A REVIEW ON PLANT DISEASES CLASSIFICATION USING DEEP LEARNING

Deep learning’s incorporation into the classification of plant diseases is a paradigm-shifting advancement in agricultural practices with far-reaching ramifications for the safety of the world’s food supply. This investigation has brought to light the enormous difficulties that plant diseases present, underlining the crucial importance of early identification and accurate classification for disease management. The scalability and accuracy of traditional approaches, which rely on human judgment and subjective symptom interpretation, have been found to be lacking. Particularly, convolutional neural networks (CNNs) have emerged as game-changing tools capable of automatically understanding subtle patterns from massive plant picture collections and offering quick, scalable, and accurate disease identification solutions. Transfer learning solves data shortages and significantly improves classification accuracy by using pre-trained networks and fine-tuning them with plant disease datasets. By combining predictions from several models, lowering the likelihood of overfitting, and increasing overall accuracy, ensemble approaches like bagging and boosting improve classification resilience. Models are better able to respond to changes in image quality and lighting conditions thanks to the addition of synthetic plant images to the training dataset via Generative Adversarial Networks (GANs).On-field systems, mobile apps, remote sensing, and precision agriculture are a few examples of useful deep learning applications in plant pathology that have the potential to transform how we identify and treat plant diseases. Although there are still issues with model interpretability and data scarcity for rare diseases, deep learning’s potential for classifying plant diseases is quite promising. This field supports more productive and efficient agricultural techniques in addition to better disease control, bringing us one step closer to a time when there will be more food security and less hunger throughout the world.