• 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 find performance of machine learning algorithms over crop stress data sets. 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. This paper makes an attempt to find efficiency of different classification models over crop stress image dataset. Classification models like LSTM, CNN and SIFT with LSTM are developed and respective results are compared with each other on crop dataset. The results are analyzed and the best suitable model is identified according to the accuracy.

"Investigating Efficiency of Machine Learning Algorithm Over Crop Stress Dataset", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 7, page no.c790-c795, July-2022, Available :http://www.jetir.org/papers/JETIR2207300.pdf

"Investigating Efficiency of Machine Learning Algorithm Over Crop Stress Dataset", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 7, page no. ppc790-c795, July-2022, Available at : http://www.jetir.org/papers/JETIR2207300.pdf