AGROCONNECT - CONNECTING FARMERS TO SMART AGRICULTURE
[1] Ms. Eugene Berna I, Assistant Professor Level -I, Department of Artificial Intelligence,
[2] Ms. Sivapriya S, Department of Electrical and Electronics Engineering,
[3] Ms. Vidharshana J, Department of Electrical and Electronics Engineering,
[4] Ms. Ragsana R V, Department of Electrical and Electronics Engineering
[1][2][3][4] Bannari Amman Institute of Technology
Abstract— Crop yield prediction focuses mostly on agricultural research, which have an enormous impact on taking decisions for example import-export, price, along with crop management. Soil is the main component and plays a significant role in agriculture. Based on the nutrients and pH value of the soil, crop yielding is determined. Farmers are still using traditional approach to analysis the soil quality. The techniques like Data Mining, Artificial Intelligence, Machine Learning, Deep learning and Predictive Analytics are the emerging technologies in research to improve the agricultural field. Predictive analysis is a technique of machine learning that predicts the future outcomes and analysis is based on the historical or past data. In agriculture, predictive analytics helps to predict or identify the soil nutrients level required for the crops like Paddy, Raagi, etc., Predicting the crop yield well ahead of its harvest would help farmers and market contractors strategize befitting actions to market and store their produce. These kinds of predictions will also help farmers minimize losses due to crop failure and can also help businesses that depend on agricultural products to plan their business logistics and resources. In this project, a method is proposed which would help predict the estimate of the crop yield for a specific land based on the analysis of geographical and climatic data using Machine Learning using LSTM. Firstly, it is able to capture the time dependency on temperature and rainfall. Secondly, it is able to work on a large and diverse dataset, unlike most models which only perform well in small regions. Lastly, it is able to use several diverse features - geographical, social, and economic to make a prediction. In addition to crop prediction, the system helps farmers to monitor the soil nutrients evolution so that action can be done on real time. The main chemical elements which are taken into the proposed model are nitrogen, phosphorus, potassium, hydrogen along with rainfall and temperature.
Keywords—Crop, Machine Learning, LSTM, deep learning (key words)
