Plant Disease Detection Using Machine Learning

Agriculture plays a crucial role in the Indian economy.Early detection of plant diseases is very much essential to prevent crop loss and further spread of diseases.Most plants such as apple, tomato, cherry, grapes show visible symptoms of the disease on the leaf.These visible patterns can be identified to correctly predict the disease and take early actions to prevent it.This can be overcome by the use of machine learning and deep learning algorithms.Hence, we are proposing a method that which is detecting the disease of a tomato plant from their leaf images.Here the process is performed with the Support Vector Machine (SVM), Random Forest algorithms of machine learning along with deep learning algorithms Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and ResNet, which is a one of the transfer learning method of CNN.Once after training the dataset with the algorithms, the accuracy of algorithms is compared and the images are classified.And the precautions are also provided for the classified plant.Early identification of plant diseases is crucial for sustainable agriculture.This study presents an innovative machine learning framework for automated plant disease detection using convolutional neural networks (CNNs).The system integrates image segmentation techniques with deep learning algorithms to extract distinctive disease features while minimizing false positives.Results demonstrate superior performance compared to traditional detection methods, with significantly reduced diagnostic time.