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Rapid and accurate identification of soybean leaf diseases is crucial for optimizing crop health and yield. We propose a cell P system with membrane division and dissolution rules (DDC-P system) for soybean leaf disease identification. Among them, the designed Efficient feature attention (EFA) and the lightweight sandglass structure and efficient feature attention (SGEFA) can focus on disease-specific information while reducing environmental interference. A fuzzy controller was developed to manage the division and dissolution of SGEFA membranes, allowing for adaptive adjustments to the model structure and avoiding redundancy. Experimental results on the homemade soybean disease dataset show that the DDC-P system achieves a recognition rate of 98.43% with an F1 score of 0.9874, while the model size is only 1.41 MB. On the public dataset, the DDC-P system achieves an accuracy of 94.40% with an F1 score of 0.9425. The average recognition time on the edge device is 0.042857 s, with an FPS of 23.3. These outstanding results demonstrate that the DDC-P system not only excels in recognition and generalization but is also ideally suited for deployment on edge devices, revolutionizing the approach to soybean leaf disease management. Graphical abstract Highlights We propose a cell P system with membrane division and dissolution rules for rapid identification of soybean leaf diseases that improves model real-time performance by reducing the amount of computation. The EFA and SGEFA membranes were designed to reduce background interference and focus on disease information. Fuzzy controllers were designed to control the triggering of membrane division and membrane dissolution rules, thus controlling the number of SGEFA membranes in parallel and dynamically adjusting the model structure to avoid redundancy. A self-developed soybean disease dataset containing 8883 images was obtained. The method proposed in this study was successfully deployed on edge devices and could provide a new basis for soybean disease diagnosis.

Soybean is one of the world’s major oil-bearing crops and occupies an important role in the daily diet of human beings. However, the frequent occurrence of soybean leaf diseases caused serious threats to its yield and quality during soybean cultivation. Rapid identification of soybean leaf diseases could provide a better solution for efficient control and subsequent precision application. In this study, a lightweight deep convolutional neural network (CNN) based on multiscale feature extraction fusion (MFEF) and combined with a dense connectivity (DC) network (MFEF-DCNet) was proposed for soybean leaf disease identification. In MFEF-DCNet, a multiscale feature extraction fusion (MFEF) module for soybean leaves was constructed by utilizing a convolutional attention module and depth-separable convolution to improve the model feature extraction capability. Multiscale features are fused by using dense connections (DC) in the backbone network to improve the model generalization capability. Experiments were implemented on eight distinct disease and deficiency classes of soybean images (including bacterial blight, cercospora leaf blight, downy mildew, frogeye leaf spot, healthy, potassium deficiency, soybean rust, and target spot) using the proposed network. The results showed that the MFEF-DCNet had an accuracy of 0.9470, an average precision of 0.9510, an average recall of 0.9480, and an F1-score of 0.9490 for soybean leaf disease identification. And MFEF-DCNet had certain performance advantages in terms of classification accuracy, convergence speed and other effects compared with VGG16, ResNet50, DenseNet201, EfficientNetB0, Xception and MobileNetV3_small models. In addition, the accuracy of the MFEF-DCNet model in recognizing soybean diseases in local data was 0.9024, which indicated that the MFEF-DCNet model had favorable application in practical applications. The proposed model and experience in this study could provide useful inspiration for automated disease identification in soybean and other crops.

The rapid and accurate identification of soybean diseases is critical for optimizing both yield and quality. Traditional image recognition techniques face notable limitations in terms of generalization and accuracy, particularly when tasked with identifying small-scale targets or distinguishing diseases with similar characteristics in large, heterogeneous, and complex environments. To address these challenges, this study proposes the YOLOv8-DML model for soybean leaf disease recognition. Building upon YOLOv8n, this model integrates a DWR module that replaces the high-level C2f module with C2f-DWR, enhancing feature extraction across varied receptive fields. Additionally, modifications to the neck structure incorporate a Multi-scale Enhanced Feature Pyramid (MEFP), which improves detection performance across targets of varying sizes by enabling effective multi-scale information fusion. A lightweight detection head (LSCD) is further introduced to facilitate multiscale feature interactions while reducing the overall model parameter count. Lastly, the WIoUv3 loss function is employed to place greater emphasis on small targets and moderate-quality samples, thereby enhancing detection precision. Experimental results demonstrate that YOLOv8-DML achieves a mAP50 of 96.9%, marking a 1.8% improvement over the original YOLOv8 algorithm, while also achieving an 18.6% reduction in parameters. Comparative analysis with other mainstream object detection models indicates that YOLOv8-DML delivers superior overall performance, highlighting its significant potential for effective soybean leaf disease identification.

Soybean diseases are the main factors causing serious yield reduction in soybeans. Therefore, an automatic identification method for soybean leaf diseases is an urgent need to develop. However, most soybean leaf disease recognition models have problems such as weak generalization ability and insufficient labeled soybean images. To overcome these problems, this paper proposes an improved method for unsupervised domain generalization (UDG) named Leaf-Bridge Across Domains (LBADs), which is dedicated to cross-domain soybean leaf disease image recognition. Our method builds a semantic-preserving image-to-image mapping between each training domain and an auxiliary leaf-bridge domain to achieve cross-domain semantic alignment of soybean disease images in the shared feature space through fully unsupervised self-supervised contrastive learning. Firstly, LBAD is performed on both labeled source and target domain data. To bridge the gap between these two domains, it constructs a cross-domain aided leaf-bridge domain within a contrastive self-supervised model, mapping images from different domains into a shared space to achieve semantic alignment. After the fully unsupervised pre-training of the feature backbone, we use 1%, 5%, and 10% of the labeled source domain images, respectively, to fine-tune the linear classifier only (the pre-trained backbone weights are completely frozen) and complete the classification of unlabeled target domain images. The highest accuracy of the three-label fraction can reach 83.56%, 89.22%, and 87.2%, respectively, higher than that of the model without LBAD. The final identification result is also higher than the accuracy of the model trained without LBAD. Experimental results showed that LBAD can improve the generalization ability of the model and obtain a more accurate soybean leaf disease recognition model.

Soybean leaf disease is one of the major problems that reduces the agricultural productivity. Detection of the soybean leaf diseases based on their category is a strategy that can annihilate them and result in an increased productivity. Moreover, erroneous detection of disease can lead to inappropriate treatments that can affect the healthy leaves. To overcome this problem, proper detection and classification techniques have to be implemented. To accurately detect and classify the diseases of the soybean leaf, Dense Inception Module based U Net Segmentation- (DIM-U-Net) with a Sparse Regularized Auto Encoder (SR-AE) and Long Short-Term Memory (LSTM) for classification is proposed. In this study, the soybean leaf diseases from the images are detected based on the DIM-U-Net which is a deep learning model that segments the image with encoding and decoding processes whose output which is the segmented image is sent to the SR-AE for feature extraction. Finally, an LSTM classification is applied to classify the leaves into three classes namely, Angular Spot, Bean Rust and Healthy. This avoids the wrong detection of the category of the leaf disease. The DIM-U-Net detected the diseased leaves and the various performance metrics such as Classification Accuracy Rate-CAR, sensitivity, specificity, precision, F1 score and AUC values have been identified in the classification of the leaves and by comparison the proposed DIM-U-Net model outperforms the existing methods.

Accurate identification of soybean leaf disease is of utmost importance for its cultivation and fine management, as it is a critical factor contributing to the decreased quality and yield of soybean. Nevertheless, several existing studies on the identification of soybean leaf disease encounter the challenge of striking a balance between model expressiveness and practical applicability. To address such issue, we propose a well-designed two-stage feature aggregation network framework (TFANet). The main component of it is the two-stage feature aggregation (TFA) module, which is designed to achieve great feature representation capability by aggregating feature information from different convolutional layers in two stages. Meanwhile, TFA module incorporates the efficient channel attention to extract crucial disease information. Afterwards, to limit the loss of feature information and expand the receptive field, a dilated convolution-based feature fusion (DCFF) module is developed. Additionally, the InceptionC module is introduced to further achieve better performance. The experimental results demonstrated that TFANet obtained an accuracy of 98.18% and an F1 scores of 98.39%, with merely 1.18 M parameters. In comparison with some classic convolutional neural network models, TFANet showed remarkable superiority in multiple evaluation metrics. Hence, TFANet has great potential to be used in practical soybean leaf disease identification.

We propose a novel approach for identifying soybean leaf diseases in the natural environment by convolutional neural network (CNN). AlexNet, GoogLeNet and ResNet were utilized for transfer learning. Firstly, 27 models were obtained by setting different batch sizes and the number of iterations. Then, the effects of CNN structure on identification performance were explored. The optimal model is based on ResNet and has the highest accuracy of 94.29%. In the parameter settings of the optimal network, the number of iterations and batch size are 1056 and 16, respectively, and the training depth is 140. Overall, the proposed method is effective for identifying soybean leaf diseases in the natural environment.

Recognition of plant leaf diseases plays an important role in improving the performance of recent agribusiness as plant diseases can cause production and economic losses in the agriculture field. There are many causes of plant diseases like insect attacks, weather, and environmental conditions, and a lack of knowledge among the farmers about the disease.Small and medium farmers generally fail to identify anomalies to take preventive measures. Several methods have been proposed to identify plants leaf diseases by color, shape, vein, and texture features using classical machine learning techniques like ANNs, Decision Trees, k-nearest neighbors (KNN) algorithm, K-means, and Support Vector Machines (SVMs) [6, 8]. In Section VI results and discussion and finally in Section VII Conclusion and Future Scope of this work are discussed. 2 Related Work S. Shrivastava in [8], presented a fully automated disease detection and disease severity level estimation approach based on digital image sensing and processing.

Background In plants, the leaf functions as a solar panel, where photosynthesis converts carbon dioxide and water into carbohydrates and oxygen. In soybean, leaf type traits, including leaf shape, leaf area, leaf width, and leaf width so on, are considered to be associated with yield. In this study, we performed morphological characterization, transcriptome analysis, and endogenous hormone analysis of a rolled and narrow leaf mutant line ( rl ) in soybean. Results Compared with wild type HX3, mutant line rl showed rolled and narrower leaflet, and smaller leaf, meanwhile rl also performed narrower pod and narrower seed. Anatomical analysis of leaflet demonstrated that cell area of upper epidermis was bigger than the cell area of lower epidermis in rl , which may lead rolled and narrow leaf. Transcriptome analysis revealed that several cytokinin oxidase/dehydrogenase (CKX) genes ( Glyma.06G028900 , Glyma.09G225400 , Glyma.13G104700 , Glyma.14G099000 , and Glyma.17G054500 ) were up-regulation dramatically, which may cause lower cytokinin level in rl . Endogenous hormone analysis verified that cytokinin content of rl was lower. Hormone treatment results indicated that 6-BA rescued rolled leaf enough, rescued partly narrow leaf. And after 6-BA treatment, the cell area was similar between upper epidermis and lower epidermis in rl . Although IAA content and ABA content were reduced in rl , but exogenous IAA and ABA didn’t affect leaf type of HX3 and rl . Conclusions Our results suggest abnormal cytokinin metabolism caused rolled and narrow leaf in rl , and provide valuable clues for further understanding the mechanisms underlying leaf development in soybean.

Background Frogeye leaf spot is a disease of soybean, and there are limited sources of crop genetic resistance. Accurate quantification of resistance is necessary for the discovery of novel resistance sources, which can be accelerated by using a low-cost and easy-to-use image analysis system to phenotype the disease. The objective herein was to develop an automated image analysis phenotyping pipeline to measure and count frogeye leaf spot lesions on soybean leaves with high precision and resolution while ensuring data integrity. Results The image analysis program developed measures two traits: the percent of diseased leaf area and the number of lesions on a leaf. Percent of diseased leaf area is calculated by dividing the number of diseased pixels by the total number of leaf pixels, which are segmented through a series of color space transformations and pixel value thresholding. Lesion number is determined by counting the number of objects remaining in the image when the lesions are segmented. Automated measurement of the percent of diseased leaf area deviates from the manually measured value by less than 0.05% on average. Automatic lesion counting deviates by an average of 1.6 lesions from the manually counted value. The proposed method is highly correlated with a conventional method using a 1–5 ordinal scale based on a standard area diagram. Input image compression was optimal at a resolution of 1500 × 1000 pixels. At this resolution, the image analysis method proposed can process an image in less than 10 s and is highly concordant with uncompressed images. Conclusion Image analysis provides improved resolution over conventional methods of frogeye leaf spot disease phenotyping. This method can improve the precision and resolution of phenotyping frogeye leaf spot, which can be used in genetic mapping to identify QTLs for crop genetic resistance and in breeding efforts for resistance to the disease.