Utility of AI Based Soil Testing in Agriculture
Dr. Anirban Mandal1Swarup Samanta2Arka Karmakar3 Suvrodeep Debnath4 Suresh Kumar Shaw5
1 Associate Professor, Department of Electronics and Communications Engineering, Future Institute of Engineering and Management, Kolkata-150 (Ph-9064900298 Email:anirban.mandal@teamfuture.in)
2,3,4 4th yr student, Department of Electronics and Communications Engineering, Future Institute of Engineering and Management, Kolkata – 150 (Ph – 7439613752, 9330886039, 9874774674, 9330490254) (Email: samanta.rup21@gmail.com, arkakarmakar92@gmail.com, suvrodeepdebnath.official@gmail.com, suresh.kr.shaw.fiem.ece20@teamfuture.in )
ABSTRACT The soil plays crucial role in any kind of agricultural crops. Depending upon the different ingredients of the soil the nature of crops to be farmed is decided.Optimizing agricultural practices is crucial for countries like India to address economic growth and escalating food demands. Historical challenges stemming from diverse weather conditions, geographical variations, conventional farming methods, and economic uncertainties have hindered improved crop yields. The dearth of information on crop yield productivity has also resulted in significant economic losses. In response, the adoption of advanced agricultural technologies such as smart farming, digital agriculture, and Data Analytics has become imperative. These innovations provide valuable insights into factors influencing crop yields, best-fit crops for a certain area, and enabling accurate predictions. With precise crop yield forecasts, farmers can develop tailored cultivation plans, implement efficient crop health monitoring systems, and manage yields effectively, thereby transforming agriculture into a highly profitable venture. This paper delves into the applications of technological advancements in agriculture, focusing on areas such as Digital Agriculture, Crop Management, and Crop Protection, and Data Analytics, with the aim of contributing to the optimization of agricultural practices and bolstering the economic impact of the agricultural sector.
KEYWORDS: Agriculture, Artificial Intelligence, Machine learning, crop prediction
Doi Link – https://doi.org/10.69758/GIMRJ2407IIIIV12P0001
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