Jul 2026· Best Journal of Innovation in Science, Research and Development· 0 citations· 11 references
TL;DR
An intelligent deep learning framework is proposed for the automated prediction of diseases in cotton plants using leaf images, which has significant potential for real-time deployment in precision agriculture sys-tems, enabling farmers to take timely dis-ease management actions and improve crop productivity.
Abstract
: Cotton is one of the most economically im-portant fiber crops worldwide, but its productivity is significantly affected by var-ious foliar diseases that reduce both yield and quality. Early and accurate detection of these diseases remains a major chal-lenge in traditional agriculture due to reli-ance on manual inspection, which is time-consuming and prone to human error. In this study, an intelligent deep learning framework is proposed for the automated prediction of diseases in cotton plants using leaf images. The framework leverages ad-vanced image processing techniques and Convolutional Neural Networks (CNNs), along with transfer learning models, to classify healthy and diseased leaves with high accuracy. The system incorporates image preprocessing, data augmentation, and feature extraction to enhance model performance and generalization. Experi-mental results demonstrate that the pro-posed approach achieves superior accura-cy, precision, and robustness compared to conventional methods. The developed mod-el has significant potential for real-time deployment in precision agriculture sys-tems, enabling farmers to take timely dis-ease management actions and improve crop productivity.
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.
M. Naeem, Muhammad Ibrahim, N. Sarwar et al.· Scientific Reports· 0 citations
Agriculture plays a great role in ensuring food security in the world but there are serious threats in rice production due to different types of bacterial and fungal diseases. Manual diagnosis is subjective and labor intensive and may be delayed. The proposed paper suggests a multi-class identification of ten different pathologies related to rice leaves through a multi-class Artificial Intelligence framework using the dataset of 15,023 images. Three methodological paradigms that use mobile net version two with XGBoost, a custom Convolutional Neural Network (CNN), and a novel Hybrid CNN-LSTM model that views patterns of diseases as feature sequences are benchmarked. Whereas the XGBoost classifier has the highest accuracy of 91.75 percent, the deep learning models have high feature extraction capacity. The proposed Ensemble model that incorporates both spatial and sequential learning achieves 97 percent accuracy. This paper supports the hypothesis that deep learning hybrid models have a great impact on diagnostic accuracy, which can be successfully used as an automated instrument to provide disease control in precision agriculture.
Ejamandla Anuradha, Vijaya Chandra Jadala, P. Ramanjineyul· 2026 International Conferenc...· 0 citations
Rice productivity is significantly affected by leaf diseases that reduce crop yield and quality. Conventional disease identification methods rely on manual observation, which is often time-consuming, subjective, and prone to misclassification due to similarities in visual symptoms. This study proposes an automated image-based classification system to detect rice leaf diseases accurately and efficiently. The system utilizes a deep learning model based on convolutional neural networks to classify rice leaf images into three disease categories: neck blast, leaf blight, and rice hispa. A dataset consisting of 3,631 images was used, with 80% allocated for training, 10% for validation, and 10% for testing. Image preprocessing techniques, including resizing, normalization, and augmentation, were applied to improve model performance and generalization. The experimental results show that the proposed model achieved a testing accuracy of 97.80%, with high precision, recall, and F1-score across all classes. The trained model was then deployed into a web-based system that enables users to upload images and obtain real-time classification results. The findings demonstrate that the proposed system provides a reliable and practical solution for early disease detection, supporting precision agriculture and improving decision-making for farmers. The system also offers potential for further development into mobile and integrated smart farming platforms.
Dian Widiarti, Olabode D. Ibini· JOKI: Journal of Computing a...· 0 citations
Rising temperatures and changing weather conditions are accelerating the spread of plant diseases and increasing the threat to global food security. Reliable detection of leaf diseases is therefore essential to protect crop yields and ensure food quality. Deep learning has proven to be a powerful tool for classifying leaf diseases across various crops. Due to the natural variability of plants, plant diseases often appear in irregular structures. Surface unevenness, folds, or dirt particles are common in field images and can be mistakenly identified as important features by convolutional neural networks (CNNs). This is a challenge that has not been sufficiently addressed in previous studies. This study proposes a novel deep learning approach that takes into account both the specific visual characteristics of plant diseases and potential disturbances in the microstructure, such as surface irregularities or prominent leaf veins, which may mislead the model. Using stratified five-fold cross-validation on a peer-reviewed dataset, which comprises 2,801 images of radish leaves across five classes (healthy, three disease classes: mosaic virus, black leaf spot, and downy mildew, and one pest-affected class: flea beetle), the proposed method achieved an average and balanced accuracy of 99.86%, establishing a new dataset-level benchmark in the field and demonstrating its effectiveness. The results indicate that the proposed approach may provide a promising basis for future applications in agricultural field monitoring, automated sorting and post-harvest quality control, offering potential to reduce both food waste and associated costs.
J. Hoffmann, Christopher Mai, Ricardo Buettner· PLoS ONE· 0 citations
Background: The necessity for effective and precise disease detection techniques is highlighted by the rising demand for legumes. Convolutional Neural Networks (CNNs), a type of deep learning, provide a potent way to diagnose plant diseases. CNNs make it possible to accurately identify illnesses in real time by quickly evaluating enormous amounts of plant pictures. By giving farmers proactive tools for monitoring crop health, cutting losses and enhancing food quality, automated detection systems can improve agricultural practices.
Methods: To categorize bean leaves, this study suggests a deep learning-based method utilizing the ResNet-20 model. To increase model generalization and lessen overfitting, data augmentation techniques such as scaling, rotation and flipping were employed. The model was trained on a dataset of labelled images and its performance was assessed using categorization metrics, confusion matrix, ROC curve and Matthews Correlation Coefficient.
Result: The ResNet-20 model’s test accuracy was 76.15%. Additional performance indicators such as metrices demonstrated the model’s reliable classification abilities. The ROC curve further illustrated the model’s exceptional ability to differentiate between healthy and unhealthy leaves.
B. D. Patil, Geetika Parmar, Manisha Shinde-Pawar et al.· Indian Journal of Agricultur...· 0 citations
Early detection of plant leaf diseases is beneficial as it helps agriculturists to apply remedial measures well in advance. This is highly recommended for a good yield from crops, which enhances the economy of an agriculture-based country. Computer vision and deep learning techniques, used in the field of precision agriculture, facilitate early detection and classification of plant leaf diseases. The literature proclaims that deep learning models outperform machine learning approaches for the classification of leaf diseases. In this paper, the state-of-the-art deep learning methods for detection and classification are applied on banana leaf dataset. The AlexNet, VGG19, DenseNet201, ResNet50, and MobileNetV2 convolutional neural networks are the models compared in this paper. The real-time images of banana leaves are used to train and test these models using Python programming. Healthy and two common diseases of banana leaves, namely Leafspot and Sigatoka, are classified in this work. After data augmentation and preprocessing, all the models could achieve good testing accuracies of more than 90.6% in the 80 of training, 10% of validation, and 10% of testing sets. ResNet50 deep technique outperforms the other architectures in 80% of the training set. The training and testing accuracies depend on the data augmentation and image pre-processing techniques.
N. Vidhya, R. Priya· AI Computer Science and Robo...· 0 citations