Plant disease classification using deep learning approaches

Abstract

This thesis extensively evaluates deep learning models for classifying plant diseases and their effects on agriculture, society, and the environment. Basic CNN, Modified AlexNet, EfficientNet B0, and EfficientNet B4 are compared for their ability to diagnose and treat agricultural diseases. The models were very accurate, with the EfficientNet B4 model scoring 99.99%. The Modified AlexNet and EfficientNet B0 models followed with 99.6% accuracy. CNN's 99.5% accuracy was impressive. The models also had modest loss values, suggesting good learning and training. With its accuracy, recall, and F1-score measures, EfficientNet B4 reliably classified all disease categories better than any other model. The models' capacity to transform agriculture, enhance crop management, and secure global food security has a major influence on society. The research also examines how deep learning models reduce chemical pesticide consumption and promote sustainable farming. Ethical considerations include data privacy, algorithmic bias, transparency, and responsibility emphasize the need for responsible and fair deployment. A sustainability strategy plans the long-term viability and inclusiveness of agricultural deep learning models. Future research should address dataset limits, examine a variety of data sources, improve model performance, and evaluate ethical issues, according to the thesis. This study highlights the potential of deep learning models in agriculture. It stresses ethical and sustainable methods while using these models to solve social issues and encourage environmental stewardship.