| Title |
Soil Quality Identifying And Monitoring Approach For Sugarcane Using Machine Learning Techniques |
| ID_Doc |
52200 |
| Authors |
Shalini Patel N.; Kumar H.P.M. |
| Year |
2022 |
| Published |
4th International Conference on Emerging Research in Electronics, Computer Science and Technology, ICERECT 2022 |
| DOI |
http://dx.doi.org/10.1109/ICERECT56837.2022.10059793 |
| Abstract |
Farming is the major occupation in India and farmers are the backbone of India. Over 70 percent of people are empowered by farming. Mandya district is one of the most agriculturally prosperous districts in Karnataka. Farming is the dominant activity in the district. Sugarcane is one of the major crops of the district. The salient provenance for farming is soil. By adapting the traditional methods of farming and adding chemical fertilizers without any recommendation, soil is losing its essence. Technologies play a major role in farming. Like smart home, smart city, many researchers are working towards smart farming. As soil is the salient provenance of farming, identifying the chemical, physical and biological parameters in the soil and monitoring the soil quality will assist farmers to be aware of the soil fertility and crop suggestions. To assist growers, proposing an approach to identifying and monitoring the quality of the soil in a modern way for the betterment of farming and to retain the fertile soil for the future generation. In this approach, use machine learning techniques (Random Forest Algorithm) to monitor the quality of the soil, decide the quantity of fertilizers to be added to the soil, and crop rotation. © 2022 IEEE. |
| Author Keywords |
crop rotation; crop suggestions; Machine learning; random forest algorithm; smart farming; soil fertility |
