ML Approaches for Real-Time Cotton Leaf Disease Detection and ‎Severity Prediction Through Multi-Modal Data Integration and ‎Transfer Learning - DhanaLakshmi, M.; Sahoo, Bidush Kumar; Ganiya, Rajendra Kumar (International Journal of Basic and Applied Sciences, 2025)
This research mainly focused on with the exploration aming deep learning and utilized ‎through a CNN model, Transfer learning, and integrated with multi-model identification of plant ‎diseases. The main objective is to identify the agronomic flaws in conventional farming ‎practices that led to wasteful and unproductive approaches, including the failure to diagnose ‎malaria, a major health risk that reduced agricultural yields. Using the Plant Village dataset, ‎which includes 54,000 images of plant leaves annotated with disease kind, we were able to ‎construct a better field readiness model by identifying 38 different illnesses using a subset of ‎these photos that provides around 30,000 normal and diseased leaves. We tried a number of ‎different ways to add to the data so that performance would go up and overfitting would be ‎avoided. Accuracy, precision, and recall for illness classes are all above average in our CNN-based model, which achieves 94.9% on average. The confusion matrix revealed only a few ‎misclassified photos, with the best accuracy for healthy leaves (98.0%), followed by "Tomato ‎Early Blight" (94.9%) and "Potato Late Blight" (96.2%). Refined a pretrained EfficientNet ‎model to achieve 96.5% accuracy. Madonna and others. This model can also train up to 25% ‎faster than other designs like ResNet, which makes it good for real-time mobile and edge ‎applications. When the environmental data (temperature, humidity) and pictures of leaves were ‎put together, the accuracy was similarly 97.1%. These approaches worked well together to find ‎illness heterogeneity caused by the environment. This work demonstrates that deep learning ‎‎(DL) may be efficiently employed for early disease detection in plantations, a fundamental ‎aspect of precision agriculture‎.
Keywords: Plant Disease Detection; Convolutional Neural Networks (CNNs); DL In Agriculture; Transfer ‎Learning; Multi-Modal Data Integration; Precision Agriculture; Crop Disease Classification; ‎Image-Based Disease Diagnosis; Data Augmentation; Real-Time Disease Monitoring