Search Results (1,785)

Controlled environment agriculture (CEA) pivots food production from an outdoor field setting to the indoors where growing conditions can be calibrated to fit crop needs. This research investigates vertical farms as a type of CEA. In particular, using the St. Louis area as a case study, it provides data-driven support for optimizing a vertical farm’s business model including its supply chain. The methodology presented here informs agri-preneurs about what crops to grow in a vertical farm, how much to grow given local market demand, and what vertical farm configuration (e.g., Dutch bucket, nutrient film technique, deep water culture) a facility should use. Based on the case study’s base scenario, the simulated vertical farm business would record an economic loss. However, the study did find several paths to improving profitability. First, reducing fixed and variable costs benefits profitability. Proper facility-level production and resource planning helps with managing the fixed costs. Second, increasing market prices may benefit profitability, but it has diminishing returns. As a result, firms can justify making investments that enhance their reputation and market competitiveness, though the advantage these marketing activities provide will decline as prices increase. Third, growing demand or increasing market share does not necessarily improve profitability. Full article

In resource-constrained edge agricultural environments, the accurate recognition of toxic weeds poses dual challenges related to model lightweight design and the few-shot generalization capability. To address these challenges, a multi-strategy recognition framework is proposed, which integrates a lightweight backbone network, a pseudo-labeling guidance mechanism, and a contrastive boundary enhancement module. This approach is designed to improve deployment efficiency on low-power devices while ensuring high accuracy in identifying rare toxic weed categories. The proposed model achieves a real-time inference speed of 18.9 FPS on the Jetson Nano platform, with a compact model size of 18.6 MB and power consumption maintained below 5.1 W, demonstrating its efficiency for edge deployment. In standard classification tasks, the model attains 89.64%, 87.91%, 88.76%, and 88.43% in terms of precision, recall, F1-score, and accuracy, respectively, outperforming existing mainstream lightweight models such as ResNet18, MobileNetV2, and MobileViT across all evaluation metrics. In few-shot classification tasks targeting rare toxic weed species, the complete model achieves an accuracy of 80.32%, marking an average improvement of over 13 percentage points compared to ablation variants that exclude pseudo-labeling and self-supervised modules or adopt a CNN-only architecture. The experimental results indicate that the proposed model not only delivers strong overall classification performance but also exhibits superior adaptability for deployment and robustness in low-data regimes, offering an effective solution for the precise identification and ecological control of toxic weeds within intelligent agricultural perception systems. Full article

Over the past two decades, hyperspectral imaging (HSI) systems have shown significant potential in agriculture, from disease detection to the assessment of plant and fruit nutritional status. However, most applications remain confined to laboratory analyses under controlled conditions, with only a limited fraction implemented in field environments. In this scenario, spectral reconstruction techniques may serve as a bridge between the high accuracy of HSI and the challenges of on-field or even real-time applications. This review outlines the current state of the art of on-field HSI in the agrifood sector, highlighting existing limitations and potential advantages. It then introduces the problem of spectral reconstruction and reviews current techniques used to address it. Laboratory and on-field studies will be taken into account. The final section offers our perspective on the limitations of HSI and the promising potential of spectral super-resolution to overcome current barriers and enable broader adoption of hyperspectral technology in precision agriculture. Full article

Differential tracked robots play a crucial role in various modernized work scenarios such as smart industry, agriculture, and transportation. However, these robots frequently encounter substantial challenges in trajectory tracking, attributable to substantial initial errors and dynamic environments, which result in slow convergence rates, cumulative errors, and diminished tracking precision. To address these challenges, this paper proposes a fuzzy adaptive virtual steering coefficient model predictive control (FVSMPC) algorithm. The FVSMPC algorithm introduces a virtual steering coefficient into the robot’s kinematic model, which is adaptively adjusted using fuzzy logic based on real-time positional error and velocity. This approach not only enhances the robot’s ability to quickly correct large errors but also maintains stability during tracking.The nonlinear kinematic model undergoes linearization via a Taylor expansion and is subsequently formulated as a quadratic programming problem to facilitate efficient iterative solutions. To validate the proposed control algorithm, a simulation environment was constructed and deployed on a real prototype for testing. Results demonstrate that compared to the baseline algorithm, the proposed algorithm performs excellently in trajectory tracking tasks, avoids complex parameter tuning, and exhibits high accuracy, fast convergence, and good stability. This work provides a practical and effective solution for improving the trajectory tracking performance of differential tracked robots in complex environments. Full article

The production of apples plays a crucial role in global agriculture. In 2023, the world production of these fruits amounted to nearly 150 million tonnes, cultivated on 6.6 million ha. Today’s horticulture faces the difficult challenge of maintaining high productivity while simultaneously reducing negative environmental impact. Traditional methods based on chemical pesticides encounter increasing problems, such as biodiversity loss, toxic residues in food, development of pest resistance, and disrupted balance of ecosystems. Integrated Pest Management (IPM) responds to these challenges by combining biological and agrotechnical methods with selective use of chemicals. Biopesticides are a crucial component of IPM, and they include antagonist microorganisms, substances of natural origin, and other biological methods of control, which represent effective alternatives to conventional measures. Their development is driven by consumer requirements concerning food safety, as well as by the need to protect the environment. The aim of this article is to highlight current problems in apple production, describe microorganisms and natural substances used as biopesticides used for the protection of apple orchards, as well as present the characteristics of modern technologies used for biocontrol in apple orchards. Full article

Livestock and poultry genetic resources form the cornerstone of elite population breeding, new breed development, and global food security. The yak (Bos mutus), endemic to the Qinghai–Tibet Plateau, is indispensable for maintaining regional biodiversity and ecological stability. The Datong yak—China’s first nationally recognized cultivated yak breed and the world’s inaugural domesticated yak variety—plays a pivotal role in enhancing yak production performance, mitigating grassland–livestock conflicts, and restoring degraded grasslands. This study aimed to provide a scientific basis for the conservation and sustainable utilization of yak genetic resources by comprehensively evaluating the genetic resource value of Datong yaks. We employed the market price method, opportunity cost method, and shadow engineering method to assess four value dimensions—aligned with the Food and Agriculture Organization (FAO) livestock genetic resource value framework and adapted to China’s yak production context: direct use value (DUV), indirect use value (IUV), potential use value (PUV), and conservation value (CV). Data were collected through expert consultations, semi-structured interviews, and questionnaire surveys in Datong County (Qinghai Province, the core production area of Datong yaks) between August and September 2024, with the widely distributed Qinghai Plateau yak serving as the control breed. Based on a recent market survey, the total genetic resource value of Datong yaks in China was estimated at CNY 2.505 billion in 2024, highlighting the increasing economic and strategic significance of yak genetic resources. Among the four value dimensions, PUV accounted for the largest share (65.67%), driven by superior production performance, market price premiums, and reduced feeding costs. DUV contributed 20.72%, reflecting the value of biological assets and beef products; IUV represented 7.10%, primarily associated with grassland conservation benefits; and CV constituted 6.51%, encompassing costs for genetic resource preservation and cultural heritage contributions. These results underscore the substantial potential of Datong yak genetic resources, particularly given their unique adaptation to high-altitude environments and their critical role in supporting local livelihoods and ecological stability. Future research should focus on expanding breeding programs and genetic conservation, optimizing industrial and value chains, and strengthening genetic improvement initiatives to promote ecological security and sustainable development of the yak industry on the Qinghai–Tibet Plateau. Full article

Extensive practice has demonstrated that the continuous pursuit of high yields in the black soil region of Northeast China resulted in imbalances in soil nutrients and declines in both soil quality and water use efficiency. Alternate wetting and drying (AWD) irrigation offers a promising solution for increasing rice yield and maintaining soil fertility. However, the success of this irrigation method largely depends on its scheduling. This study examined the threshold effects of AWD on rice growth, yield, and soil nutrient availability in the Sanjiang Plain, a representative black soil region in Northeast China. A two-year trial was conducted from 2023 to 2024 at the Qixing National Agricultural Science and Technology Park. “Longjing 31”, a local cultivar, was selected as the experimental material. The lower limit of soil water content under AWD was set as the experimental factor, with three levels: −10 kPa (LA), −20 kPa (MA), and −30 kPa (SA). The local traditional irrigation practice, continuous flooding, served as the control treatment (CK). Indicators of rice growth and soil nutrient content were measured and analyzed at five growth stages: tillering, jointing, heading, milk ripening, and yellow ripening. The results showed that, compared to CK, AWD had minimal impact on rice plant height and tiller number, with no significant differences (p > 0.05). However, AWD affected leaf area index (LAI), shoot dry matter (SDM), yield, and soil nutrient availability. In 2023, control had little effect on rice plant height and tiller number among the different irrigation treatments. The LAI of LA was 11.1% and 22.5% higher than that of MA and SA, respectively, while SDM in LA was 10.5% and 17.2% higher than in MA and SA. Significant differences were found between LA and MA, as well as between LA and SA, whereas no significant differences were observed between MA and SA. The light treatment is beneficial to the growth and development of rice, while the harsh growth environment caused by the moderate and severe treatments is unfavorable to rice growth. The average contents of nitrate nitrogen (NO₃⁻-N), available phosphorus (AP), and available potassium (AK) in LA were 11.4%, 8.4%, and 9.3% higher than in MA, and 16.7%, 11.5%, and 15.0% higher than in SA, respectively. Significant differences were observed between LA and SA. This is because the light treatment facilitates the release of available nutrients in the soil, while the moderate and severe treatments hinder this process. Although panicle number per unit area and grain number per panicle in LA were 7.5% and 2.3% higher than in MA, and 10.8% and 2.2% higher than in SA, these differences were not statistically significant. Seed setting rate and thousand-grain weight showed little variation across irrigation treatments. The yield of LA was 10,233.3 kg hm⁻², 9.1% and 14.1% higher than that of MA and SA, respectively, with significant differences observed. Compared with the moderate and severe treatments, the light treatment increases indicators such as the number of panicles per unit area, grains per panicle, thousand-grain weight, and seed setting rate, resulting in significant differences among the treatments. Water use efficiency (WUE) decreased as the control level increased. The WUE of all AWD irrigation treatments was significantly higher than that of the control treatment (CK). Compared with CK, AWD reduces evaporation, percolation, and other water losses, leading to a significant decrease in water consumption. Meanwhile, the yield remains basically unchanged or even slightly increases, thus resulting in a higher WUE than CK. The trends in rice growth, soil nutrient indicators, and WUE in 2024 were generally consistent with those observed in 2023. In 2024, the yield of LA was 9832.7 kg hm⁻², 14.9% and 17.3% higher than that of MA and SA, respectively, with significant differences observed. Based on the results, the following conclusions are drawn: (1) AWD irrigation can affect the growth of rice, alter the status of available nutrients in the soil, and thereby cause changes in yield and WUE; (2) LA is the optimal treatment for increasing rice yield, improving the availability of soil available nutrients, and improving WUE; (3) Both MA and SA enhanced WUE; however, these practices negatively impacted rice growth and the concentration of soil available nutrients, leading to a concurrent decline in yield. To increase rice yield and maintain soil fertility, LA, with an irrigation upper limit of 30 mm and a soil water potential threshold of −10 kPa, is recommended for the Sanjiang Plain region. Full article

Biological control with beneficial bacteria offers a sustainable alternative to synthetic agrochemicals for managing plant pathogens and enhancing plant health. However, bacterial biocontrol agents (BCAs) remain underexploited due to regulatory hurdles (such as complex registration timelines and extensive dossier requirements) and limited strain characterization. Recent advances in omics technologies (genomics, transcriptomics, proteomics, and metabolomics) have strengthened the bioprospecting pipeline by uncovering key microbial traits involved in biocontrol. Genomics enables the identification of biosynthetic gene clusters, antimicrobial pathways, and accurate taxonomy, while comparative genomics reveals genes relevant to plant–microbe interactions. Metagenomics uncovers unculturable microbes and their functional roles, especially in the rhizosphere and extreme environments. Transcriptomics (e.g., RNA-Seq) sheds light on gene regulation during plant-pathogen-bacteria interactions, revealing stress-related and biocontrol pathways. Metabolomics, using tools like Liquid Chromatography–Mass Spectrometry (LC-MS) and Nuclear Magnetic Resonance spectroscopy (NMR), identifies bioactive compounds such as lipopeptides, Volatile Organic Compounds (VOCs), and polyketides. Co-culture experiments and synthetic microbial communities (SynComs) have shown enhanced biocontrol through metabolic synergy. This review highlights how integrating omics tools accelerates the discovery and functional validation of new BCAs. Such strategies support the development of effective microbial products, promoting sustainable agriculture by improving crop resilience, reducing chemical inputs, and enhancing soil health. Looking ahead, the successful application of omics-driven bioprospection of BCAs will require addressing challenges of large-scale production, regulatory harmonization, and their integration into real-world agricultural systems to ensure reliable, sustainable solutions. Full article

Selenium (Se) is an essential micronutrient for human health, yet its dietary intake is insufficient in many populations worldwide. Agronomic biofortification represents an effective strategy to enrich crops with Se, and Totally Controlled Environment Agriculture (TCEA) provides a reliable platform to evaluate cultivar-specific responses under standardized conditions. This study evaluated the effects of foliar sodium selenate doses of 0, 5, 10, and 15 µM on two basil (Ocimum basilicum L.) cultivars, ‘Fine Verde’ (FV) and ‘Red Rubin’ (RR), in a micro-TCEA system. The yield was not significantly different at 5–10 µM but declined by 21% at 15 µM, particularly for FV. RR out-yielded FV (+14%), whereas FV produced taller shoots. The 5 µM Se concentration did not affect the total chlorophyll content and quantum yield of photosystem II under control conditions. The highest Se dose (15 µM) decreased the chlorophyll content and electron transport rate by 18% and 12%, respectively, while increasing the stomatal conductance (50%) and transpiration rate (120%). The total phenolics content in RR was double that in FV and increased with Se, whereas the NO₃⁻ concentration in RR decreased by 9% at 10 µM. Principal component analysis separated treatments by Se dose (PC1 = 44.5%) and cultivar (PC2 = 42.7%), showing RR’s stronger connection of RR to biomass and antioxidant accumulation under moderate Se. Overall, a single foliar application of 5 µM sodium selenate appears optimal to achieve effective Se enrichment while maintaining productivity and quality. These findings support basil as a promising candidate for Se biofortification in TCEA systems, with potential contributions to dietary Se intake. Full article

A crucial task in industrial quality control, especially in the food and agriculture sectors, is the quick and precise estimation of an object’s volume. This study combines cutting-edge machine learning and computer vision techniques to provide a comprehensive, non-destructive method for predicting orange volume. We created a reliable pipeline that employs top and side views of every orange to estimate four important dimensions using a calibrated marker. These dimensions are then fed into a machine learning model that has been fine-tuned. Our method uses a range of engineered features, such as complex surface-area-to-volume ratios and new shape-based descriptors, to go beyond basic geometric formulas. Based on a dataset of 150 unique oranges, we show that the Stacking Regressor performs significantly better than other single-model benchmarks, including the highly tuned LightGBM model, achieving an $R^2$ score of 0.971. Because of its reliance on basic physical characteristics, the method is extremely resilient to the inherent variability in fruit and may be used with a variety of produce types. Because it allows for the real-time calculation of density (mass over volume) for automated defect detection and quality grading, this solution is directly applicable to a factory sorting environment. Full article

Balancing soil nitrogen leaching with production benefits remains a critical challenge in sustainable greenhouse tomato cultivation. This study evaluated the effects of reduced water-soluble nitrogen fertilizer (N) application rates on soil environmental parameters and production outcomes to optimize nitrogen management strategies. Four treatments were implemented across two growing seasons: control (CK), high-N (H), medium-N (M), and low-N (L) nitrogen fertilizer applications in soil solution (SS) and autumn–winter (AW) systems. Results demonstrated that reduced nitrogen inputs significantly decreased soil electrical conductivity and soil nitrogen retention by 88% and 83% in SS and AW, respectively, while reducing soil residual nitrate nitrogen. The tomato yield decreased by 14–26% under low fertilizer treatment, while fruit quality was substantially enhanced, with soluble solid content increasing by 56% in SS and 217% in AW for the L treatment compared to the CK. Nitrogen-use efficiency improved by 54.7% and 34.78% in SS and AW, respectively, demonstrating superior resource utilization under reduced fertilizer applications. Principal component analysis revealed that fruit quality was primarily influenced by soluble solid content, organic acid, total soluble solids, and sugar–acid ratio. Gray relational analysis identified the L treatment (361.62 kg ha⁻¹ in SS and 182.6 kg ha⁻¹ in AW) as optimal for comprehensive performance evaluation. The findings demonstrate that strategic nitrogen reduction effectively balances production benefits with environmental sustainability, providing a practical framework for sustainable nitrogen management in controlled environment agriculture. Full article

Tree endotherapy has risen to prominence in the field of precision agriculture as an innovative and sustainable method of tree care, being respectful of both environmental protection and consumer health needs. A comprehensive review of the state of the art of research in this field has made it possible to spotlight the main advantages of tree infusion, which has undergone significant progress in step with technological innovation and an increased understanding of tree anatomy and physiology. The major criticalities associated with this technique, as well as the biological and technical–operational obstacles that still hinder its wider use, are also highlighted. What emerges is an innovative and rapidly expanding technique in tree care, in both the cultivation and phytosanitary management of fruit and ornamental trees. Some of the strengths of the endotherapy technique, such as the next-to-no water consumption, the strong reduction in the use of fertilizers and pesticides, the possibility of using biological control agents (BCAs) or other products of natural origin, the precision administration of the product inside the xylem of the tree, and the efficacy (20–90%) and persistence (1–2 years) of treatments, make it one of the cornerstones of sustainable tree protection at present. With a very low consumption of the “active ingredient”, endotherapy has a negligible impact on the external environment, minimizing the drift and dispersal of the active ingredient and thus limiting the exposure of non-target organisms such as beneficial insects, birds, and wildlife. The large-scale application of the technique would therefore also help to achieve an important goal in “climate-smart agriculture”, the saving of water resources, significantly contributing to climate change mitigation, especially in those areas of the planet where water is a precious resource. Full article

Unmanned Aerial Vehicles (UAVs), commonly known as drones, have become widely used in many fields, ranging from agriculture to military operations, due to recent advances in technology and decreases in costs. Quadrotors are particularly important UAVs, but their complex, coupled dynamics and sensitivity to outside disturbances make them challenging to control. This paper introduces a new control method for quadrotors called Backstepping Sliding Mode Control (BSMC), which combines the strengths of two established techniques: Backstepping Control (BC) and Sliding Mode Control (SMC). Its primary goal is to improve trajectory tracking while also reducing chattering, a common problem with SMC that causes rapid, high-frequency oscillations. The BSMC method achieves this by integrating the SMC switching gain directly into the BC through a process of differential iteration. Herein, a Lyapunov stability analysis confirms the system’s asymptotic stability; a genetic algorithm is used to optimize controller parameters; and the proposed control strategy is evaluated under diverse payload conditions and dynamic wind disturbances. The simulation results demonstrated its capability to handle payload variations ranging from 0.5 kg to 18 kg in normal environments, and up to 12 kg during gusty wind scenarios. Furthermore, the BSMC effectively minimized chattering and achieved a superior performance in tracking accuracy and robustness compared to the traditional SMC and BC. Full article

Grape production is one of the most agronomically important activities worldwide. However, it is threatened by diseases caused by phytopathogenic microorganisms, which cause severe economic losses. The primary strategy to control phytopathogenic fungi is the application of fungicides; however, they affect the environment and induce resistance in fungi. Nanomaterials, especially those green-synthesized, emerge as an eco-friendly and sustainable alternative to control fungal pathogens. The objective of this work is to evaluate the sensitivity of fungal phytopathogens to biosynthesized copper-oxide nanoparticles (CuONPs). Nanoparticles were evaluated as preventive and corrective treatments in grapevine green tissues and fruits under field conditions, using in vitro and in vivo experimental approaches. Interestingly, corrective treatment was highly effective and showed little accumulation of Cu on the fruits, even less than a commercial copper-based fungicide. Moreover, we report that Aspergillus niger causes lesions in photosynthetic tissues and severe disease symptoms in grapes. We also describe for the first time the presence of Alternaria alternata causing lesions, mainly on the stems and young leaves of grapevine plants in Mexico. These pathogens were inhibited by the biosynthesized CuONPs. All these findings show the effectiveness of using CuONPs to control phytopathogenic fungi, even under field conditions, shedding light on their potential use in agriculture with a less environmental impact than the commercial fungicides and agrochemicals currently used. Full article

Nitrogen eutrophication represents a significant environmental challenge in Chinese aquatic ecosystems, exacerbated by rapid agricultural intensification, industrial expansion, and urban development. This review consolidates existing knowledge on the drivers and impacts of nitrogen pollution in Chinese aquatic ecosystems, with a focus on environments such as lakes, rivers, and coastal waters. The primary sources of nitrogen enrichment are excessive fertilizer application, livestock manure discharge, industrial emissions, and untreated industrial and municipal wastewater. These inputs have led to severe ecological consequences, including harmful algal blooms, hypoxia, loss of biodiversity, and deteriorating water quality, threatening ecosystem health and human well-being. The review also examines mitigation strategies implemented in China, encompassing regulatory policies such as the “Zero Growth” fertilizer initiative, as well as technological advancements in wastewater treatment and sustainable farming practices. Case studies highlighting successful interventions, such as lake restoration projects and integrated watershed management, demonstrate the potential for effective nitrogen control. However, persistent challenges remain, including uneven policy enforcement, insufficient public awareness, and gaps in scientific understanding of nitrogen cycling dynamics. This review aims to inform future efforts toward achieving sustainable nitrogen management in China by synthesizing current research and identifying key knowledge gaps. Addressing these issues is crucial for safeguarding China’s aquatic ecosystems and promoting global nutrient stewardship. Full article