An Interactive Interface for Plant Disease Prediction and Remedy Recommendation

Economy and financial status of a country is largely dependent on agriculture. Virtual Assistants (VA) are successfully deployed in different applications, however its use in the farming domain is very limited. There are number of challenges in this like lack of single database which will satisfy the varied needs of farmers like crop prediction, disease management, prediction about proper harvest time, locating nearby godowns, weather forecast etc. Along with this one of the major problems is limited technology literacy of the users poses difficulty in designing and deployment of VA in farming domain. Many times, it is difficult to describe the field conditions verbally. In this case it will be better to accept the photo-based input from the user. The user can pass on additional queries using audio input. By this method the effectivity of VA can be increased. In this work the plant disease prediction is designed with the help of plant leaf images. The system is trained on images taken from PlantVillage Dataset. The system is trained on 61 images of 5 different class. The database contains the images from various crops like maize, strawberry, tomato. 12 different features of diseased segment are used to have the trained model. It is found that the accuracy of SVM classifier is 88.5% and using NN the accuracy is improved to 90.16%. To improve the accuracy further, the image datasize should be increased. Deep learning algorithms like EfficientNet, InceptionNet, ResNet,etc. have better accuracy. In future we plan to integrate this with a system that will generate the audio responses for audio queries raised by farmers.