Title: AoDrA-Net: an approach to recommend crop for sustainable agriculture

Authors: P. Prabharani; S. Appavu Alias Balamurugan; S. Sasikala

Addresses: Information and Communication Engineering, Anna University, Chennai, India ' Department of Computer Science and Engineering, Periyar Maniammai Institute of Science and Technology, Thanjavur, India ' Department of Artificial Intelligence and Data Science, Velammal College of Engineering and Technology, Madurai, India

Abstract: Agriculture is a fundamental component of India's socio-economic structure. The inability of farmers to detect the most appropriate crop for the soil through conventional and non-scientific practices is a severe problem in a nation where 58% of the approximate population is engaged in agriculture. In some cases, farmers were unable to select the appropriate crops due to different soil conditions, different sowing seasons, and different regions. This results in suicide, abandoning agriculture, and shifting to metropolitan areas for livelihood. Archimedes optimised discrete deep residual AlexNet (AoDrA-Net) builds a complete crop recommendation framework (CRS-DDRAN-AOA) which offers direction and inspiration for the mentioned deficiencies. Initially, data is taken from the dataset of crop recommendation. Then the input data is pre-processed under z-score standardisation procedure. Then, the pre-processed output data is given to DDRAN augmented with AOA that accurately recommends the crop by lessening the error and raising the recommendation accuracy.

Keywords: discrete deep residual AlexNet; crop recommendation dataset; Archimedes optimisation algorithm; z-score standardisation process; crop recommendation system.

DOI: 10.1504/IJBIC.2024.141462

International Journal of Bio-Inspired Computation, 2024 Vol.24 No.2, pp.109 - 121

Received: 07 Nov 2022
Accepted: 27 Nov 2023
Published online: 13 Sep 2024 *

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