Search Results (1,232)

As global agriculture shifts to intelligence and precision, crop attribute detection has become foundational for intelligent systems (harvesters, UAVs, sorters). It enables real-time monitoring of key indicators (maturity, moisture, disease) to optimize operations—reducing crop losses by 10–15% via precise cutting height adjustment—and boosts resource-use efficiency. This review targets harvesting-stage and in-field monitoring for grains, fruits, and vegetables, highlighting practical technologies: near-infrared/Raman spectroscopy (non-destructive internal attribute detection), 3D vision/LiDAR (high-precision plant height/density/fruit location measurement), and deep learning (YOLO for counting, U-Net for disease segmentation). It addresses universal field challenges (lighting variation, target occlusion, real-time demands) and actionable fixes (illumination compensation, sensor fusion, lightweight AI) to enhance stability across scenarios. Future trends prioritize real-world deployment: multi-sensor fusion (e.g., RGB + thermal imaging) for comprehensive perception, edge computing (inference delay < 100 ms) to solve rural network latency, and low-cost solutions (mobile/embedded device compatibility) to lower smallholder barriers—directly supporting scalable precision agriculture and global sustainable food production. Full article

Quantitative estimation of rapeseed yield is important for precision crop management and sustainable agricultural development. Traditional manual measurements are inefficient and destructive, making them unsuitable for large-scale applications. This study proposes a canopy-volume estimation and yield-modeling framework based on unmanned aerial vehicle light detection and ranging (UAV-LiDAR) data combined with a HybridMC-Poisson reconstruction algorithm. At the early yellow ripening stage, 20 rapeseed plants were reconstructed in 3D, and field data from 60 quadrats were used to establish a regression relationship between plant volume and yield. The results indicate that the proposed method achieves stable volume reconstruction under complex canopy conditions and yields a volume–yield regression model. When applied at the field scale, the model produced predictions with a relative error of approximately 12% compared with observed yields, within an acceptable range for remote sensing–based yield estimation. These findings support the feasibility of UAV-LiDAR–based volumetric modeling for rapeseed yield estimation and help bridge the scale from individual plants to entire fields. The proposed method provides a reference for large-scale phenotypic data acquisition and field-level yield management. Full article

Advancements in genome editing have transformed agricultural biotechnology by allowing for precise modifications of DNA. This technology has sparked increasing interest in enhancing important traits of major crops, including peanuts. As a nutritionally rich legume prized for its high oil content, peanut production still faces significant challenges, including disease outbreaks, nutrient deficiencies, and pest infestations. Addressing these challenges is essential for achieving high yields and sustainable cultivation. CRISPR technology, a cutting-edge genome editing tool, has emerged as a powerful platform for improving peanut traits. Its ability to facilitate gene knockouts, regulate gene expression, and introduce targeted genetic changes has accelerated research efforts in this field. The successful applications of CRISPR in peanut improvement, such as increasing oleic acid content and reducing allergenicity, reassure us about the effectiveness and potential of this technology. Despite the complexity of the peanut genome as a polyploid crop, these successes demonstrate the power of genome editing. This review emphasizes the crucial role of genome editing in enhancing peanut traits and outlines the promising future of CRISPR-based approaches in advancing peanut breeding and agricultural productivity. Full article

Intercropping triggers coordinated changes in gene expression and metabolite accumulation across sugarcane roots, stems, and leaves, leading to higher crop yields—an effect that has drawn growing attention. Yet, how this transcriptional and metabolic interplay precisely enhances productivity remains poorly understood, limiting insight into intercropping’s yield-promoting mechanisms. This research explored the relationships between sugarcane, its metabolites, and transcriptomes through field trials integrated with multi-omics analysis. Data from the field showed clear differences in gene expression and metabolite patterns between monoculture and intercropped sugarcane. Plants under intercropping displayed stronger differential gene expression, greater metabolite diversity, and shifts in physiological traits. Metabolite variation was closely linked to gene regulation and network complexity, which in turn affected key agricultural characteristics including plant height, stem thickness, and sugar content. Follow-up experiments confirmed that applying zinc—a element boosted by intercropping—improved growth in monoculture sugarcane and modified its hormonal composition. These results highlight the important role of coordinated transcriptome-metabolite activity in intercropping systems. The study provides valuable perspectives for making intensive farming more economical and sustainable, supporting efforts to raise crop output and improve ecosystem functions. Full article

The integration of beneficial microorganisms with sensor technologies represents a transformative advancement toward sustainable smart agriculture. This review synthesizes recent progress in combining microbial bioinoculants with sensor-based monitoring systems to enhance crop productivity, resource-use efficiency, and environmental resilience. Beneficial bacteria and fungi improve nutrient cycling, stress tolerance, and soil fertility thereby reducing the reliance on chemical fertilizers and pesticides. In parallel, sensor networks—including soil moisture, nutrient, environmental, and remote-sensing platforms—enable real-time, data-driven management of agroecosystems. Integrated microbe–sensor approaches have demonstrated 10–25% yield increases and up to 30% reductions in agrochemical inputs under optimized field conditions. We propose an integrative Microbe–Sensor Closed Loop (MSCL) framework in which microbial activity and sensor feedback interact dynamically to optimize inputs, monitor plant–soil interactions, and sustain productivity. Key applications include precision fertilization, stress diagnostics, and early detection of nutrient or pathogen imbalances. The review also highlights barriers to large-scale adoption, such as variable field performance of inoculants, high sensor costs, and limited interoperability of data systems. Addressing these challenges through standardization, cross-disciplinary collaboration, and farmer training will accelerate the transition toward climate-smart, self-regulating agricultural systems. Collectively, the integration of biological and technological innovations provides a clear pathway toward resilient, resource-efficient, and ecologically sound food production. Full article

The intricate connections between plants and the microbial populations that surround them are crucial for plant development and resilience, but little is known about the evolutionary processes influencing these partnerships. Less is known about how pathogenic and beneficial microbes coevolve with their plant hosts over ecological and evolutionary timeframes, despite the fact that several studies identify rhizosphere and endophytic microbes that support nutrient acquisition, disease resistance, and stress tolerance. Using molecular, ecological, and evolutionary investigations from soil, rhizosphere, and endosphere habitats, this review summarizes current findings on microbial coevolution in plant–microbe systems. We look at the endosymbiotic processes that underlie the development of organelles, the mechanisms of mutualism and antagonism, and the eco-evolutionary feedbacks that affect plant health and agricultural output. The inadequate comprehension of intraspecific microbial diversity, the application of laboratory coevolution experiments to field settings, and the long-term effects of climate change on the evolutionary dynamics of plants and microbiomes are some of the major knowledge gaps. When pathogenic and beneficial microbes apply selective pressures to one another and their common host, coevolution takes place. This results in mutual genetic and physiological adaptations, such as modifications to host immunity, microbial virulence, or competitive tactics, which influence the way the two types interact over time. We conclude that understanding plants as holobiont-integrated units of hosts and their microbiomes offers fresh chances to develop microbiome-based approaches to sustainable agriculture, such as coevolutionary breeding programs, precision biofertilizers, and resilient cropping systems. Full article

The accurate classification of plant diseases is vital for global food security, as diseases can cause major yield losses and threaten sustainable and precision agriculture. The classification of plant diseases in low-light noisy environments is crucial because crops can be continuously monitored even at night. Important visual cues of disease symptoms can be lost due to the degraded quality of images captured under low-illumination, resulting in poor performance of conventional plant disease classifiers. However, researchers have proposed various techniques for classifying plant diseases in daylight, and no studies have been conducted for low-light noisy environments. Therefore, we propose a novel model for classifying plant diseases from low-light noisy images called dilated pixel attention network (DPA-Net). DPA-Net uses a pixel attention mechanism and multi-layer dilated convolution with a high receptive field, which obtains essential features while highlighting the most relevant information under this challenging condition, allowing more accurate classification results. Additionally, we performed fractal dimension estimation on diseased and healthy leaves to analyze the structural irregularities and complexities. For the performance evaluation, experiments were conducted on two public datasets: the PlantVillage and Potato Leaf Disease datasets. In both datasets, the image resolution is 256 × 256 pixels in joint photographic experts group (JPG) format. For the first dataset, DPA-Net achieved an average accuracy of 92.11% and harmonic mean of precision and recall (F1-score) of 89.11%. For the second dataset, it achieved an average accuracy of 88.92% and an F1-score of 88.60%. These results revealed that the proposed method outperforms state-of-the-art methods. On the first dataset, our method achieved an improvement of 2.27% in average accuracy and 2.86% in F1-score compared to the baseline. Similarly, on the second dataset, it attained an improvement of 6.32% in average accuracy and 6.37% in F1-score over the baseline. In addition, we confirm that our method is effective with the real low-illumination dataset self-constructed by capturing images at 0 lux using a smartphone at night. This approach provides farmers with an affordable practical tool for early disease detection, which can support crop protection worldwide. Full article

Food shortages are becoming increasingly urgent due to the growing global population. Enhancing oil crop yields, particularly sunflowers, is key to ensuring food security and the sustainable provision of vegetable fats essential for human nutrition and animal feed. However, sunflower yields are often reduced by diseases, pests, and other factors. Remote sensing technologies, such as unmanned aerial vehicle (UAV) scans and satellite monitoring, combined with machine learning algorithms, provide powerful tools for monitoring crop health, diagnosing diseases, mapping fields, and forecasting yields. These technologies enhance agricultural efficiency and reduce environmental impact, supporting sustainable development in agriculture. This systematic review aims to assess the accuracy of various machine learning technologies, including classification and segmentation algorithms, convolutional neural networks, random forests, and support vector machines. These methods are applied to monitor sunflower crop conditions, diagnose diseases, and forecast yields. It provides a comprehensive analysis of current methods and their potential for precision farming applications. The review also discusses future research directions, including the development of automated systems for crop monitoring and disease diagnostics. Full article

The escalating challenges of climate change, soil degradation, and the need to ensure global food security are driving the transition towards more sustainable agricultural practices. Biostimulants, a diverse category of substances and microorganisms, have emerged as promising tools to enhance crop resilience, improve nutrient use efficiency (NUE), and support sustainable intensification. However, their widespread adoption is hampered by significant variability in efficacy and a lack of consensus on their optimal use. This comprehensive review synthesizes current scientific knowledge to critically evaluate the performance of biostimulants within sustainable agricultural systems. It aims to move beyond isolated case studies to provide a holistic analysis of their modes of action, efficacy under stress, and interactions with the environment. The analysis confirms that biostimulant efficacy is inherently context-dependent, governed by a complex interplay of biological, environmental, and management factors. Performance variability is explained by four core principles: the Limiting Factor Principle, the Biological Competition Axiom, the Stress Gradient Hypothesis, and the Formulation and Viability Imperative. A significant disconnect exists between promising controlled-environment studies and variable field results, highlighting the danger of extrapolating data without accounting for real-world agroecosystem complexity. Biostimulants are not universal solutions but are sophisticated tools whose value is realized through context-specific application. Their successful integration requires a precision-based approach aligned with specific agronomic challenges. We recommend that growers adopt diagnostic tools and on-farm trials, while producers must provide transparent multi-location field data and invest in advanced formulations. Future research must prioritize field validation, mechanistic studies using omics tools, and the development of crop-specific protocols and industry-wide standards to fully unlock the potential of biostimulants for building resilient and productive agricultural systems. Full article

To address prevalent challenges in field-based wheat pest recognition—namely, viewpoint perturbations, sample scarcity, and heterogeneous data distributions—a pest identification framework named CropCLR-Wheat is proposed, which integrates self-supervised contrastive learning with an attention-enhanced mechanism. By incorporating a viewpoint-invariant feature encoder and a diffusion-based feature filtering module, the model significantly enhances pest damage localization and feature consistency, enabling high-accuracy recognition under limited-sample conditions. In 5-shot classification tasks, CropCLR-Wheat achieves a precision of 89.4%, a recall of 87.1%, and an accuracy of 88.2%; these metrics further improve to 92.3%, 90.5%, and 91.2%, respectively, under the 10-shot setting. In the semantic segmentation of wheat pest damage regions, the model attains a mean intersection over union (mIoU) of 82.7%, with precision and recall reaching 85.2% and 82.4%, respectively, markedly outperforming advanced models such as SegFormer and Mask R-CNN. In robustness evaluation under viewpoint disturbances, a prediction consistency rate of 88.7%, a confidence variation of only 7.8%, and a prediction consistency score (PCS) of 0.914 are recorded, indicating strong stability and adaptability. Deployment results further demonstrate the framework’s practical viability: on the Jetson Nano device, an inference latency of 84 ms, a frame rate of 11.9 FPS, and an accuracy of 88.2% are achieved. These results confirm the efficiency of the proposed approach in edge computing environments. By balancing generalization performance with deployability, the proposed method provides robust support for intelligent agricultural terminal systems and holds substantial potential for wide-scale application. Full article

Accurate segmentation of crops and weeds is essential for enhancing crop yield, optimizing herbicide usage, and mitigating environmental impacts. Traditional weed management practices, such as manual weeding or broad-spectrum herbicide application, are labor-intensive, environmentally harmful, and economically inefficient. In response, this study introduces a novel precision agriculture framework integrating Unmanned Aerial Vehicle (UAV)-based remote sensing with advanced deep learning techniques, combining Super-Resolution Reconstruction (SRR) and semantic segmentation. This study is the first to integrate UAV-based SRR and semantic segmentation for tobacco fields, systematically evaluate recent Transformer and Mamba-based models alongside traditional CNNs, and release an annotated dataset that not only ensures reproducibility but also provides a resource for the research community to develop and benchmark future models. Initially, SRR enhanced the resolution of low-quality UAV imagery, significantly improving detailed feature extraction. Subsequently, to identify the optimal segmentation model for the proposed framework, semantic segmentation models incorporating CNN, Transformer, and Mamba architectures were used to differentiate crops from weeds. Among evaluated SRR methods, RCAN achieved the optimal reconstruction performance, reaching a Peak Signal-to-Noise Ratio (PSNR) of 24.98 dB and a Structural Similarity Index (SSIM) of 69.48%. In semantic segmentation, the ensemble model integrating Transformer (DPT with DINOv2) and Mamba-based architectures achieved the highest mean Intersection over Union (mIoU) of 90.75%, demonstrating superior robustness across diverse field conditions. Additionally, comprehensive experiments quantified the impact of magnification factors, Gaussian blur, and Gaussian noise, identifying an optimal magnification factor of 4$\times$, proving that the method was robust to common environmental disturbances at optimal parameters. Overall, this research established an efficient, precise framework for crop cultivation management, offering valuable insights for precision agriculture and sustainable farming practices. Full article

The accurate monitoring of crop water status is critical for optimizing irrigation strategies in winter wheat. Compared with satellite remote sensing, unmanned aerial vehicle (UAV) technology offers superior spatial resolution, temporal flexibility, and controllable data acquisition, making it an ideal choice for the small-scale monitoring of crop water status. During 2023–2025, field experiments were conducted to predict crop water status using UAV images in the North China Plain (NCP). Thirteen vegetation indices were calculated and their correlations with observed crop water content (CWC) and equivalent water thickness (EWT) were analyzed. Four machine learning (ML) models, namely, random forest (RF), decision tree (DT), LightGBM, and CatBoost, were evaluated for their inversion accuracy with regard to CWC and EWT in the 2024–2025 growing season of winter wheat. The results show that the ratio vegetation index (RVI, NIR/R) exhibited the strongest correlation with CWC (R = 0.97) during critical growth stages. Among the ML models, CatBoost demonstrated superior performance, achieving R² values of 0.992 (CWC) and 0.962 (EWT) in training datasets, with corresponding RMSE values of 0.012% and 0.1907 g cm⁻², respectively. The model maintained robust performance in testing (R² = 0.893 for CWC, and R² = 0.961 for EWT), outperforming conventional approaches like RF and DT. High-resolution (5 cm) inversion maps successfully identified spatial variability in crop water status across experimental plots. The CatBoost-RVI framework proved particularly effective during the booting and flowering stages, providing reliable references for precision irrigation management in the NCP. Full article

Agroforestry systems offer clear environmental and agronomic advantages, but their effect on plant–biotic stressor interactions remains poorly understood. Specifically, the shade from companion trees can create microclimates favorable to fungal diseases on herbaceous crops. This potential drawback may offset other benefits, highlighting the urgent need for advanced plant health monitoring in these systems. This study assessed the potential of hyperspectral reflectance to detect the single and combined effects of simulated tree shading and infection by the fungal pathogen Alternaria alternata on grain sorghum (Sorghum bicolor L. Moench) under rainfed field conditions. Sorghum was grown either under full light or 50% shading conditions. Half of the plots were artificially inoculated with an A. alternata spore suspension (2 × 10⁸ CFU mL⁻¹), while the others served as controls. Leaf and ground-canopy measurements were acquired with a full range spectroradiometer (VNIR-SWIR, 400–2,400 nm) and UAV imagery covered the VIS-NIR range (400–1,000 nm) before the onset of visible symptoms. Permutational multivariate analysis of variance of leaf and ground-canopy data revealed significant effects of shading (Sh), infection (Aa), and their interaction (p < 0.05), allowing early detection of infection two days before symptom appearance, while UAV data showed only singular significant effects. Partial least squares discriminant analysis accuracy reached 78% at the leaf level, 90% at the ground-canopy level, and 74% (Sh) and 75% (Aa) at the UAV scale. Furthermore, vegetation spectral indices derived from the spectra confirmed greater physiological stress in shaded and infected plants, consistent with disease incidence assessments. Our results establish scale-specific hyperspectral reflectance spectroscopy as a powerful, non-destructive technique for early plant health surveillance in agroforestry. This advanced optical sensing capability is poised to illuminate complex stressor interactions, marking a significant step forward for precision agroforestry management. Full article

The swift integration of AI, robotics, and advanced sensing technologies has revolutionized agriculture into a data-centric, autonomous, and sustainable sector. This systematic study examines the interplay between artificial intelligence and agricultural robotics in intelligent farming systems. Artificial intelligence, machine learning, computer vision, swarm robotics, and generative AI are analyzed for crop monitoring, precision irrigation, autonomous harvesting, and post-harvest processing. Employing PRISMA to categorize more than 10,000 high-impact publications from Scopus, WoS, and IEEE. Drones and vision-based models predominate the industry, while IoT integration, digital twins, and generative AI are on the rise. Insufficient field validation rates, inadequate crop and regional representation, and the implementation of explainable AI continue to pose significant challenges. Inadequate model generalization, energy limitations, and infrastructural restrictions impede scalability. We identify solutions in federated learning, swarm robotics, and climate-smart agricultural artificial intelligence. This paper presents a framework for inclusive, resilient, and feasible AI-robotic agricultural systems. Full article

Model-based simulation of farmland evapotranspiration and crop growth facilitates precise monitoring of crop and farmland dynamics with high efficiency, real-time responsiveness, and continuity. However, there are still significant limitations in using crop models to simulate the dynamic process of evapotranspiration and cotton growth in mulched drip-irrigated cotton fields under different irrigation gradients. The SWAP crop growth model effectively simulates crop growth. However, the original SWAP model lacks a dedicated module to consider the impact of mulching on cotton field evapotranspiration and cotton dry matter mass. Therefore, in this study, the source codes of the soil moisture, evapotranspiration, and crop growth modules of the SWAP model were improved. The evapotranspiration and cotton growth data of the mulched drip-irrigated cotton fields under three irrigation treatments (W1 = 3360 m³·hm⁻², W2 = 4200 m³·hm⁻², and W3 = 5040 m³·hm⁻²) in 2023 and 2024 at the Xinjiang Modern Water-saving Irrigation Key Experimental Station of the Corps were used to verify the simulation accuracy of the improved SWAP model. Research shows the following: (1) The average relative errors of the simulated evapotranspiration, leaf area index, and dry matter weight of cotton in the improved SWAP crop growth model are all <20% compared with the measured values. The root means square errors of the three treatments (W1, W2, and W3) ranged from 0.85 to 1.38 mm, from 0.03 to 0.18 kg·hm⁻², and 55.01 to 69 kg·hm⁻², respectively. The accuracy of the improved model in simulating evapotranspiration and cotton growth in the mulched cotton field increased by 37.49% and 68.25%, respectively. (2) The evapotranspiration rate of cotton fields is positively correlated with the irrigation water volume and is most influenced by meteorological factors such as temperature and solar radiation. During the flowering stage, evapotranspiration accounted for 62.83%, 62.09%, 61.21%, 26.46%, 40.01%, and 38.8% of the total evapotranspiration. Therefore, the improved SWAP model can effectively simulate the evaporation and transpiration of the mulched drip-irrigated cotton fields in the Manas River Basin. This study provides a scientific basis for the digital simulation of mulched farmland in the arid regions of Northwest China. Full article