AgroAdvisor: Crop Yield Prediction, Crop and Fertilizer Recommendation System using Random Forest with Gradient Boosting and DeepFM for Precise Agriculture-Reference-Cited by-同舟云学术

AgroAdvisor: Crop Yield Prediction, Crop and Fertilizer Recommendation System using Random Forest with Gradient Boosting and DeepFM for Precise Agriculture

Published:2024-04-09 Issue: Volume: Page:
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Author:

Kukkar Ashima¹,Mohana Rajni²,Sharma Aman²,Mallik Saurav³,Shah Mohd Asif⁴

Affiliation:

1. Chitkara University Institute of Engineering and Technology, Chitkara University

2. Jaypee University of Information and Technology Technology

3. Harvard T H Chan School of Public Health

4. Kardan University

Abstract

Abstract Crop yield prediction plays a very important role in productivity growth. Prediction of the crop yield in particular area helps the farmer to choose the right crop to be grown in the land. With crop yield prediction crop recommendation boosts up the productivity of crop. Recommending the correct type of crop in particular land on the factors of soil pH, rainfall, temperature, humidity etc. helps the farmer to choose specific and most suitable crop. With recommendation and yield prediction of crop, fertilizer recommendation is also necessary for more productivity and yield. It is necessary to use suitable fertilizers on optimal timing for the growth of crops. Therefore, in this paper, we have attempted to address these issues by proposing three model systems that will efficiently manage crop production. In this paper, we designed an integrated system named as AgroAdvisor using the hybrid proposed technique such as Random Forest with Extreme Gradient Boosting (RFXGB) and Deep Factorization Machine (DeepFM). RFGB is applied for processing the features, which improves the DeepFM ability to handle the dense numerical features and increase the prediction performance. The result of RFXGB-DeepFM is compared with classical machine learning and deep learning techniques by using recall, F-value, precision and accuracy parameters. The results show that the proposed RFGB-DeepFM technique gives better accuracy than the classical techniques. The impact of RFGXB on existing techniques is also analyzed using Friedman and post hoc statistical testing and results show that in most cases RFGXB enhanced the performance.

Publisher

Research Square Platform LLC

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