Grapes are among the most economically and culturally significant fruits on a global scale, and table grapes and wine are produced in significant quantities in Europe and Asia. The production and quality of grapes are significantly impacted by grape diseases such as Bacterial Rot, Downy Mildew, and Powdery Mildew. Consequently, the sustainable management of a vineyard necessitates the early and precise identification of these diseases. Current automated methods, particularly those that are based on the YOLO framework, are often computationally costly and lack interpretability that makes them unsuitable for real-world scenarios. This study proposes grape leaf disease classification using Optimized DenseNet 121. Domain-specific preprocessing and extensive connectivity reveal disease-relevant characteristics, including veins, edges, and lesions. An extensive comparison with baseline CNN models, including ResNet18, VGG16, AlexNet, and SqueezeNet, demonstrates that the proposed model exhibits superior performance. It achieves an accuracy of 99.27%, an F1 score of 99.28%, a specificity of 99.71%, and a Kappa of 98.86%, with an inference time of 9 seconds. The cross-validation findings show a mean accuracy of 99.12%, indicating strength and generalizability across all classes. We also employ Grad-CAM to highlight disease-related regions to guarantee the model is highlighting physiologically relevant aspects and increase transparency and confidence. Model optimization reduces processing requirements for real-time deployment, while transfer learning ensures consistency on smaller and unbalanced samples. An effective architecture, domain-specific preprocessing, and interpretable outputs make the proposed framework scalable, precise, and computationally inexpensive for detecting grape leaf diseases.
Improving Fine-Grained Rice Leaf Disease Detection via Angular-Compactness Dual Loss Learning
Begum Rokeya University, Rangpur（贝古姆·罗基亚大学，朗布尔）；Wilmington University（威尔明顿大学）；Washington University of Science and Technology（华盛顿科学与技术大学）；Central Michigan University（中密歇根大学）
Analysis of Convolutional Neural Network-based Image Classifications: A Multi-Featured Application for Rice Leaf Disease Prediction and Recommendations for Farmers
Nepal Engineering College（尼泊尔工程学院）；Pokhara University（博卡拉大学）；Tribhuvan University（特里布万大学）；Institute of Engineering（工程学院）
Artificial Intelligence Lab, Department of Computer Systems Engineering, University of Engineering and Applied Sciences (UEAS), Swat（斯瓦特工程与应用科学大学计算机系统工程系人工智能实验室）；Interdisciplinary Research Center for Smart Mobility and Logistics (IRC-SML), King Fahd University of Petroleum and Minerals (KFUPM), Dhahran（法赫德国王石油矿产大学智能移动与物流跨学科研究中心）
Involution-Infused DenseNet with Two-Step Compression for Resource-Efficient Plant Disease Classification
Department of Computer Science and Engineering, School of Data and Sciences, BRAC University（计算机科学与工程系，数据与科学学院，BRAC大学）；Computing for Sustainability and Social Good (C2SG) Research Group, Department of Computer Science and Engineering, United International University（可持续性与社会公益（C2SG）研究组，计算机科学与工程系，联合国际大学）
SugarcaneNet: An Optimized Ensemble of LASSO-Regularized Pre-trained Models for Accurate Disease Classification
Dhaka University of Engineering & Technology（达卡工程技术大学）；Jashore University of Science and Technology（杰索尔科技大学）；International Islamic University Chittagong（吉大港国际伊斯兰大学）；Zarqa University（扎尔卡大学）；Northumbria University（诺森比亚大学）；Qassim University（卡西姆大学）
Real-time Plant Health Assessment Via Implementing Cloud-based Scalable Transfer Learning On AWS DeepLens
The Institute for Sustainable Industries and Liveable Cities (ISILC), Victoria University（维多利亚大学可持续产业与宜居城市研究所（ISILC））；Namal Institute（纳马尔学院）；Charles Sturt University（查尔斯特大学）；Victoria University（维多利亚大学）
