ITM Web Conf.
Volume 44, 2022International Conference on Automation, Computing and Communication 2022 (ICACC-2022)
|Number of page(s)||6|
|Published online||05 May 2022|
Farming Assistance for Soil Fertility Improvement and Crop Prediction using XGBoost
Department of Computer Engineering, Ramrao Adik Institute of Technology, Nerul, Navi Mumbai, India
In India most vital and widely practiced occupation is Agriculture and it plays a vital role in the development of our country. Soil properties, rainfall, temperature, humidity and soil pH are the factors on which agriculture is depended. In agriculture, the selection of the wrong crop may reduce crop production. Farmers should know which crops can be grown in their area. Machine Learning-based solutions are widely used in the agriculture sector. This proposed work is a recommendation system in which Machine Learning techniques are used to recommend best three crops based on soil and weather parameters. The top three crops are recommended because farmers may not have access to a particular crop if only one crop is recommended. Previous studies in this field have been done by using different Machine Learning algorithms such as Random Forest, KNN, Naïve Bayes, etc. In this proposed system XGBoost Machine Learning algorithm is used which gives better results than other algorithms. In addition, the system provides information about how to improve the soil for growing the desired crop and gives the weather forecast for next five days. As a result, this system will help farmers minimize their financial losses while also increasing crop productivity.
Key words: Machine Learning / Crop Recommendation System / XGBoost / Weather Forecast
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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