Machine learning and IoT-based smart farming for enhancing the crop yield

Abstract

The lack of favourable atmospheric conditions leads to the loss of many crops each year. In India alone, over 11 billion dollars are lost. By combining IoT and machine learning technologies, this team has created a system that integrates agriculture’s three primary operations: crop selection, autonomous watering, and fertiliser suggestion. The following crops—Apple, Rice, Maize, Grape, Banana, Orange, Cotton, and Coffee—were considered in the study. Three systems are covered in the paper: The crop recommendation system employs machine learning to examine factors including nitrogen (N), phosphorous (P), potassium (K), pH, and weather before recommending a crop. The crop type and the current levels of soil nutrients are the two main determinants on which the fertiliser recommendation method bases its recommendation. When employing an automatic irrigation system, the crop is irrigated automatically while taking current soil moisture levels and weather forecasts into consideration. This paper attempted to implement the mentioned systems. The paper discusses the successes of the crop recommendation system, the automatic watering system, and the fertiliser recommendation system. In this paper, we report the results of simulations of the mentioned systems.

Smart irrigation with crop recommendation using machine learning approach

“…ACRIS module is another crop recommender system implemented [23]. Sundaresan et al [24] considered some crops like bananas, oranges, cotton, coffee, and many more for the recommendation system. Thangavel et al [25] identified agriculture machinery usage by districts around South Tamil Nadu to predict suitable machinery for that area.…”