Soil Analysis and Crop Recommendation using Machine Learning
IEEE BASE PAPER ABSTRACT:
India is the land of agriculture and is among the top three global producers of many crops. The Indian farmer lies at the heart of the agricultural sector yet most Indian farmers remain at the bottom of the social strata. In addition, farmers find it difficult to decide which crop is best suitable and profitable for their soil, in spite of the few technological solutions that exist today, due to the variation in soil types across geographical regions. This paper proposes a crop recommendation system that uses a Convolutional Neural Network (CNN) and a Random Forest Model to predict the optimal crop to be grown by analyzing various parameters including the region, soil type, yield, selling price, etc. The CNN architecture gave an accuracy of 95.21%, and the Random Forest Algorithm had an accuracy of 75%.
OUR PROPOSED ABSTRACT:
India’s agriculture sector is significant. It is necessary for the Indian economy’s survival and expansion. India is a significant producer of many different agricultural goods. In the process of cultivating crops, soil is crucial. A non-renewable, dynamic natural resource required for life is soil. The selection of the right crop based on the needs of the soil is a common issue faced by young Indian farmers. They experience a significant decline in productivity as a result. Earlier crop cultivation used to be done by farmers with practical experience. Based on the qualities and properties of the soil, farmers are no longer able to select the ideal crop. Therefore, a recommendation system that uses a machine learning algorithm to suggest the crop that can be harvested in that specific soil has been developed. In the proposed system, we process the user-supplied image of the soil and classify it into one of four classifications of soil: Red, Alluvial, Black, and Clay. A MobileNetV2 Architecture model accomplishes this. Several crops that can be grown in that soil type are recommended when the soil type is forecasted. In order to anticipate the list of crops that would grow well in a given soil, our suggested system maps the soil and crop data. As a result, the farmers will find our proposed method useful in helping them choose the right crops for their soil and in educating inexperienced farmers. Our proposed system achieved Training accuracy of 97.34% and validation accuracy of 99.21%.
PROJECT OUTPUT VIDEO:
ALGORITHM /MODEL USED:
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 4 GB
- Operating system : Windows 10.
- Coding Language : Python 3.8
- Web Framework : Flask
Aditya Motwani, Param Patil, Vatsa Nagaria, Shobhit Verma ,Sunil Ghane, “Soil Analysis and Crop Recommendation using Machine Learning”, IEEE Conference, 2022.
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