Accurate counting of rubber trees is important for yield estimation, refined field management, and the sustainable development of the rubber industry. In deep-learning-based counting, reliable canopy delineation is a prerequisite for accurate segmentation and subsequent statistical analysis. However, maintaining stable canopy segmentation under long-term and complex field conditions remains difficult, mainly because of overlapping crowns, interference from background vegetation, and pronounced seasonal changes. To address these issues, this study proposes CSAF, a rubber tree canopy segmentation model designed for high-precision canopy segmentation and counting from UAV imagery. CSAF consists of three main components: the Boundary Continuity Modelling Module (BCMM), the Physical Morphology Constraint Module (PMCM), and the Cross-Temporal Learning Adaptively Module (CTLAM). BCMM improves the representation of complex canopy boundaries by using the Fourier transform to suppress feature noise and state-space modelling to refine boundary responses. PMCM introduces a Poisson diffusion prior to constrain the segmentation results according to canopy morphology, thereby reducing the influence of irregular background vegetation. CTLAM adjusts the replay ratio and scale through a state-feedback mechanism, which helps mitigate inter-seasonal variation and improve cross-temporal generalization. In addition, this study presents RT-Set, a UAV-based rubber tree canopy dataset containing 5281 high-resolution images that cover the full growth cycle of rubber trees, from the budding stage to the defoliation stage. RT-Set provides a dedicated benchmark for precise rubber tree canopy segmentation and counting. On RT-Set, CSAF achieves an AP₅₀ of 76.63%, outperforming the compared methods. Experiments on three additional public datasets further show that CSAF remains competitive with mainstream approaches, including DetecTree2 and Cascade Mask R-CNN. In the counting experiments, CSAF also shows high accuracy and stable performance, suggesting its practical value for rubber tree counting under complex field conditions. The RT-Set dataset is available at https://github.com/zengjiangquan1/RT-Set.

Rubber products have become an important strategic resource in the global economy. However, individual rubber tree segmentation in plantation environments remains challenging due to canopy background interference and significant morphological variations among trees. To address these issues, we propose a high-precision segmentation network,TM-WSNet (Spatial Geometry Enhanced Hybrid Feature Extraction Module–Wavelet Grid Feature Fusion Encoder Segmentation Network). First, we introduce SGTramba, a hybrid feature extraction module combining Grouped Transformer and Mamba architectures, designed to reduce confusion between tree crown boundaries and surrounding vegetation or background elements. Second, we propose the WGMS encoder, which enhances structural feature recognition by applying wavelet-based spatial grid downsampling and multiscale feature fusion, effectively handling variations in canopy shape and tree height. Third, a scale optimization algorithm (SCPO) is developed to adaptively search for the optimal learning rate, addressing uneven learning across different resolution scales. We evaluate TM-WSNet on a self-constructed dataset (RubberTree) and two public datasets (ShapeNetPart and ForestSemantic), where it consistently achieves high segmentation accuracy and robustness. In practical field tests, our method accurately predicts key rubber tree parameters—height, crown width, and diameter at breast height with coefficients of determination (R²) of 1.00, 0.99, and 0.89, respectively. These results demonstrate TM-WSNet's strong potential for supporting precision rubber yield estimation and health monitoring in complex plantation environments.

Precise disease detection is crucial in modern precision agriculture, especially in ensuring the health of tomato crops and enhancing agricultural productivity and product quality. Although most existing disease detection methods have helped growers identify tomato leaf diseases to some extent, these methods typically target fixed categories. When faced with new diseases, extensive and costly manual annotation is required to retrain the dataset. To overcome these limitations, this study proposes a multimodal model PDC-VLD based on the open-vocabulary object detection (OVD) technology within the VLDet framework, which can accurately identify new tomato leaf diseases without manual annotation by using only image–text pairs. First, we developed a progressive visual transformer-convolutional pyramid module (PVT-C) that effectively extracts tomato leaf disease features and optimizes anchor box positioning using the self-supervised learning algorithm DINO, suppressing interference from irrelevant backgrounds. Then, a context feature guided module (CFG) was adopted to address the low adaptability and recognition accuracy of the model in data-scarce environments. To validate the model's effectiveness, we constructed a tomato leaf disease image dataset containing 4 base classes and 2 new categories. Experimental results show that the PDC-VLD model achieved 61.2% on the main evaluation metric mAP_novel⁵⁰, and 56.4% on mAP_novel⁷⁵, 87.7% on mAP_base⁵⁰, 81.0% on mAP_all⁵⁰, and 45.5% on average recall, outperforming existing OVD models. Our research provides an innovative solution for efficiently and accurately detecting new diseases, substantially reducing the need for manual annotation, and offering critical technical support and practical reference for agricultural workers.

Rice leaf diseases have an important impact on modern farming, threatening crop health and yield. Accurate semantic segmentation techniques are crucial for segmenting diseased leaf parts and assisting farmers in disease identification. However, the diversity of rice growing environments and the complexity of leaf diseases pose challenges. To address these issues, this study introduces an innovative semantic segmentation algorithm for rice leaf pests and diseases based on the Transformer architecture AISOA-SSformer. First, it features the sparse global-update perceptron for real-time parameter updating, enhancing model stability and accuracy in learning irregular leaf features. Second, the salient feature attention mechanism is introduced to separate and reorganize features using the spatial reconstruction module (SRM) and channel reconstruction module (CRM), focusing on salient feature extraction and reducing background interference. Additionally, the annealing-integrated sparrow optimization algorithm fine-tunes the sparrow algorithm, gradually reducing the stochastic search amplitude to minimize loss. This enhances the model’s adaptability and robustness, particularly against fuzzy edge features. The experimental results show that AISOA-SSformer achieves an 83.1% MIoU, an 80.3% Dice coefficient, and a 76.5% recall on a homemade dataset, with a model size of only 14.71 million parameters. Compared with other popular algorithms, it demonstrates greater accuracy in rice leaf disease segmentation. This method effectively improves segmentation, providing valuable insights for modern plantation management. The data and code used in this study will be open sourced at https://github.com/ZhouGuoXiong/Rice-Leaf-Disease-Segmentation-Dataset-Code.

Tomato disease control is an urgent requirement in the field of intellectual agriculture, and one of the keys to it is quantitative identification and precise segmentation of tomato leaf diseases. Some diseased areas on tomato leaves are tiny and may go unnoticed during segmentation. Blurred edge also makes the segmentation accuracy poor. Based on UNet, we propose an effective image-based tomato leaf disease segmentation method called Cross-layer Attention Fusion Mechanism combined with Multi-scale Convolution Module (MC-UNet). First, a Multi-scale Convolution Module is proposed. This module obtains multiscale information about tomato disease by employing 3 convolution kernels of different sizes, and it highlights the edge feature information of tomato disease using the Squeeze-and-Excitation Module. Second, a Cross-layer Attention Fusion Mechanism is proposed. This mechanism highlights tomato leaf disease locations via gating structure and fusion operation. Then, we employ SoftPool rather than MaxPool to retain valid information on tomato leaves. Finally, we use the SeLU function appropriately to avoid network neuron dropout. We compared MC-UNet to the existing segmentation network on our self-built tomato leaf disease segmentation dataset and MC-UNet achieved 91.32% accuracy and 6.67M parameters. Our method achieves good results for tomato leaf disease segmentation, which demonstrates the effectiveness of the proposed methods.

Tomato leaf diseases have a significant impact on tomato cultivation modernization. Object detection is an important technique for disease prevention since it may collect reliable disease information. Tomato leaf diseases occur in a variety of environments, which can lead to intraclass variability and interclass similarity in the disease. Tomato plants are commonly planted in soil. When a disease occurs near the leaf’s edge, the soil backdrop in the image tends to interfere with the infected region. These problems can make tomato detection challenging. In this paper, we propose a precise image-based tomato leaf disease detection approach using PLPNet. First, a perceptual adaptive convolution module is proposed. It can effectively extract the disease’s defining characteristics. Second, a location reinforcement attention mechanism is proposed at the neck of the network. It suppresses the interference of the soil backdrop and prevents extraneous information from accessing the network’s feature fusion phase. Then, a proximity feature aggregation network with switchable atrous convolution and deconvolution is proposed by combining the mechanisms of secondary observation and feature consistency. The network solves the problem of disease interclass similarities. Finally, the experimental results show that PLPNet achieved 94.5% mean average precision with 50% thresholds (mAP50), 54.4% average recall (AR), and 25.45 frames per second (FPS) on a self-built dataset. The model is more accurate and specific for the detection of tomato leaf diseases than other popular detectors. Our proposed method may effectively improve conventional tomato leaf disease detection and provide modern tomato cultivation management with reference experience.