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3 pages, 156 KiB

Open AccessEditorial

Application of Smart Technology and Equipment in Horticulture

by Chenglin Wang and Lufeng Luo

Horticulturae 2024, 10(7), 676; https://doi.org/10.3390/horticulturae10070676 - 26 Jun 2024

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Abstract

Horticulture, as an important component of modern agriculture, plays a significant role in beautifying the environment but also in enriching human nutrition [...] Full article

(This article belongs to the Special Issue Application of Smart Technology and Equipment in Horticulture)

Research

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14 pages, 4056 KiB

Open AccessEditor’s ChoiceArticle

A New Plant-Wearable Sap Flow Sensor Reveals the Dynamic Water Distribution during Watermelon Fruit Development

by Runqing Zhang, Yangfan Chai, Xinyu Liang, Xiangjiang Liu, Xiaozhi Wang and Zhongyuan Hu

Horticulturae 2024, 10(6), 649; https://doi.org/10.3390/horticulturae10060649 - 17 Jun 2024

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Abstract

This study utilized a plant-wearable sap flow sensor developed by a multidisciplinary team at Zhejiang University to monitor water distribution patterns in watermelon fruit stalks throughout their developmental stages. The dynamic rules of sap flow at different stages of fruit development were discovered: [...] Read more.

This study utilized a plant-wearable sap flow sensor developed by a multidisciplinary team at Zhejiang University to monitor water distribution patterns in watermelon fruit stalks throughout their developmental stages. The dynamic rules of sap flow at different stages of fruit development were discovered: (1) In the first stage, sap flow into the fruit gradually halts after sunrise due to increased leaf transpiration, followed by a rapid increase post-noon until the next morning, correlating with fruit expansion. (2) In the second stage, the time of inflow sap from noon to night is significantly shortened, while the outflow sap from fruit is observed with the enhancement of leaf transpiration after sunrise, which is consistent with the slow fruit growth at this stage. (3) In the third stage, the sap flow maintains the diurnal pattern. However, the sap flow that inputs the fruit at night is basically equal to the sap flow that outputs the fruit during the day; the fruit phenotype does not change anymore. In addition, a strong correlation between the daily mass growth in fruit and the daily sap flow amount in fruit stalk was identified, validating the sensor’s utility for fruit growth monitoring and yield prediction. Full article

(This article belongs to the Special Issue Application of Smart Technology and Equipment in Horticulture)

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19 pages, 6062 KiB

Open AccessArticle

Non-Destructive Detection of Cerasus Humilis Fruit Quality by Hyperspectral Imaging Combined with Chemometric Method

by Bin Wang, Hua Yang, Lili Li and Shujuan Zhang

Horticulturae 2024, 10(5), 519; https://doi.org/10.3390/horticulturae10050519 - 17 May 2024

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Abstract

Cerasus Humilis fruit is susceptible to rapid color changes post-harvest, which degrades its quality. This research utilized hyperspectral imaging technology to detect and visually analyze the soluble solid content (SSC) and firmness of the fruit, aiming to improve quality and achieve optimal pricing. [...] Read more.

Cerasus Humilis fruit is susceptible to rapid color changes post-harvest, which degrades its quality. This research utilized hyperspectral imaging technology to detect and visually analyze the soluble solid content (SSC) and firmness of the fruit, aiming to improve quality and achieve optimal pricing. Four maturity stages (color turning stage, coloring stage, maturity stage, and fully ripe stage) of Cerasus Humilis fruit were examined using hyperspectral images (895–1700 nm) alongside data collection on SSC and firmness. These samples were divided into a calibration set and a validation set with a ratio of 3:1 by sample set partitioning based on the joint X-Y distances (SPXY) method. The original spectral data was processed by a spectral preprocessing method. Multiple linear regression (MLR) and nonlinear least squares support vector machine (LS-SVM) detection models were established using feature wavelengths selected by the successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and two combined downscaling algorithms (UVE-SPA and UVE-CARS), respectively. For SSC and firmness detection, the best models were the SNV-SPA-LS-SVM model with 18 feature wavelengths and the original spectra-UVE-CARS-LS-SVM model with eight feature wavelengths, respectively. For SSC, the correlation coefficient of prediction (Rp) was 0.8526, the root mean square error of prediction (RMSEP) was 0.9703, and the residual prediction deviation (RPD) was 1.9017. For firmness, Rp was 0.7879, RMSEP was 1.1205, and RPD was 2.0221. Furthermore, the optimal model was employed to retrieve the distribution of SSC and firmness within Cerasus Humilis fruit. This retrieved information facilitated visual inspection, enabling a more intuitive and comprehensive assessment of SSC and firmness at each pixel level. These findings demonstrated the effectiveness of hyperspectral imaging technology for determining SSC and firmness in Cerasus Humilis fruit. This paves the way for online monitoring of fruit quality, ultimately facilitating timely harvesting. Full article

(This article belongs to the Special Issue Application of Smart Technology and Equipment in Horticulture)

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26 pages, 3993 KiB

Open AccessArticle

An Original UV Adhesive Watermelon Grafting Method, the Grafting Device, and Experimental Verification

by Xin Zhang, Linghao Kong, Hanwei Lu, Qingchun Feng, Tao Li, Qian Zhang and Kai Jiang

Horticulturae 2024, 10(4), 365; https://doi.org/10.3390/horticulturae10040365 - 5 Apr 2024

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Abstract

This study is aimed at traditional vegetable grafting using a large number of plastic clips, which cannot be recycled in time and cause serious pollution within the planting environment. This paper proposes a new grafting method based on a UV adhesive instead of [...] Read more.

This study is aimed at traditional vegetable grafting using a large number of plastic clips, which cannot be recycled in time and cause serious pollution within the planting environment. This paper proposes a new grafting method based on a UV adhesive instead of plastic clips. First of all, a UV adhesive spray grafting device was designed. The structure includes seedling adsorption positioning mechanisms, a butt joint mechanism, a handling mechanism, a spray valve, a UV curing lamp, etc., to facilitate the adhesive spraying. For the rootstock and scion, a horizontal, lateral seedling and negative pressure adsorption and positioning method is adopted, with fluid dynamics simulation of the diameter and quantity of the adsorption holes in the rootstock adsorption mechanism carried out using Fluent 2022 R1 software and completion of the optimization of the parameters of the adsorption and positioning mechanism. The fluid volume method is used to simulate the adsorption and positioning mechanism. For optimization, the volume of fluid method (VOF) and the discrete particle method (DPM) are used in a coupled simulation of the UV adhesive spraying process, and the value range of the spraying influencing factors is determined: the selected glue pressure, atomization pressure, and spraying height for three-factor, three-level orthogonal simulation. A grafting test is also verification, deriving the significance ranking of their impact on the success rate of the grafting: atomization pressure > spraying height > glue pressure. Under the condition of a 0.25 Mpa atomization pressure, a 0.15 Mpa glue supply pressure, and a 10 mm spraying height, the grafting success rate for watermelon was 100%, the effective spraying rate was 83.03%, the healing success rate was 94.5%, and the length of the film was 7.86 mm. The results of the study can provide a research basis for the research and development of new types of spraying and grafting robot technology. Full article

(This article belongs to the Special Issue Application of Smart Technology and Equipment in Horticulture)

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24 pages, 14107 KiB

Open AccessArticle

Human-Following Strategy for Orchard Mobile Robot Based on the KCF-YOLO Algorithm

by Zhihao Huang, Chuhong Ou, Zhipeng Guo, Lei Ye and Jin Li

Horticulturae 2024, 10(4), 348; https://doi.org/10.3390/horticulturae10040348 - 31 Mar 2024

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Abstract

Autonomous mobile robots play a vital role in the mechanized production of orchards, where human-following is a crucial collaborative function. In unstructured orchard environments, obstacles often obscure the path, and personnel may overlap, leading to significant disruptions to human-following. This paper introduces the [...] Read more.

Autonomous mobile robots play a vital role in the mechanized production of orchards, where human-following is a crucial collaborative function. In unstructured orchard environments, obstacles often obscure the path, and personnel may overlap, leading to significant disruptions to human-following. This paper introduces the KCF-YOLO fusion visual tracking method to ensure stable tracking in interference environments. The YOLO algorithm provides the main framework, and the KCF algorithm intervenes in assistant tracking. A three-dimensional binocular-vision reconstruction method was used to acquire personnel positions, achieving stabilized visual tracking in disturbed environments. The robot was guided by fitting the personnel’s trajectory using an unscented Kalman filter algorithm. The experimental results show that, with 30 trials in multi-person scenarios, the average tracking success rate is 96.66%, with an average frame rate of 8 FPS. Additionally, the mobile robot is capable of maintaining a stable following speed with the target individuals. Across three human-following experiments, the horizontal offset Error Y does not exceed 1.03 m. The proposed KCF-YOLO tracking method significantly bolsters the stability and robustness of the mobile robot for human-following in intricate orchard scenarios, offering an effective solution for tracking tasks. Full article

(This article belongs to the Special Issue Application of Smart Technology and Equipment in Horticulture)

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16 pages, 5875 KiB

Open AccessArticle

Simulation Model Construction of Plant Height and Leaf Area Index Based on the Overground Weight of Greenhouse Tomato: Device Development and Application

by Shenbo Guo, Letian Wu, Xinwei Cao, Xiaoli Sun, Yanfei Cao, Yuhan Li and Huifeng Shi

Horticulturae 2024, 10(3), 270; https://doi.org/10.3390/horticulturae10030270 - 11 Mar 2024

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Abstract

Plant height and leaf area index (LAI) are crucial growth indicators that reflect the growth status of tomatoes in greenhouses, enabling accurate determinations to effectively estimate crop transpiration and formulate irrigation strategies for reducing agricultural water waste. There is a need for the [...] Read more.

Plant height and leaf area index (LAI) are crucial growth indicators that reflect the growth status of tomatoes in greenhouses, enabling accurate determinations to effectively estimate crop transpiration and formulate irrigation strategies for reducing agricultural water waste. There is a need for the increased application of related models to simulate tomato growth indices in the traditional greenhouse production in China. This study proposes a nondestructive, real-time monitoring and simulation device for measuring tomato plant height and leaf area index. The weight of aboveground tomatoes was obtained by suspending tomato plants on dynamometers, while the total weight of stem and leaf organs was determined using a distribution coefficient simulation model. The R² value between the measurements from the electronic scale and those from the aboveground fresh weight device for tomatoes was 0.937, with an RMSE value of 0.05 kg. The monitoring device did not affect the average tomato growth during operation. The device will not affect the growth of tomatoes during monitoring. A multiple linear regression was used to compare the measured and simulated values of the plant height and leaf area index of various types of greenhouse tomatoes cultivated in different greenhouse types. The average R² value for simulating plant height was 0.817 with an RMSE of 10.81 cm. The average R² value for the leaf area index was 0.854, with an RMSE of 0.55 m²·m⁻². The simulated values for plant height and leaf area index closely matched the measured values, indicating that the model has high accuracy and applicability in traditional Chinese greenhouses (solar greenhouses and insulated plastic greenhouses). However, further optimization is required for commercially produced, continuous plastic greenhouses equipped with greenhouse environmental control equipment. Full article

(This article belongs to the Special Issue Application of Smart Technology and Equipment in Horticulture)

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15 pages, 7221 KiB

Open AccessArticle

IMVTS: A Detection Model for Multi-Varieties of Famous Tea Sprouts Based on Deep Learning

by Runmao Zhao, Cong Liao, Taojie Yu, Jianneng Chen, Yatao Li, Guichao Lin, Xiaolong Huan and Zhiming Wang

Horticulturae 2023, 9(7), 819; https://doi.org/10.3390/horticulturae9070819 - 17 Jul 2023

Cited by 3 | Viewed by 1490

Abstract

The recognition of fresh tea leaf sprouts is one of the difficulties in the realization of the automated picking of fresh tea leaves. At present, the research on the detection of fresh tea leaf sprouts is based on a single variety of tea [...] Read more.

The recognition of fresh tea leaf sprouts is one of the difficulties in the realization of the automated picking of fresh tea leaves. At present, the research on the detection of fresh tea leaf sprouts is based on a single variety of tea leaves for a specific period or specific place, which has no advantage for the spread, promotion, and application of the methods. To address this problem, an identification of multiple varieties of tea sprouts (IMVTS) model was proposed. First, images of three different varieties of tea (ZhongCha108 (ZC108), ZhongHuangYiHao (ZH), ZiJuan (ZJ)) were obtained, and the multiple varieties of tea (MVT) dataset for training and validating models was created. In addition, the detection effects of adding a convolutional block attention module (CBAM) or efficient channel attention (ECA) module to YOLO v7 were compared. In the detection of the MVT dataset, YOLO v7+ECA and YOLO v7+CBAM showed a higher mean average precision (mAP) than YOLO v7, with 98.82% and 98.80%, respectively. Notably, the IMVTS model had the highest AP for ZC108, ZH, and ZJ compared with the two other models, with 99.87%, 96.97%, and 99.64%, respectively. Therefore, the IMVTS model was proposed on the basic framework of the ECA and YOLO v7. To further illustrate the superiority of the model, this study also conducted a comparison test between the IMVTS model and the mainstream target detection models (YOLO v3, YOLO v5, FASTER-RCNN, and SSD) and the IMVTS model on the VOC dataset, and it is clear from the test results that the mAP of the IMVTS model is ahead of the remaining models. Concisely, the detection accuracy of the IMVTS model can meet the engineering requirements for the automatic harvesting of autumn fresh famous tea leaves, which provides a basis for the future design of detection networks for other varieties of autumn tea sprouts. Full article

(This article belongs to the Special Issue Application of Smart Technology and Equipment in Horticulture)

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15 pages, 3504 KiB

Open AccessArticle

A Comparative Analysis of the Grafting Efficiency of Watermelon with a Grafting Machine

by Huan Liang, Juhong Zhu, Mihong Ge, Dehuan Wang, Ke Liu, Mobing Zhou, Yuhong Sun, Qian Zhang, Kai Jiang and Xianfeng Shi

Horticulturae 2023, 9(5), 600; https://doi.org/10.3390/horticulturae9050600 - 19 May 2023

Cited by 3 | Viewed by 2256

Abstract

The rising age of the population in rural China and the labor intensity of grafting have resulted in a decrease in the number of grafters and a subsequent increase in their wages. Manual grafting can no longer satisfy the increasing demand for watermelon-grafted [...] Read more.

The rising age of the population in rural China and the labor intensity of grafting have resulted in a decrease in the number of grafters and a subsequent increase in their wages. Manual grafting can no longer satisfy the increasing demand for watermelon-grafted transplanting; thus, machine grafting will be an effective alternative. In order to accelerate the implementation of machine grafting in China, a comparative analysis between the automatic grafting machine (model 2TJGQ-800) and traditional hand grafting was conducted. The reliability and feasibility of machine grafting were evaluated through a comprehensive evaluation of the production capacity and grafting seedling quality. This study focuses on the grafting application of watermelon plug-tray seedlings. The scion and rootstock seeds were sown on 9 November 2022. Grafting experiments using an automatic grafting machine, skilled workers, and ordinary workers were conducted with the root-pruned one-cotyledon grafting method on 24 November 2022. The results showed that the machine grafting had a high uniformity and grafting speed. The grafting speed of the grafting machine was 774 plant·h⁻¹ and 1.65–2.55-fold higher than the hand grafting. With training, workers can improve their grafting speed, but it will still be slower than machine grafting. In addition, there was no significant difference in the grafting survival rate between the machine grafting and hand grafting. However, using machine grafting, the success rate decreased from 100% to 90.07% and the rootstock regrowth rate increased from 18.44% to 72.69%. Incomplete rootstock cutting, clip supply failure, and grafting drop failure are the three main factors that result in machine grafting failure. In conclusion, the grafting machine has advantages in terms of grafting speed and uniformity. Upon improving the accuracy of the cutting mechanism and grafting success rate, it will be adopted by commercial nurseries. Full article

(This article belongs to the Special Issue Application of Smart Technology and Equipment in Horticulture)

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22 pages, 9698 KiB

Open AccessArticle

GA-YOLO: A Lightweight YOLO Model for Dense and Occluded Grape Target Detection

by Jiqing Chen, Aoqiang Ma, Lixiang Huang, Yousheng Su, Wenqu Li, Hongdu Zhang and Zhikui Wang

Horticulturae 2023, 9(4), 443; https://doi.org/10.3390/horticulturae9040443 - 28 Mar 2023

Cited by 10 | Viewed by 2573

Abstract

Picking robots have become an important development direction of smart agriculture, and the position detection of fruit is the key to realizing robot picking. However, the existing detection models have the shortcomings of missing detection and slow detection speed when detecting dense and [...] Read more.

Picking robots have become an important development direction of smart agriculture, and the position detection of fruit is the key to realizing robot picking. However, the existing detection models have the shortcomings of missing detection and slow detection speed when detecting dense and occluded grape targets. Meanwhile, the parameters of the existing model are too large, which makes it difficult to deploy to the mobile terminal. In this paper, a lightweight GA-YOLO model is proposed. Firstly, a new backbone network SE-CSPGhostnet is designed, which greatly reduces the parameters of the model. Secondly, an adaptively spatial feature fusion mechanism is used to address the issues of difficult detection of dense and occluded grapes. Finally, a new loss function is constructed to improve detection efficiency. In 2022, a detection experiment was carried out on the image data collected in the Bagui rural area of Guangxi Zhuang Autonomous Region, the results demonstrate that the GA-YOLO model has an mAP of 96.87%, detection speed of 55.867 FPS and parameters of 11.003 M. In comparison to the model before improvement, the GA-YOLO model has improved mAP by 3.69% and detection speed by 20.245 FPS. Additionally, the GA-YOLO model has reduced parameters by 82.79%. GA-YOLO model not only improves the detection accuracy of dense and occluded targets but also lessens model parameters and accelerates detection speed. Full article

(This article belongs to the Special Issue Application of Smart Technology and Equipment in Horticulture)

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20 pages, 6729 KiB

Open AccessArticle

Reasearch on Kiwi Fruit Flower Recognition for Efficient Pollination Based on an Improved YOLOv5 Algorithm

by Haili Zhou, Junlang Ou, Penghao Meng, Junhua Tong, Hongbao Ye and Zhen Li

Horticulturae 2023, 9(3), 400; https://doi.org/10.3390/horticulturae9030400 - 20 Mar 2023

Cited by 10 | Viewed by 2430

Abstract

A close relationship has been observed between the growth and development of kiwi fruit and the pollination of the kiwi flower. Flower overlap, flower tilt, and other problems will affect this plant’s pollination success rate. A pollination model based on YOLOv5 was developed [...] Read more.

A close relationship has been observed between the growth and development of kiwi fruit and the pollination of the kiwi flower. Flower overlap, flower tilt, and other problems will affect this plant’s pollination success rate. A pollination model based on YOLOv5 was developed to improve the pollination of kiwi flowers. The K-means++ clustering method was used to cluster the anchors closer to the target size, which improved the speed of the algorithm. A convolutional block module attention mechanism was incorporated to improve the extraction accuracy with respect to kiwi flower features and effectively reduce the missed detection and error rates. The optimization of the detection function improves the recognition of flower overlap and the accuracy of flower tilt angle calculation and accurately determines flower coordinates, pollination point coordinates, and pollination angles. The experimental results show that the predicted value of the YOLOv5s model is 96.7% and that its recognition accuracy is the highest. Its mean average precision value is up to 89.1%, its F1 score ratio is 90.12%, and its memory requirements are the smallest (only 20 MB). The YOLOv5s model achieved the highest recognition accuracy as determined through a comparison experiment of the four sets of analysed models, thereby demonstrating its ability to facilitate the efficient target pollination of kiwi flowers. Full article

(This article belongs to the Special Issue Application of Smart Technology and Equipment in Horticulture)

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18 pages, 37574 KiB

Open AccessArticle

HeLoDL: Hedgerow Localization Based on Deep Learning

by Yanmei Meng, Xulei Zhai, Jinlai Zhang, Jin Wei, Jihong Zhu and Tingting Zhang

Horticulturae 2023, 9(2), 227; https://doi.org/10.3390/horticulturae9020227 - 8 Feb 2023

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Abstract

Accurate localization of hedges in 3D space is a key step in automatic pruning. However, due to the irregularity of the hedge shape, the localization accuracy based on traditional algorithms is poor. In this paper, we propose a deep learning approach based on [...] Read more.

Accurate localization of hedges in 3D space is a key step in automatic pruning. However, due to the irregularity of the hedge shape, the localization accuracy based on traditional algorithms is poor. In this paper, we propose a deep learning approach based on a bird’s-eye view to overcoming this problem, which we call

H e L o D L

. Specifically, we first project the hedge point cloud top-down as a single image and, then, augment the image with morphological operations and rotation. Finally, we trained a convolutional neural network,

H e L o D L

, based on transfer learning, to regress the center axis and radius of the hedge. In addition, we propose an evaluation metric OIoU that can respond to the radius error, as well as the circle center error in an integrated way. In our test set,

H e L o D L

achieved an accuracy of 90.44% within the error tolerance, which greatly exceeds the 61.74% of the state-of-the-art algorithm. The average OIoU of

H e L o D L

is 92.65%; however, the average OIoU of the best conventional algorithm is 83.69%. Extensive experiments demonstrated that

H e L o D L

shows considerable accuracy in the 3D spatial localization of irregular models. Full article

(This article belongs to the Special Issue Application of Smart Technology and Equipment in Horticulture)

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20 pages, 18331 KiB

Open AccessArticle

Research on Real-Time Automatic Picking of Ground-Penetrating Radar Image Features by Using Machine Learning

by Zhi Qiu, Junyuan Zeng, Wenhui Tang, Houcheng Yang, Junjun Lu and Zuoxi Zhao

Horticulturae 2022, 8(12), 1116; https://doi.org/10.3390/horticulturae8121116 - 28 Nov 2022

Cited by 5 | Viewed by 2173

Abstract

Hard foreign objects such as bricks, wood, metal materials, and plastics in orchard soil can affect the operational safety of garden machinery. Ground-Penetrating Radar (GPR) is widely used for the detection of hard foreign objects in soil due to its advantages of non-destructive [...] Read more.

Hard foreign objects such as bricks, wood, metal materials, and plastics in orchard soil can affect the operational safety of garden machinery. Ground-Penetrating Radar (GPR) is widely used for the detection of hard foreign objects in soil due to its advantages of non-destructive detection (NDT), easy portability, and high efficiency. At present, the degree of automatic identification applied in soil-oriented foreign object detection based on GPR falls short of the industry’s expectations. To further enhance the accuracy and efficiency of soil-oriented foreign object detection, we combined GPR and intelligent technology to conduct research on three aspects: acquiring real-time GPR images, using the YOLOv5 algorithm for real-time target detection and the coordinate positioning of GPR images, and the construction of a detection system based on ground-penetrating radar and the YOLOv5 algorithm that automatically detects target characteristic curves in ground-penetrating radar images. In addition, taking five groups of test results of detecting different diameters of rebar inside the soil as an example, the obtained average error of detecting the depth of rebar using the detection system is within 0.02 m, and the error of detecting rebar along the measuring line direction from the location of the starting point of GPR detection is within 0.08 m. The experimental results show that the detection system is important for identifying and positioning foreign objects inside the soil. Full article

(This article belongs to the Special Issue Application of Smart Technology and Equipment in Horticulture)

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Review

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26 pages, 3223 KiB

Open AccessFeature PaperReview

Artificial Intelligence: A Promising Tool for Application in Phytopathology

by Victoria E. González-Rodríguez, Inmaculada Izquierdo-Bueno, Jesús M. Cantoral, María Carbú and Carlos Garrido

Horticulturae 2024, 10(3), 197; https://doi.org/10.3390/horticulturae10030197 - 20 Feb 2024

Cited by 5 | Viewed by 5912

Abstract

Artificial intelligence (AI) is revolutionizing approaches in plant disease management and phytopathological research. This review analyzes current applications and future directions of AI in addressing evolving agricultural challenges. Plant diseases annually cause 10–16% yield losses in major crops, prompting urgent innovations. Artificial intelligence [...] Read more.

Artificial intelligence (AI) is revolutionizing approaches in plant disease management and phytopathological research. This review analyzes current applications and future directions of AI in addressing evolving agricultural challenges. Plant diseases annually cause 10–16% yield losses in major crops, prompting urgent innovations. Artificial intelligence (AI) shows an aptitude for automated disease detection and diagnosis utilizing image recognition techniques, with reported accuracies exceeding 95% and surpassing human visual assessment. Forecasting models integrating weather, soil, and crop data enable preemptive interventions by predicting spatial-temporal outbreak risks weeks in advance at 81–95% precision, minimizing pesticide usage. Precision agriculture powered by AI optimizes data-driven, tailored crop protection strategies boosting resilience. Real-time monitoring leveraging AI discerns pre-symptomatic anomalies from plant and environmental data for early alerts. These applications highlight AI’s proficiency in illuminating opaque disease patterns within increasingly complex agricultural data. Machine learning techniques overcome human cognitive constraints by discovering multivariate correlations unnoticed before. AI is poised to transform in-field decision-making around disease prevention and precision management. Overall, AI constitutes a strategic innovation pathway to strengthen ecological plant health management amidst climate change, globalization, and agricultural intensification pressures. With prudent and ethical implementation, AI-enabled tools promise to enable next-generation phytopathology, enhancing crop resilience worldwide. Full article

(This article belongs to the Special Issue Application of Smart Technology and Equipment in Horticulture)

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