• Megha Mohan
• Dr. Sushopti D.Gawade

Many people in India depend on agriculture for daily livelihood. But farmers suffer a lot of problems during cultivation of crops. One of the main problems is crop stress. Crop stress destroys the crop productivity and yield. Early detection of crop stress will be very helpful for the farmers. Machine learning and deep learning can contribute a lot in this arena. This project attempts to create a system to diagnose the crop stress. The system collects data as images. These are images of stressed grape crops and collected in an image dataset. Different machine learning’s deep learning techniques are used for classification. Classification models like LSTM, CNN and SIFT with LSTM are used here for crop stress diagnosis. In this system users can select any classification model to predict the crop stress. After processing respective models diagnosis is communicated by the system which is very essential for the farmers to improve farming which increases crop yield. The system identifies the type of crop stress affected and also identifies the name of crop stress. After identifying stress, the system gives the proper solution to the crop stress affected.

"Design and Development of Crop Stress Diagnosis System Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 7, page no.c786-c789, July-2022, Available :http://www.jetir.org/papers/JETIR2207299.pdf

"Design and Development of Crop Stress Diagnosis System Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 7, page no. ppc786-c789, July-2022, Available at : http://www.jetir.org/papers/JETIR2207299.pdf