral members of the robotics family, they are designed with advanced perceptual capabilities, autonomous decision-making, control systems, and precise execution abilities, enabling them to operate effectively in complex and hazardous environments. The growing demand for labor efficiency and enhanced agricultural production has led to an expansion in the types and applications of agricultural robots.

Agricultural robots can generally be categorized into three main types: field robots, fruit and vegetable robots, and livestock robots [183]. Research primarily focuses on field robots and those used for harvesting fruits and vegetables. Despite the differences in their specific applications, these robots share common technological features, including stable mobile platforms, multi-sensor integration, advanced image processing, sophisticated algorithms, and flexible locomotion control. Figure 7 shows the core technologies included in agricultural robotic applications. A summary of various agricultural robots, their applications, and key tasks is provided in

Table 7. An overview of some agricultural robots developed to perform specific tasks.

Application	Accuracy	Ref.
Tilling	Performed very well	[184]
Tractor	Performed very well	[185]
Wheat Precision Seeding	The qualified rates of seeding exceed 93%	[186]
Spraying fertilizers and pesticides	Performed very well	[187]
Spraying fertilizers and pesticides	The system can detect lines in plantations and can be used to retrofit conventional boom sprayers.	[188]
Planting	Performed well	[189]
Plant protection	Path planning accuracy is up to 97.8%	[190]
Strawberry sensing and harvesting	Trials showed an overall success rate of 78% in dealing with harvestable strawberries with a 23% damage rate	[191]
Inspect the presence of pests and diseases	A detection rate of 66.4% was obtained for images obtained in the laboratory and 59.8% for images obtained in the field.	[192]
Harvesting paddy	Harvesting process improved	[193]
Detecting and classifying weed	The results showed that the development deployed automatically on AgBot II was effective in controlling all weeds.	[194]
Tomato treatment and harvesting	Performed well	[195]
Tomato harvesting	Performed well	[196]

. This evolution in agricultural robotics promises greater automation and efficiency in farming practices, contributing to more precise and sustainable crop monitoring.

The future of Ag5.0 will increasingly rely on collaborative robots that improve agricultural practices by enhancing ergonomics and sharing workspace [197]. These robots will play a key role in organic food production, particularly in non-chemical pest control using mechanical methods [198], as well as in efficient harvesting systems [199], which will improve organic crop monitoring effectiveness [200].

As Ag5.0 technologies advance, it will facilitate the integration of UGVs and UAVs into coordinated operations under a single control system, leading to the development of multi-robot fleet systems (MFS). These fleets, consisting of smaller robots, can perform tasks similar to those of larger machines while offering greater precision in positioning [201]. Technological progress has also enabled UAVs to develop sensory-motor, reactive, and cognitive autonomy [202], making them highly effective for crop monitoring when equipped with RGB, multispectral, and hyperspectral sensors.

In Ag5.0, ML algorithms will be integrated into UAVs’ systems, enabling decentralized, real-time monitoring of autonomous vehicle fleets (both UAVs and UGVs). This will support advanced navigation systems, such as the redundant navigation system developed by Belhajem et al., which combines artificial neural networks, genetic algorithms, and the Extended Kalman Filter to estimate vehicle positions in real time, even without GPS [203]. Autonomous vehicle fleets will enable real-time crop monitoring, allowing for quick detection of issues affecting crop health [204]. Ultimately, this technology will reduce production costs, improve economic returns, enhance sustainability, and minimize the environmental impact of traditional farming practices.

7. Opportunities and Challenges Towards Ag5.0

The transition to Ag5.0 is expected to improve human well-being and security by creating numerous community opportunities, particularly in addressing unemployment. A significant challenge in agriculture today is the decline in productivity, which has contributed to many countries’ food security issues and social crises. This decline is partly due to the negative perception of the sector among younger generations [205] and the aging agricultural workforce [206]. Ag5.0 technologies, such as AI and collaborative robots, will generate new, innovative job opportunities that improve job satisfaction and shift the perception of agriculture from a low-status, unskilled field to one requiring technical expertise [207]. This requires a highly skilled workforce to enhance productivity and compete in the global market.

Ag5.0 can potentially reduce unemployment, alleviate poverty, decrease social tensions, and potentially reduce terrorism. It will also allow consumers to choose products tailored to their preferences, increasing customer satisfaction, offering more specialized services, and reducing waste [34]. Integrating human workers with intelligent machines will improve both the quality and quantity of agricultural production, optimizing resource use and maximizing profits. Additionally, Ag5.0 will enhance data monitoring and control through advanced data collection, analysis, decision-making, and monitoring technologies while promoting safer, organic, and sustainable production practices [33].

However, the transition to Ag5.0 faces several challenges, particularly cybersecurity. Emerging technologies may introduce public safety and privacy risks, including data security, access control, communication networks, and human–machine interactions [22]. One of the main challenges in adopting Ag5.0 is the need for more expertise and infrastructure. Ag5.0 technologies such as blockchain, big data analytics, and quantum computing are relatively new technological developments and need to be better understood by people in all sectors of the global economy, including agriculture [155]. As a result, technology adoption in the specific context of smart agriculture may be higher because most industry stakeholders need the necessary competencies to use them. The scope of most Ag5.0 technologies, such as blockchain and quantum computing in smart agriculture, is far-reaching, meaning that it is difficult for most people to learn and use the technology in all its various applications [155]. Although Ag5.0 holds great promise, it remains an emerging field with limited availability of hardware and software worldwide, making it difficult to test and optimize quantum algorithms in a variety of areas, including smart agriculture [208]. The lack of standardization makes it difficult for farmers and agricultural organizations to effectively use Ag5.0 technologies, such as quantum computing, in their operations, as they may need to create solutions tailored to their specific needs. In this regard, the perceived scarcity of hardware and software may result from the fact that it is difficult for different stakeholders in agriculture to find the specific quantum computing options they need, as, in most cases, the technology must be customized for each user. Thus, unifying hardware and software for the agricultural sector can overcome the scarcity of technology resources. Cybersecurity risks are a major challenge facing the adoption of Agriculture 5.0, leading to negative impacts on society such as security risks and privacy issues [22]. The issues involved include agricultural data security, access control issues, network and communication security, security related to human–machine interactions, and safe machine operations [22]. Ag5.0 technologies, such as quantum computing, can break traditional encryption methods, making sensitive data vulnerable to attack. The reliance on Ag5.0 on state-of-the-art technology has made it very easy to hack most software-based security systems, increasing the risk of unauthorized individuals, such as hackers, accessing essential data about organizations in various contexts, including smart agriculture. In this regard, stakeholders in the agricultural sector may be reluctant to adopt the technology for fear of being exposed to security breaches by malicious parties [155,209]. Another challenge in adopting Ag5.0 is requiring specialized quantum sensors and devices. Quantum sensors are essential for the growth of quantum computing technology in smart agriculture [209]. These sensors are adept at measuring physical properties, making soil and water quality monitoring possible. Despite the many benefits, the development and implementation of sensors suitable for Ag5.0 adoption is still in its early stages, and many unsolved technical challenges need to be addressed.

8. Future Trends and Research Needs

Agriculture 5.0 is the next revolution in the agricultural sector thanks to the integration of advanced technologies such as IoT, AI, quantum computing, robotics, and smart grids, which opens new horizons for improving sustainability, efficiency, and productivity, in addition to addressing global challenges such as labor shortages and food insecurity.

The success of Ag5.0 depends on integrating technology with agricultural science, which contributes to modernizing agricultural operations and creating high-skilled jobs. Using smart machines, such as collaborative robots, will enhance productivity and empower farmers [206]. In addition, the collaboration between human expertise and smart technologies enhances data management and allows farmers to control their data and generate additional income through knowledge exchange [35]. Regional adaptation is crucial to the successful implementation of Ag5.0, as technologies such as AI and IoT can solve unemployment problems and contribute to improving food production in areas with labor shortages [207]. Designing agricultural solutions tailored to local needs also boosts the local economy and increases customer satisfaction [205]. On the other hand, advances in AI and robotics, such as neuromorphic computing and evolving neural networks, offer great potential to improve the efficiency of agricultural robots in performing precise tasks such as harvesting and soil monitoring [206]. Combining these technologies with quantum computing will enable advanced agricultural data analysis, enhancing farm management and contributing to waste reduction and more sustainable agricultural practices [33].

9. Conclusions

This article presents a framework for the future of precision crop Management, emphasizing the scientific, agronomic, and industrial applications of traditional ML algorithms, alongside recent advancements in artificial neural network (ANN) models. Between 2015 and 2024, various algorithms have been identified and applied across multiple disciplines to address growth assessment, water status, nutritional stress, crop diseases, weeds, pests, and prediction of yields. These algorithms aim to tackle tasks involving classification, regression, clustering, anomaly detection, dimensionality reduction, and association rule learning, thereby advancing precision crop detection in alignment with the emerging concept of Ag5.0. The transition to Ag5.0 will require innovations and tailored hardware, telecommunications, and robotics solutions—some already being implemented in agriculture, while others remain in development. This article highlights these emerging technologies. The shift from current Agriculture 4.0 strategies to future Ag5.0 approaches in precision crop detection will depend largely on the focus and level of automation involved. Ag5.0 will usher in a new era of intelligent crop monitoring, with an emphasis on resolving complex detection challenges, such as the early identification of crop pests, while improving overall monitoring practices, including autonomous real-time multitasking. A central focus will be on automated decision-making processes, unmanned operations, and a progressively reduced need for human intervention supported by cutting-edge AI systems, advanced robotics, and robust ML algorithms.