An Empirical Analysis of Agricultural Machine Learning - Techniques and Applications

  • V.P. Gladis Pushparathi, Auxilia Osvinnancy.V


Agricultural machine learning is a well established framework that collects specific data and use computational models to achieve predicted results. They are being used to forecast agricultural productivity as well as livestock farming in agriculture. Every year, farmers make thousands of complicated and interlinked choices that affect their risk, productivity, and sector returns. Conventionally, a variety of variables, namely crop parameters, soil type, environmental issues, and more, could not be applicable to farming strategies for rendering nuanced decisions based on interdependencies. However, agricultural machine learning allows much more precision, allowing farmers to handle plants and animals almost individually, which greatly raises the efficacy of farmers' choices considerably. Recently machine learning has been used as a new paradigm for designing and optimizing high-intelligence smart farming. Therefore, initiatives from both industry and academia on agricultural machine learning have already been placed in the introduction. With the realistic account of all challenges and opportunities of machine learning from engineering perspectives, requires a complete study of available agricultural background. In this chapter, huge potential efforts are made to study the role of machine learning in agriculture with the detail of how modern agriculture differs in traditional farming. In the end, this chapter also highlights various applications for agricultural machine learning.

Keywords- Agriculture, Artificial Intelligence, Crop Prediction, Machine Learning, Smart Farming.