Machine Learning-Based Prediction of Tomato Yield in Greenhouse Environments

DOI

10.2991/978-94-6463-496-9_10How to use a DOI?

Keywords

Tomato yield prediction; Machine Learning; Smart agriculture; Greenhouse environments; Precision agriculture

Abstract

The agricultural sector heavily relies on accurate crop yield predictions, providing farmers with crucial information to manage their crops, allocate resources efficiently, and plan market strategies. This article proposes a novel approach utilizing a Stacked Ensemble Model for predicting tomato crop yield in greenhouse environments. A comprehensive dataset encompassing various factors related to greenhouse climate, crop parameters, and production was used for training and evaluating the models. Comparative analysis with other advanced regression models, including K-Nearest Neighbors (KNN), Random Forest, and Light-GBM, demonstrated the superior performance of the Stacked Ensemble Model, highlighted by the highest R2 value (0.896) and the lowest mean squared error (MSE) of 0.008. These results signify heightened accuracy and a close alignment between predicted and actual values. Our proposed system empowers farmers with the ability to accurately predict tomato yield, enabling them to mitigate risks, optimize harvest schedules, and effectively meet market demands.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - M’hamed Mancer
AU  - Labib Sadek Terrissa
AU  - Soheyb Ayad
PY  - 2024
DA  - 2024/08/31
TI  - Machine Learning-Based Prediction of Tomato Yield in Greenhouse Environments
BT  - Proceedings of the International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024)
PB  - Atlantis Press
SP  - 117
EP  - 128
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-496-9_10
DO  - 10.2991/978-94-6463-496-9_10
ID  - Mancer2024
ER  -