AIoTST-CR : AIoT Based Soil Testing and Crop  Recommendation to Improve Yield

Shradha Joshi-Bag; Archana Vyas

doi:10.52549/ijeei.v11i3.4858

AIoTST-CR : AIoT Based Soil Testing and Crop Recommendation to Improve Yield

Shradha Joshi-Bag, Archana Vyas

Abstract

Agriculture is a backbone of any country. Farmers need to test the soil fertility and nutrients present in the soil for proper growth of the crops. In traditional system, the farmers collect soil sample and submit to soil testing labs for testing the soil nutrients and get the soil test reports manually. Farmers based on his experience and the season; decide which crop to be taken in the farm. Based on soil testing reports farmers decide which fertilizers required for the proper growth of the crop. This process is time consuming and human efforts are required and hence crop yield is affected. The recent technologies in cloud storage, wireless sensors, and AI based algorithms are very instrumental in decision making process of crop growth life cycle. Farmers can make use of mechanical automation tools for seeding, watering, supplying fertilizers, crop cutting etc. for proper growth of the crop. However, to observe the crop growth during the entire life cycle of crop farmer has to take lot of efforts to check need of water, any problem of disease to the crop, any specific fertilizers required or not and whether there is a need of harvesting. A proper decision support system is needed for helping the farmers in all such activities. Such support can be provided to a farmer so that he will be well updated about the growth of his crop in the farm. To reduce the human efforts and improve the crop yield, Artificial Intelligence and IOT based soil testing and Crop Recommendation system (AIoTST-CR) is designed and developed. AIoT based handheld soil testing system has pH, Nitrogen, Phosphorous, Potassium and Soil moisture sensing capability. A mobile application is developed to fetch the sensed data from AIoT system. A historical data is inputted to give training to ML models. Machine learning algorithm is used to predict and recommend the crop to be taken. The results show AIoTST-CR which is AIoT based soil testing and crop recommendation system provides effortless and accurate recommendations of crop. Our findings indicate that AIoT based system provides high accuracy, which outperforms existing commonly, used machine learning based crop recommendation systems.

Keywords

AIoTST-CR; AIoT; Crop Recommendation; Decision Tree; Random Forest; SVM; Naïve Bays; Crop Yield; Soil Testing

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