A transfer learning-based model, CLDP-CNN, designed to enhance feature extraction and classification efficiency using pre-trained deep neural networks is proposed and optimized, utilizing Transfer Learning (TL) which operates on meticulously prepared datasets.
Abstract
Cotton leaf diseases present a major threat to global cotton production, significantly impacting both yield and fiber quality. Traditional diagnostic methods are labor-intensive, time-consuming, and demand highly skilled professionals, making them inefficient for large-scale agricultural applications. Although earlier deep learning -based approaches have shown promising results in identifying cotton leaf diseases such as Bacterial Blight, Fusarium Wilt, and Curl Virus Disease, their performance is often limited by complex preprocessing requirements and insufficient generalization to real-world field conditions. To address these challenges, this study proposes and optimized transfer learning-based model, CLDP-CNN, designed to enhance feature extraction and classification efficiency using pre-trained deep neural networks. This study demonstrates the development of Cotton Leaf Disease Prediction Convolutional Neural Network (CLDP-CNN) automatically, utilizing Transfer Learning (TL) which operates on meticulously prepared datasets. Two distinct datasets were used to train the model: the first consisted of field images from cotton farms, while the second was sourced from Kaggle. The main goal of this research examines how the model performs on real-world field datasets. The CLDP-CNN model has proven highly accurate by attaining 99.78% detection success rates for cotton leaf diseases when processing primary dataset which surpasses its secondary dataset accuracy rate of 99.62%. Both the primary dataset and secondary dataset resulted in high accuracy values for the VGG16 pre-trained model which achieved 99.56% accuracy on the primary dataset and 98.82% on the secondary dataset. A web-based application enhances the capabilities of the CLDP-CNN model by providing real-time updates on the health status of cotton plants. This technology empowers farmers with valuable information, enabling them to take timely protective actions to prevent potential severe yield losses in their cotton crops.
The proposed automated leaf disease detection system using image processing and deep learning techniques can detect leaf diseases effectively and efficiently, making it a useful and cost-effective solution for supporting farmers and agricultural experts in early disease diagnosis.
Shilpa T S, K. U, A. Jajur. J· World Journal of Advanced En...· 0 citations
The proposed CNN framework provides a scalable, computationally efficient, and intelligent solution for automated cotton leaf disease classification, contributing to the advancement of AI-driven precision agriculture and sustainable crop management.
Sonali Kamra, Vijay Laxmi· International Journal of Res...· 0 citations
With the global prevalence of tomato diseases causing 20 to 40% annual crop losses and over USD 220 billion in economic damage, traditional manual scouting and laboratory diagnostics prove labor intensive, subjective, delayed, and impractical for resource constrained rural farmers. To address this challenge, this study proposes a lightweight 17 layer convolutional neural network (CNN) model enhanced by comprehensive data augmentation, effectively classifying nine prevalent tomato leaf diseases Bacterial Spot, Early Blight, Late Blight, Leaf Mold, Septoria Leaf Spot, Spider Mites, Target Spot, Yellow Leaf Curl Virus, and Mosaic Virus using the PlantVillage dataset of 16,012 images. The experiment utilized 80/20 train test splits with Adam optimizer (learning rate 0.001), categorical cross entropy loss, 50 epochs, and batch size 32. The proposed CNN was compared with pretrained InceptionV3 and ResNet152V2 baselines. Experimental results demonstrate the model achieves state of the art performance with 95.28% test accuracy, 97.80% training accuracy, 0.970 macro F1 score, 0.932 micro MCC, and 0.983 micro average AUC, outperforming InceptionV3 (81.54%) and ResNet152V2 (85.89%) by 13.74% and 9.39% respectively, while surpassing tomato specific SOTA VGG 19 (93%). Ablation experiments confirm augmentation yields 16.68% accuracy improvement over non augmented baselines. The model powers a React Native Android app with TensorFlow Lite INT8 quantization (7.1 MB), delivering sub 200 ms inference for online cloud analysis via FastAPI and offline edge computing, providing farmers real time diagnostics with robust generalization across diverse field conditions and significant practical value for precision agriculture and food security.
D. M. Balungu, Maksim Aleksandrovich Malykh, Dmitry Evgenievich Burdin et al.· Informatica· 0 citations
Experimental data show that the proposed EfficientNetB0V2 + CNN model achieves superior performance, with higher accuracy and better precision, recall and F1 Score across all classes, highlighting the effectiveness of the suggested approach in detecting complex disease patterns.
Nisha Rani· International journal of com...· 0 citations
Rapid and accurate identification of cotton foliar diseases which seriously threatens cotton output worldwide is of great importance. A strong deep learning model with custom architecture DenseNet169 is proposed in this research for automatic classification of seven diseases of cotton leaf: Bacterial Blight, Curl Virus, healthy leaf, herbicide growth damage, leaf hopper jassids, leaf redding and leaf variegation. We proposed a two-step transfer learning method with enhanced data augmentation based on the SAR-CLD–2024 dataset, which contains 9,137 images. The DenseNet169 architecture proposed here yielded a remarkable performance with a validation accuracy of 96.83% while precision, recall, and F1-score were 96.89%, 96.93%, and 96.90%, respectively, with a significant enhancement than prior related approaches. It gets flawless classification for Herbicide Growth Damage and close to perfect for each disease types with macro-average AUC of 99.81%. The second is the parameters of the deep architecture that we adapted for agricultural pathology where we were broadly successful at systematic feature extraction and then fine-tuning 161 layers. The new high-water mark physiological plant disease diagnosis that we establish here is an important step toward ultimately real-world applicability as adaptive components of precision agriculture to monitor crop health and protect yield.
Rebally.Vijay Kumar, G. Thirupati· International Journal of Sci...· 0 citations
Plant leaf diseases significantly reduce agricultural productivity and crop yield worldwide, making early and accurate detection essential to prevent large-scale crop damage. Traditional disease identification methods rely on manual inspection by experts, which is time-consuming, costly, and often inaccessible to farmers in rural areas. This paper proposes an AI-based leaf disease detection system using deep learning and transfer learning, in which EfficientNetB5 serves as a pretrained feature extractor to classify 38 plant disease categories spanning 14 crop species. Preprocessing includes HSV-based leaf segmentation, resizing to 456×456 pixels, and EfficientNet-specific normalization. A compact two-layer dense classifier is trained on the 2,048-dimensional feature vectors produced by the frozen backbone. The system achieves an overall validation accuracy of 96.49%, macro-average precision of 0.97, recall of 0.96, and F1-score of 0.96 on 2,280 held-out images. Beyond classification, the system provides actionable cure and precautionary recommendations for every detected disease, making it directly useful to smallholder farmers. Comparative analysis with ResNet50, VGG16, and MobileNetV2 confirms that EfficientNetB5 achieves the highest accuracy with a favorable parameter-to-performance ratio. Multi-class ROC evaluation further demonstrates strong discriminative capability across all disease categories.
Kuppala Ajay Kumar, Yella Sai Krishna, R. Kumar et al.· 2026 6th International Confe...· 0 citations
