• PARVATHY.S

Today, farmers are suffering from the low yield of crops. Though right crop selection is the main boosting key to maximize crop yield by doing soil analysis and considering metrological factors, the lack of knowledge about soil fertility and crop selection is the main reason for low crop production. In the changed current climate, the farmers having primitive knowledge about conventional farming are facing challenges about making sagacious decisions on crop selection. The selection of the same crop in every seasonal cycle makes the low soil fertility. This study is aimed at making an efficient and accurate system using IoT devices and machine learning (ML) algorithms that can correctly select a crop for maximal yield. Such a system is reliable as compared to the old laboratory testing manual systems, which bear the chances of human errors. Correct selection of a crop is predominantly a priority in agricultural arena. As a contribution, we propose an ML-based model, Agro Harvest (AH), which is a Kerala based crop and yield prediction based on data of metrological and soil factors. These factors include pH, temperature, humidity, and moisture of soil. Existing IoT-based systems are not efficient as compared to our proposed model due to limited consideration of these factors.

"AGROHARVEST IOT BASED CROP AND YIELD PREDICTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.n257-n271, April-2025, Available :http://www.jetir.org/papers/JETIR2504D34.pdf

"AGROHARVEST IOT BASED CROP AND YIELD PREDICTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppn257-n271, April-2025, Available at : http://www.jetir.org/papers/JETIR2504D34.pdf