Manual observation bias in identifying cabbage leaf diseases necessitates efficient automated detection systems in agriculture. In this study, a deep learning-based approach was developed for the automatic recognition and categorization of cabbage diseases, focusing on aphids, worm cuts, and black rot. Using 1400 images, including healthy and diseased samples, our method employs novel deep learning models. EfficientNetB7, MobileNetV2, and DenseNet201 were utilized to successfully identify three types of cabbage leaf diseases with accuracies of 98.56%, 98.26%, and 97.64%, respectively. EfficientNetB7 achieved the highest accuracy of 98.56% for disease classification. The system's ability to automatically extract features from images enhances disease detection, crucial for mitigating yield losses. Through visualization techniques, we analyzed the location of unhealthy regions in the leaves, the filter, and the intermediate layer.

A deep learning model was developed to detect, classify, and assess the severity of potato leaf diseases using 4200 images captured with a smartphone. The dataset, including healthy and diseased leaves affected by early and late blight, was preprocessed with techniques like median filtering and data augmentation. For disease identification, three classes, early blight, healthy, and late blight, were targeted. For severity level detection, potato leaf images with early and late blight were categorized into six classes: EB-low, EB-moderate, EB-severe, LB-low, LB-moderate, and LB-severe. The proposed model's performance was compared with AlexNet and VGG16. The model, using a CNN architecture with 1 fully connected and 11 convolutional layers, achieved an overall accuracy of 99% for disease classification. For severity detection, the model reached a 96% average accuracy. Performance was compared to AlexNet and VGG16, with the proposed model outperforming both in all metrics. The softmax activation was used in the output layer to classify potato leaf diseases as well as categorize the severity level of diseased leaf images.

Common bean is one of the most important crops used by Ethiopian farmers for export and local consumption. However, the quality and quantity of this crop are heavily affected by different leaf diseases and affect crop growth. Currently, common bean disease detection is performed through expert visual observation. Disease detection through observation is costly, time-consuming, and inaccurate. As a result, in this paper, a novel deep convolutional neural network model is proposed for the automatic identification of common bean leaf diseases. This research mainly focuses on the identification of three common bean leaf diseases, such as common bean brown leaf spot, common bean leaf rust, and common bean leaf beetle. The proposed model was trained using a total of 1766 images of diseased and healthy common bean leaves. The proposed model has 12 convolutional layers. The classes of the diseases were classified using a softmax classifier. The proposed model achieved a training accuracy of 98%, a validation accuracy of 97.2%, and a testing accuracy of 96%. We retrained the pre-trained models (AlexNet, GoogleNet, VGG16) in a similar simulation environment to make a comparison with the proposed model. Therefore, the proposed model achieved better performance than the pre-trained models.

Field peas are grown by smallholder farmers in Ethiopia for food, fodder, income, and soil fertility. However, leaf diseases such as ascochyta blight, powdery mildew, and leaf spots affect the quantity and quality of this crop as well as crop growth. Experts use visual observation to detect field pea disease. However, this approach is expensive, labor-intensive, and imprecise. Therefore, in this study, we presented a transfer learning approach for the automatic diagnosis of field pea leaf diseases. We classified three field pea leaf diseases: Ascochyta blight, leaf spot, and powdery mildew. A softmax classifier was used to classify the diseases. A total of 1600 images of both healthy and diseased leaves were used to train, validate, and test the pretrained models. According to the experimental results, DenseNet121 achieved 99.73% training accuracy, 99.16% validation accuracy, and 98.33% testing accuracy after 100 epochs. we expect that this research work will offer various benefits for farmers and farm experts. It reduced the cost and time needed for the detection and classification of field pea leaf disease. Thus, a fast, automated, less costly, and accurate detection method is necessary to overcome the detection problem.

Wireless sensor network nodes are small, non-rechargeable devices powered by batteries with limited energy supplies. When using multi-hop communication, data is sent over numerous hops before reaching the cluster head that is farthest from the base station. To minimize power consumption and maximize network lifetimes, this study integrates Multi-Hop communication into the Centralized Energy Efficient Clustering Routing Protocol to determine the optimal path for inter-cluster multi-hop communication between a source (cluster head) and a destination (base station). According to the simulation findings, the suggested algorithm outperforms both the Multi-Hop Technique for the Improvement of Low-Energy Adaptive Clustering Hierarchy Protocol and Energy Efficient Multi-Hop Routing using Genetic Algorithm. The proposed algorithm performs somewhat better than both Energy Efficient Multi-Hop Routing using Genetic Algorithm and Multi-Hop Technique for the Improvement of Low-Energy Adaptive Clustering Hierarchy Protocol in terms of normalized average energy consumption, network lifetime, and packet delivery ratio. The suggested approach is assessed using a MATLAB simulator and contrasted with other similar approaches. Simulations were conducted in a 300 × 300 m² area with 200 nodes, a base station at (100,100), a round length of 500 s, and an initial node energy of 100 J. Key energy parameters include ETX = ERX = 30 nJ/bit, Efs = 10 nJ/bit/m², and Emp = 30 nJ/bit/m⁴. In comparison with Multi-Hop Transmission, the simulation results demonstrated that Energy-Efficient Hierarchical Multi-Hop Routing using Genetic Algorithm improved normalized average energy usage by an average of 5.5 percent and 2.5 percent, packet delivery ratio by an average of 4.1 percent and 2.5 percent, and 3.5 percent and 4.2 percent, respectively.