• MIRIYALA. BABYVIDYASRI.
• K. LAKSHMANA REDDY

Agricultural monitoring, particularly in developing countries, can aid in the prevention of famine and the support of humanitarian efforts. One major challenge is yield estimation, which is the prediction of crop yields prior to harvesting. Using publicly available remote sensing data, we present a scalable, accurate, and low-cost method for predicting crop yields. This solution, if implemented at the government established soil health centers, could assist all farmers in using minimal fertilizers in order to maintain soil health while also providing them with the opportunity to gain the most revenue from the same piece of land. As a result, it would be a win-win situation for all parties involved. This is accomplished through the use of technologies such as Machine Learning and Image Processing.

CROP YIELD PREDICTION SYSTEM USING MACHINELEARNING

"CROP YIELD PREDICTION SYSTEM USING MACHINELEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 8, page no.b840-b846, August-2021, Available :http://www.jetir.org/papers/JETIR2108235.pdf

"CROP YIELD PREDICTION SYSTEM USING MACHINELEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 8, page no. ppb840-b846, August-2021, Available at : http://www.jetir.org/papers/JETIR2108235.pdf