This study presents a novel CNN for multi-class classification of 38 diseases, demonstrating an effective balance between predictive performance and computational efficiency, positioning the model as a promising tool for real-world agricultural deployment.
Abstract
Plant diseases threaten global agriculture, causing 20–40% yield losses and food insecurity. Current diagnostic methods are costly and lack scalability. While deep learning advances plant disease detection, there remains a need for CNNs with simpler architectures, better generalizability, and lower computational cost. This study presents a novel CNN for multi-class classification of 38 diseases. Trained on a public dataset of over 87,000 RGB images, the architecture comprises five convolutional blocks (filters 32–512) with max pooling and dropout (0.25, 0.4), followed by a 1,500-unit dense layer and SoftMax output. Optimized with Adam (lr=0.0001) and categorical cross-entropy, the model achieved 98% training and 96% validation accuracy with approximately 28.7 million parameters significantly fewer than transfer learning architectures. These results demonstrate an effective balance between predictive performance and computational efficiency, positioning the model as a promising tool for real-world agricultural deployment.
An extensive set of experiments was conducted to evaluate the performance of the proposed model for plant disease detection, and it is demonstrated that the model achieves highly reliable results, with an accuracy of 97.13%.
Hayat Meddeber, M. Meddeber· ITEGAM- Journal of Engineeri...· 0 citations
This study introduces a hybrid deep learning architecture that integrates squeeze-and-excitation residual blocks, capsule networks, bidirectional long short-term memory, and attention mechanisms, enabling farmers to obtain rapid, reliable, and cost-effective field diagnoses, thereby improving agricultural productivity and sustainability.
Aekkarat Suksukont, Ekachai Naowanich· Journal of Advances in Infor...· 0 citations
A hybrid framework integrating a Convolutional Neural Network with a Large Language Model to perform image-based plant leaf disease classification accompanied by interpretable diagnostic explanations demonstrated that combining high-performance visual recognition with language-based reasoning enhanced both diagnostic reliability and interpretability in digital agriculture applications.
Frenky Riski Gilang Pratama, S. Surono, A. Thobirin· International Journal of Adv...· 0 citations
Findings confirm that the hybrid CNN–Transformer architecture effectively enhances classification performance, robustness, and generalization in coffee leaf disease classification, with potential applications in precision agriculture and data-driven crop management.
This study aims to improve the performance of the Convolutional Neural Network (CNN) algorithm in detecting coffee leaf diseases using the Inception V3 architecture. The main challenge of this classification is the subtle visual similarity in color, texture, and symptom patterns between diseases. To overcome this, Inception V3 is implemented because of its superiority in multi-scale feature extraction through convolution factorization which reduces parameters while increasing accuracy. The dataset used consists of 1,120 images, evenly distributed into four classes (three types of diseases and one healthy class, each with 280 images), with a training, validation, and test data split ratio of 896:112:112. As a comparison, a conventional basic CNN architecture consisting of 3 convolution layers (3 X 3, stride 1), 3 max-pooling, and 1 dense layer, trained with the same hyperparameters (Adam optimizer, learning rate 0.001, batch size 32) is used. The experimental results show a significant performance improvement; Model accuracy increased from 74.4% on a standard CNN to 97.0% after integrating Inception V3. The scientific contribution of this research lies in mapping overlapping visual characteristics of coffee diseases through multi-scale feature optimization, which demonstrates that computational efficiency can go hand in hand with accuracy improvements on complex agricultural image datasets. These findings confirm that the Inception V3 architecture provides a robust and efficient solution for automating plant disease diagnosis in the field.
The infection of mango leaves is a major yield and fruit quality loss problem, and the necessity of having a precise and early diagnostic system for sustainable agriculture. Five convolutional neural network (CNN) architectures–namely, VGG16, VGG19, ResNet50, DenseNet121, and a custom-built AlexNet variant–were evaluated for their performance in classifying eight categories of mango leaf diseases. All models were pre-trained using a well-defined dataset of healthy and infected leaves and fine-tuned using transfer learning. VGG16 had the highest test accuracy of 99%, which was better than ResNet50 (91.5%), DenseNet121 (90.5%), VGG19 (87%), and AlexNet (86.5%). Further evaluation of these models using confusion matrices and F1-scores was conducted to ensure the strength of these models for various diseases including powdery mildew, anthracnose, and bacterial canker. Overall, the results underscore that advanced CNN models—particularly VGG16— can deliver near-expert precision in automated mango leaf disease identification. The leaf disease of mango is a major issue confronting farmers in Southeast Asia, particularly in India. Mango leaf disease is one of the biggest challenges faced by the farmers of south-east Asian countries, especially India.
P. G. K., Kiran Kumar H R· 2026 International Conferenc...· 0 citations