Alternate Crop Prediction using Artificial Intelligence: A Case Study in Assam

In recent years, there has been a lot of utilization of Artificial Intelligence and Machine Learning in the field of agriculture to address various types of challenges faced by this sector. In an agro-based country, the focus of the agricultural sector is to achieve the maximum yield of the crops grown and make profits out of it. There has been a severe loss of crops due to the various climatic variations, pest infestation, improper soil treatment, inadequate rainfall, inadequate nutrients etc. In various research studies, the use of machine learning has been found very helpful in addressing various crop-related problems including crop prediction based on various factors. Motivated from this, we, in this paper conducted a case study in Assam for the prediction of alternate crops using artificial intelligence and with an objective to help out the farmers. With our proposed solution, the farmers will be able to predict a particular crop that will be most suitable to grow according to the season, pH of the soil, temperature, rainfall and type of the soil, keeping an eye to get the maximum yield followed by maximum profit. We have used Artificial Neural Networks (ANN) to predict the right crop to be grown. The proposed model efficiently predicts the alternate crop by preserving the original data distribution with an accuracy of about 90.89% for the test data and by using the k-fold Cross-Validation, the accuracy is about 91.57%.