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.
Cite this article
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 -