Plant Disease Detection Using Deep Learning and Machine Learning Algorithms

Understanding the economic sector of India's agricultural productivity relies heavily on the ability to identify diseases in plants. Although numerous diseases pose a significant threat to the food industry, prompt detection remains challenging due to a lack of infrastructure. Categorizing the diseases in plants at an early stage can help in the proper growth and development of the species. The increased use of computers has made deep learning possible and convenient. This research paper explores the use of deep learning algorithms, specifically ResNet model for the automated classification of plant diseases found in the images and many more have been explored to detect the diseases in plants using a dataset of plant leaves with diseases. The goal is to check how well the algorithm works and figure out which one is best for classifying plant diseases. This paper proposes a model which includes image filters and deep learning for plant diseases and it is found that ResNet is a powerful architecture for plant disease classification and achieves 87.5 % accuracy. Approaching the methods of deep learning on images of leaves shows a clear path towards the disease in the plant on a global level.