Support Vector Machine for Crop Yield Prediction Towards Smart Agriculture

Meenakshi, G. Annalakshmi, Domenic Sanchez, Malik Jawarneh

2023

Abstract

Agriculture is absolutely necessary for the continued existence of the human race. The agriculture industry provides a living for a great number of people in a great number of countries. People now have access to a more diverse range of options when it comes to their job trajectories. In spite of the fact that conventional farming brings in pitiful returns in today’s market, many farmers harbour a deep-seated yearning for the less complicated times of days gone by. Agriculture production can be increased by agribusinesses by concentrating on high-yielding crop varieties and investing in the infrastructure required to support those types. Forecasting agricultural production requires taking into account a number of factors, including climate, soil health, the availability of water, crop pricing, and consumer demand. It would be difficult to predict agricultural production based on factors such as location, climate, and harvest season without the assistance of machine learning. Agricultural productivity can be influenced by factors such as location, climate, and harvest season. However, the development of this technology may make it feasible. Farmers can use this instrument to discover which types of plant life would be most successful in a particular setting. This paper describes architecture for the use of machine learning in agriculture for the purpose of predicting crop yields. Crop yield data set is used for experimental work. Accuracy, sensitivity and specificity are used to compare the performance.

Paper Citation

in Harvard Style

Meenakshi., Annalakshmi G., Sanchez D. and Jawarneh M. (2023). Support Vector Machine for Crop Yield Prediction Towards Smart Agriculture. 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 541-545. DOI: 10.5220/0012614900003739

in Bibtex Style

@conference{ai4iot23,
author={Meenakshi and G. Annalakshmi and Domenic Sanchez and Malik Jawarneh},
title={Support Vector Machine for Crop Yield Prediction Towards Smart Agriculture},
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={541-545},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012614900003739},
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 - Support Vector Machine for Crop Yield Prediction Towards Smart Agriculture
SN - 978-989-758-661-3
AU - Meenakshi.
AU - Annalakshmi G.
AU - Sanchez D.
AU - Jawarneh M.
PY - 2023
SP - 541
EP - 545
DO - 10.5220/0012614900003739
PB - SciTePress