Forthcoming and Online First Articles

International Journal of Artificial Intelligence and Soft Computing

International Journal of Artificial Intelligence and Soft Computing (IJAISC)

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International Journal of Artificial Intelligence and Soft Computing (1 paper in press)

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  • An extensive three-tiered architecture for comprehensive crop and fertilizer prediction using supervised learning   Order a copy of this article
    by Abhinav Singh Roy, Sarvika Tiwari, Shubham Wawale, Soham Talekar, Pallavi V. Chavan 
    Abstract: Agriculture accounts for 19.9% of Indias gross domestic product. However, the research and development in this sector do not reflect the massive share of its contribution to our economy. A lot of agricultural activities are still conducted in an archaic manner with little to no thought given to a data-led approach to maximizing yield and profits. Crop yield prediction is a demanding but stimulating hurdle in the agricultural domain as it is contingent on various soil, environmental and geographical factors. It is a crucial step for systematic agronomy to maximize profits while maintaining soil health. Suitable fertilizer selection is paramount not only in optimizing crop yield but also in conserving soil fertility and quality. In this paper, the authors present an extensive threetiered architecture for comprehensive crop and fertilizer prediction. The authors use large amounts of historical data (which is freely available in the public domain) to train the model with different variables such as soil pH, moisture and temperature in mind. A three-tiered solution is proposed which focuses on predicting a crop based on the area under cultivation and geographical region. The yield for the given crop is predicted. The second tier focuses on predicting the cost of cultivation for the given crop and the area under cultivation. Finally, a fertilizer is predicted for the given crop based on soil nutrient and environmental factors in the third tier. Crop Prediction implemented through The Random Forest Classifier gave 99.54% accuracy. Yield Prediction determined using Linear Regression yielded 89.57% accuracy. Naive Bayes algorithm used to predict the fertilizer for a given crop provided 100% accuracy.
    Keywords: Naive Bayesian; Random Forest Classification; Linear Regression; Supervised Learning; Prediction.