Towards an Efficient Deep Learning Approach for Crop Recommendation

D. Kumar, S. Sheeja

2023

Abstract

The agriculture industry depends heavily on crop output, which is influenced by a variety of meteorological and chemical elements. Just because of these two factors, farmers lose a lot of money. Although the climatic factors are beyond human control, automated technologies may be used to control the chemical factors. Many solutions to these worries have been offered through research. This study, however, focuses on crop suggestions based on chemical and meteorological conditions. In order to recommend better crops depending on chemical and meteorological circumstances, this study proposes an optimization-based deep learning approach based on the grey wolf. According on a number of chemical and meteorological factors, such as pH, nitrogen, phosphorus, and potassium as well as rainfall, temperature, and humidity, this paper makes crop recommendations to farmers. The entire plan is presented in layers: A high-performance Convolution neural network is utilized to extract and categories important characteristics, after which the feature is optimized using the grey wolf method to suggest a better crop based on a variety of criteria.

Paper Citation

in Harvard Style

Kumar D. and Sheeja S. (2023). Towards an Efficient Deep Learning Approach for Crop Recommendation. In Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT; ISBN 978-989-758-661-3, SciTePress, pages 281-285. DOI: 10.5220/0012614100003739

in Bibtex Style

@conference{ai4iot23,
author={D. Kumar and S. Sheeja},
title={Towards an Efficient Deep Learning Approach for Crop Recommendation},
booktitle={Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT},
year={2023},
pages={281-285},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012614100003739},
isbn={978-989-758-661-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT
TI - Towards an Efficient Deep Learning Approach for Crop Recommendation
SN - 978-989-758-661-3
AU - Kumar D.
AU - Sheeja S.
PY - 2023
SP - 281
EP - 285
DO - 10.5220/0012614100003739
PB - SciTePress