Abhinav Sharma|Arpit Jain|Prateek Gupta|Vinay Chowdary
Machine Learning Applications for Precision Agriculture: A Comprehensive Review
agriculture plays vital role economic growth countrywith increase population frequent changes climatic conditions limited resources becomes challenging task fulfil food requirement present populationprecision agriculture also known smart farming emerged innovative tool address current challenges agricultural sustainabilitythe mechanism drives cutting edge technology machine learning mlit gives machine ability learn without explicitly programmedml together iot internet things enabled farm machinery key components next agriculture revolutionin article authors present systematic review ml applications field agriculturethe areas focused prediction soil parameters organic carbon moisture content crop yield prediction disease weed detection crops species detectionml computer vision reviewed classification different set crop images order monitor crop quality yield assessmentthis approach integrated enhanced livestock production predicting fertility patterns diagnosing eating disorders cattle behaviour based ml models using data collected collar sensors etc intelligent irrigation includes drip irrigation intelligent harvesting techniques also reviewed reduces human labour great extentthis article demonstrates knowledgebased agriculture improve sustainable productivity quality gep gene expressions programming rcnn regionscnn ga genetic algorithm pso particle swarm optimization plsr partial least square regression anfis