Precision Agriculture Using Machine learning techniques to Increase the Yield

Abstract

To ensure the crops and soil receives ideal health and abundant yield, Precision Agriculture (PA) is one of the approaches in farm management with the use of information technology. Precision Agriculture is also noted as satellite agriculture or site-specific crop management. In this approach the data accessing is real-time which includes crop and soil conditions, ambient air, labour costs, equipment availability, hyper-local weather predictions, etc. This paper scrutinizes the concept of Precision agriculture using machine learning and provides a equivalent inquiry of the various machine learning algorithms used and their reliability. The goal of precision agriculture research is to create a decision support system for entire-farm management by detecting, testing and reacting to outside and within-field changes in crops. Exploration of various types of classification and clustering algorithms that involves prediction agriculture to increase the production is done here. It describes various methods of regressions to get effective precision. The production of crop can also be used to find areas within the farm that are non-profitable for a period of time and it can be aggregated for preserving it with no economic collision for the manufacturer. This system contains the Artificial Neural Network Machine learning algorithm which is used for predicting the data sensed by sensors which helps to improve the detection of diseases and forecast how the disease will unfurl in the crop field. This technique will reduce the cost and chemical wages and it can also be adjusted to different environmental factors. By using the distinct environmental structures farmers can produce the particular crop in certain field at the right time, enhance the productivity to a greater extent.