Rice leaf diseases critically threaten global rice production by reducing crop yield and quality. Efficient disease detection in complex field environments remains a persistent challenge for sustainable agriculture. Existing deep learning-based methods for rice leaf disease detection struggle with inadequate sensitivity to subtle disease features, high computational complexity, and degraded accuracy under complex field conditions, such as background interference and fine-grained disease variations. To address these limitations, this research aims to develop a lightweight yet high-accuracy detection model tailored for complex field environments that balances computational efficiency with robust performance. We propose RDRM-YOLO, an enhanced YOLOv5-based network, integrating four key improvements: (i) a cross-stage partial network fusion module (Hor-BNFA) is integrated within the backbone network’s feature extraction stage to enhance the model’s ability to capture disease-specific features; (ii) a spatial depth conversion convolution (SPDConv) is introduced to expand the receptive field, enhancing the extraction of fine-grained features, particularly from small disease spots; (iii) SPDConv is also integrated into the neck network, where the standard convolution is replaced with a lightweight GsConv to increase the accuracy of disease localization, category prediction, and inference speed; and (iv) the WIoU Loss function is adopted in place of CIoU Loss to accelerate convergence and enhance detection accuracy. The model is trained and evaluated utilizing a comprehensive dataset of 5930 field-collected and augmented sample images comprising four prevalent rice leaf diseases: bacterial blight, leaf blast, brown spot, and tungro. Experimental results demonstrate that our proposed RDRM-YOLO model achieves state-of-the-art performance with a detection accuracy of 94.3%, and a recall of 89.6%. Furthermore, it achieves a mean Average Precision (mAP) of 93.5%, while maintaining a compact model size of merely 7.9 MB. Compared to Faster R-CNN, YOLOv6, YOLOv7, and YOLOv8 models, the RDRM-YOLO model demonstrates faster convergence and achieves the optimal result values in Precision, Recall, mAP, model size, and inference speed. This work provides a practical solution for real-time rice disease monitoring in agricultural fields, offering a very effective balance between model simplicity and detection performance. The proposed enhancements are readily adaptable to other crop disease detection tasks, thereby contributing to the advancement of precision agriculture technologies.

This review comprehensively examines the application of deep learning technologies across the entire soybean production chain, encompassing areas such as disease and pest identification, weed detection, crop phenotype recognition, yield prediction, and intelligent operations. By systematically analyzing mainstream deep learning models, optimization strategies (e.g., model lightweighting, transfer learning), and sensor data fusion techniques, the review identifies their roles and performances in complex agricultural environments. It also highlights key challenges including data quality limitations, difficulties in real-world deployment, and the lack of standardized evaluation benchmarks. In response, promising directions such as reinforcement learning, self-supervised learning, interpretable AI, and multi-source data fusion are proposed. Specifically for soybean automation, future advancements are expected in areas such as high-precision disease and weed localization, real-time decision-making for variable-rate spraying and harvesting, and the integration of deep learning with robotics and edge computing to enable autonomous field operations. This review provides valuable insights and future prospects for promoting intelligent, efficient, and sustainable development in soybean production through deep learning.

This study proposes ToRLNet, a lightweight tomato ripeness detector designed for real-time deployment in resource-constrained agricultural settings. Built on YOLOv12n, ToRLNet integrates three self-constructed modules (WaveFusionNet for frequency–spatial enhancement and feature extraction, ETomS for efficient context-aware encoding, and SFAConv for selective multi-scale downsampling) to address subtle inter-stage color transitions, small fruit instances, and cluttered canopies. We benchmark ToRLNet against lightweight and small-scale YOLO baselines (YOLOv8–YOLOv12) and conduct controlled ablations isolating each module’s contribution. ToRLNet attains Precision 90.27%, Recall 86.77%, F1-score 88.49%, mAP50 91.76%, and mAP 78.01% with only 6.9 GFLOPs, outperforming representative nano/small YOLO variants under comparable compute budgets. Ablation results show WaveFusionNet improves spectral–textural robustness, ETomS balances the precision–recall trade-off while reducing redundancy, and SFAConv preserves fine chromatic gradients and boundary structure during downsampling; their combination yields the most balanced performance. These findings demonstrate that ToRLNet delivers a favorable accuracy–efficiency trade-off and provides a practical foundation for on-board perception in automated harvesting, yield estimation, and greenhouse management.

Accurate real-time detection of blueberry maturity is vital for automated harvesting. However, existing methods often fail under occlusion, variable lighting, and dense fruit distribution, leading to reduced accuracy and efficiency. To address these challenges, we designed a lightweight deep learning framework that integrates improved feature extraction, attention-based fusion, and progressive transfer learning to enhance robustness and adaptability To overcome these challenges, we propose BMDNet-YOLO, a lightweight model based on an enhanced YOLOv8n. The backbone incorporates a FasterPW module with parallel convolution and point-wise weighting to improve feature extraction efficiency and robustness. A coordinate attention (CA) mechanism in the neck enhances spatial-channel feature selection, while adaptive weighted concatenation ensures efficient multi-scale fusion. The detection head employs a heterogeneous lightweight structure combining group and depthwise separable convolutions to minimize parameter redundancy and boost inference speed. Additionally, a three-stage transfer learning framework (source-domain pretraining, cross-domain adaptation, and target-domain fine-tuning) improves generalization. Experiments on 8250 field-collected and augmented images show BMDNet-YOLO achieves 95.6% mAP@0.5, 98.27% precision, and 94.36% recall, surpassing existing baselines. This work offers a robust solution for deploying automated blueberry harvesting systems.

Real-time detection of apple leaf diseases in orchard environments faces ongoing challenges, particularly in preserving fine-grained disease features with limited computing resources. To address these issues, we propose a high-precision lightweight model based on YOLOv10n, called YOLO-ALD. First, we introduce Spatial and Channel Reconstruction Convolution into deeper backbone networks to replace standard downsampling layers and convolutions. This suppresses spatial and channel redundancy caused by environmental noise and optimizes feature representation. Second, we design a new C2f-Faster-SimAM module for the neck network. This module combines the inference efficiency of FasterNet with a parameter-free 3D attention mechanism to adaptively focus on early lesions, effectively distinguishing them from leaf veins without increasing model complexity. Third, in the detection head section, we use the Focaler-ShapeIoU loss function to optimize bounding box regression. It utilizes a dynamic focusing mechanism and geometric constraints to ensure the localization accuracy of irregular shapes and hard-to-detect samples. Experimental results on our self-built dataset covering four specific diseases and healthy leaves showed that, compared with YOLOv10n, the mAP@0.5 of YOLO-ALD reached 92.1%, achieving a 2.1% increase. In addition, the model has an inference speed of 105 FPS, with only 2.1 M parameters and 5.6 GFLOPs. Therefore, YOLO-ALD achieves a good balance between efficiency and robustness, showing strong theoretical potential for resource-constrained mobile agriculture diagnosis.

Grape berries are highly susceptible to diseases during growth and harvest, which severely impacts yield and postharvest quality. While rapid and accurate disease detection is essential for real-time control and optimized management, it remains challenging due to complex symptom patterns, occlusions in dense clusters, and orchard environmental interference. Although deep learning presents a viable solution, robust methods specifically for detecting grape berry diseases under dense clustering conditions are still lacking. To bridge this gap, we propose GBDR-Net—a high-accuracy, lightweight, and deployable model based on YOLOv10. It incorporates four key enhancements: (1) an SDF-Fusion module replaces the original C2f module in deeper backbone layers to improve global context and subtle lesion feature extraction; (2) an additional Detect-XSmall head is integrated at the neck, with cross-concatenated outputs from SPPF and PSA modules, to enhance sensitivity to small disease spots; (3) the nearest-neighbor upsampling is substituted with a lightweight content-aware feature reassembly operator (LCFR-Op) for efficient and semantically aligned multi-scale feature enhancement; and (4) the conventional bounding box loss function is replaced with Inner-SIoU loss to accelerate convergence and improve localization accuracy. Evaluated on the Grape Berry Disease Visual Analysis (GBDVA) dataset, GBDR-Net achieves a precision of 93.4%, recall of 89.6%, mAP@0.5 of 90.2%, and mAP@0.5:0.95 of 86.4%, with a model size of only 4.83 MB, computational cost of 20.5 GFLOPs, and a real-time inference speed of 98.2 FPS. It outperforms models such as Faster R-CNN, SSD, YOLOv6s, and YOLOv8s across key metrics, effectively balancing detection accuracy with computational efficiency. This work provides a reliable technical solution for the intelligent monitoring of grape berry diseases in horticultural production. The proposed lightweight architecture and its design focus on dense, small-target detection offer a valuable framework that could inform the development of similar systems for other cluster-growing fruits and vegetables.