Abstract
Agriculture is the primary employment of people in India. Even after years of experience in farming, farmers are not able to take the right decision when it comes to the crop selection. This paper proposes to use plate tectonics neighborhood-based classifier along with Adam’s algorithm for more optimized results. The final aim of this paper is to create an algorithm that will correctly determine the most suitable crop to be grown in a particular region given various input factors. As part of the paper, plate tectonics-based optimization is used in combination with Adam’s algorithm, and a hybrid technique is developed from it. The hybrid is tested on the benchmarks to compare its convergence to global minima in comparison with its predecessor PBO. The classifier that was developed is employed for finding the best class of crops that can be grown in a place given a set of features associated with the place. We also test our proposed algorithm on soybean dataset to predict disease class based on the various symptoms leading to the disease. The Chapter also emphasizes accuracies found on few datasets that are collected online or collected independently. Of the data collected online, it was found to be 90% accurate and had a Cohen Kappa Score of 0.84 from 1 while for the data collected independently, it was 98% accurate and had a Cohen Kappa Score of 0.984 from 1.
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Goel, L., Bansal, N., Benny, N. (2020). Design and Implementation of Hybrid Plate Tectonics Neighborhood-Based ADAM’s Optimization and Its Application on Crop Recommendation. In: Hemanth, D., Kumar, B., Manavalan, G. (eds) Recent Advances on Memetic Algorithms and its Applications in Image Processing. Studies in Computational Intelligence, vol 873. Springer, Singapore. https://doi.org/10.1007/978-981-15-1362-6_8
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