• ch.phanindra
• g nagalakshmi
• j navya
• r vasantha
• a hemalatha

—The effective planning in agriculture requires accurate prediction of the crop yield but, most of the machine learning–based techniques to this effect are only concerned with estimating the crop yield and they do not provide actionable strategies for productivity improvement. This makes them less useful for farmers. To fill this hole in knowledge, a crop yield prediction system that combines the yield forecasting and yield enhancement recommendation is proposed. The proposed method, which is based on machine learning methods like Decision Trees and Gradient Boosting along with the usage of Python and Pandas tries to predict crop yield based parameters such as type of crop, area under cultivation, Season, temperature, rainfall for the given field area in per hectare terms. Beyond the prediction, we make decision-support recommendations that could help boosting yield and profit. It also gives you best crop pairs, such as mirchi and onion which can be intercropped to get maximum benefit from the land and returns. The package also prescribes crop specific organic fertilizers and eco-friendly inputs that include neem oil and neem based formulations as the means for enhancing soil fertility, pest management while promoting sustainable agriculture farming. With predictive analytics and sound agriculturaladvice, the system provides all-around farmercentred functionality, thereby improving productivity and profitability in modern farming.

Crop Yield Prediction, Machine Learning, Decision Trees, Gradient Boosting, Agriculture, Profit Estimation

"Automated Crop Yield Prediction With Yield Enhancement Recommendations Using ML", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 3, page no.c443-c447, March-2026, Available :http://www.jetir.org/papers/JETIR2603258.pdf

"Automated Crop Yield Prediction With Yield Enhancement Recommendations Using ML", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 3, page no. ppc443-c447, March-2026, Available at : http://www.jetir.org/papers/JETIR2603258.pdf