Agrolife: Machine Learning Based Plant Disease Classification System

Agriculture is very important in developing countries like India, but there are still serious issues with food security. Plant diseases are the primary cause of squandered crops and their early detection is crucial since they have an impact on how much food is produced. In-depth explanations of how Deep Learning models are used to visualize various plant ailments are given in this article, along with suggestions for effective solutions. Our goal is to determine whether the plant is sick or not and if it is, to identify the type of illness that caused it. Along with this, the system will give a general overview of the illness, suggest a few straightforward home cures (if necessary), and direct the user toward getting the appropriate consultation. Convolutional Neural Network (CNN), a deep learning model is employed to recognize and categorize plant leaf diseases, to display the type of ailment as well as potential treatments after receiving input from the user in the form of picture data. Other programs utilized include Google Cloud/ AWS Cloud Server, and also TensorFlow which will be used for data preprocessing. Applying the CNN algorithm, it can be seen that an average classification accuracy of 97% can be obtained to classify the plant leaf diseases. Also, by utilizing machine learning algorithms, the system facilitates farmers in identifying the optimal crop for their farmland based on inputs such as soil type and other relevant factors, as well as recommending suitable fertilizers. This would ultimately enhance productivity and diminish crop losses.