An artificial intelligence solution for crop recommendation

Author:

N. Varshitha D.,Choudhary Savita

Abstract

Agriculture is the major occupation in India. The development of India is in the hands of farmers. Farmers are said to be our nation’s backbone, so there is a need to support our farmers technologically so that the difficulties of traditional agricultural practices would be overcome and also there will be positive impact on the yield, harvest, healthy crop output and the income of the farmers. Farmer needs awareness about his soil and the methods to improve his soil to grow the healthy crops. We propose an approach which involves deep learning and some IOT features to help our farmers. Soil parameters such as nitrogen, phosphorous, potassium (NPK), pH, organic carbon, moisture content and few more things are considered for predicting the fertility of the soil and also to predict the right crops to be grown and nutrition required for it. We have developed a deep neural network model to predict the crop which can be suitably grown in the soil. We have also implemented the other machine learning classifiers on the same collected dataset to test the accuracies of each classifier and our deep neural network model.

Publisher

Institute of Advanced Engineering and Science

Subject

Electrical and Electronic Engineering,Control and Optimization,Computer Networks and Communications,Hardware and Architecture,Information Systems,Signal Processing

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Comprehensive Analysis of Artificial Intelligence based Crop Recommendation and Soil Analysis;2024 Second International Conference on Data Science and Information System (ICDSIS);2024-05-17

2. A Machine Learning-Driven Crop Recommendation System with IoT Integration;2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT);2024-05-02

3. AI-Enhanced Precision Crop Rotation Management for Sustainable Agriculture;2024 International Conference on E-mobility, Power Control and Smart Systems (ICEMPS);2024-04-18

4. Soil Classification and Crop Yield Prediction using Deep Learning;2023 4th International Conference on Data Analytics for Business and Industry (ICDABI);2023-10-25

5. Boosting of fruit choices using machine learning-based pomological recommendation system;SN Applied Sciences;2023-08-19

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