Machine Learning Applications for Precision Agriculture: A Comprehensive Review

Agriculture plays a vital role in the economic growth of any country. With increase population, frequent changes climatic conditions and limited resources, it becomes challenging task to fulfil food requirement present population. Precision agriculture also known as smart farming have emerged an innovative tool address current challenges agricultural sustainability. The mechanism that drives this cutting edge technology is machine learning (ML). It gives ability learn without being explicitly programmed. ML together with IoT (Internet Things) enabled farm machinery are key components next revolution. In article, authors systematic review applications field agriculture. areas focused prediction soil parameters such organic carbon moisture content, crop yield prediction, disease weed detection crops species detection. computer vision reviewed for classification different set images order monitor quality assessment. This approach can be integrated enhanced livestock production by predicting fertility patterns, diagnosing eating disorders, cattle behaviour based on models using data collected collar sensors, etc. Intelligent irrigation which includes drip intelligent harvesting techniques reduces human labour great extent. article demonstrates how knowledge-based improve sustainable productivity product.