• Shubham Sakhare
• Shruti Lamkhade
• Shubham Garad
• Shridhar Shinde
• H. R. Kulkarni, Vaijanta Bhogawade

Organic and herbal farming plays a crucial role in promoting sustainable agriculture, yet traditional methods often face challenges such as low productivity, inefficient resource use, and limited data-driven decision support. Recent studies demonstrate the potential of Machine Learning (ML) and Artificial Intelligence (AI) in addressing these issues through UAV imagery, IoT sensors, and advanced algorithms. This paper reviews existing approaches, identifies research gaps in herbal-focused applications, and proposes a hybrid ML–DL framework for crop suitability assessment, yield prediction, and disease detection. The framework integrates UAV and IoT data with supervised and deep learning models, offering scalable solutions for smallholder farms. Results from literature highlight classification accuracies ranging from 60–98%, indicating strong potential for improved decision support in organic and herbal farming. The study concludes that adopting ML-driven models can significantly enhance sustainability, precision, and efficiency in organic and herbal agriculture.

Machine Learning in Agriculture,Organic and Herbal Farming,UAV and IoT Integration,Crop Suitability Prediction,Precision Agriculture,Deep Learning Applications.

"A Review on Role of Machine Learning In Supporting Organic and Herbal Farming Practices", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 1, page no.205-217, January-2026, Available :http://www.jetir.org/papers/JETIRHG06022.pdf

"A Review on Role of Machine Learning In Supporting Organic and Herbal Farming Practices", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 1, page no. pp205-217, January-2026, Available at : http://www.jetir.org/papers/JETIRHG06022.pdf