Enhancing Precision Agriculture: Deep Learning for Rice Leaf Disease Identification

Although rice is a major crop in many countries, its stability depends on the ongoing health test to ensure food supply and develop permanent growing practices. Economic security and higher agricultural output are the outcomes of early disease detection of rice plant leaves. A deep learning architecture that combines the extractive and computationally efficient MobileNet network with DenseNet, EfficientNetV2 and ResNet50 networks for accurate classification aids in the diagnosis of rice leaf disease. Advanced enrichment metal pictures that undergo preprocessing with contrasting growth and noise reduction are essential for increased resistance of the model. Action may be taken by farmers before the diseases deteriorate thanks to the broad detection of the diseases of the plant, including a bacterial blight paired with explosion and brown spot. The performance assessment compares various architectural effectiveness using tough criteria, F1-score, accurate, recall and accuracy. The integration of deep learning technology brings major plant pathology improvements which allow sustainable practices and yield improvement and secure the food supply against disease threats.