Crop Suggestion based on Regional Soil Quality using Machine Learning Techniques

Abstract

Agriculture in India plays a major role in economy and employment. The common difficulties present among the Indian farmers are they don’t opt for the proper crop based on their soil necessities. Because of this productivity is affected. This problem of the farmers has been solved through precision agriculture. This method is characterized by a soil database collected from the farm, crop provided by agricultural experts, achievement of parameters such as soil through soil testing lab dataset. Agribusiness assumes a prevailing job in the development of the nation's economy. Atmosphere and natural changes have become a genuine danger inside the agri-field. Machine Learning ML is a significant methodology for accomplishing reasonable and compelling answers for this disadvantage. Harvest Yield Prediction technique includes foreseeing yield of the harvest from reachable historical and possible data like climate parameter, soil parameter and yield prediction. Real information of the state was utilized for building this model and furthermore the models were tried with tests acquired from the information. The expectation can make the farmer to foresee the yield of harvest before developing into the agribusiness zone. To anticipate the harvest yield in future precisely Random Forest, the most remarkable and popular administered machine learning rule is utilized. With the impact of climate change in India, the majority of the agricultural crops are being badly affected in terms of their performance over a period of last two decades. Predicting the crop yield well ahead of its harvest would help the policy makers and farmers for taking appropriate measures for marketing and storage. Such predictions will also help the associated industries for planning the logistics of their business. Several methods of predicting and modeling crop yields have been developed in the past with varying rates of success, as these don’t take into account characteristics of the weather, and are mostly empirical. This software provides proper information to farmers and for that Data mining and machine learning is still an emerging technique in the field of agriculture and horticulture. In this paper we have proposed a method for classifying the soil according to the macro nutrients and micro nutrients and predicting the type of crop that can be cultivated in that particular soil type. Several types of machine learning algorithms are used such as K-Nearest Neighbor (K-NN), Support vector machine (SVM) and logistic regression.