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To optimize agricultural operations and guarantee food security, it is imperative to accurately predict drop yields. This paper introduces a deep neural network (DNN) model for predicting tomato yield in a controlled environment agriculture (CEA) system. The model capitalizes on a high-dimensional dataset that encompasses a diverse array of environmental and agronomic variables. With a test root mean square error (RMSE) of 0.07, mean absolute error (MAE) of 0.05, and R-squared ( \({R}^{2}\) ) of 0.96, the DNN model exhibited high predictive accuracy. The cross-validated RMSE of 0.07 (95% Confidence Interval (CI): 0.03–0.12), MAE of 0.06 (95% CI: 0.01–0.12), and \({R}^{2}\) of 0.82 (95% CI: 0.56–1.08) further validated the model's performance. These results indicate the potential of the proposed DNN model to significantly enhance precision agriculture practices.

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A Deep Neural Network for Tomato Yield Prediction in Controlled Environment Agriculture

  • 会议论文

  • 发表日期：

    2025年

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  • Hassan Ali,
  • Rachid Benlamri,
  • Aitazaz A. Farooque,
  • Raziq Yaqub,
  • Fahim Ullah Khan,
  • Ahmed Badawi

摘要

To optimize agricultural operations and guarantee food security, it is imperative to accurately predict drop yields. This paper introduces a deep neural network (DNN) model for predicting tomato yield in a controlled environment agriculture (CEA) system. The model capitalizes on a high-dimensional dataset that encompasses a diverse array of environmental and agronomic variables. With a test root mean square error (RMSE) of 0.07, mean absolute error (MAE) of 0.05, and R-squared ( \({R}^{2}\) ) of 0.96, the DNN model exhibited high predictive accuracy. The cross-validated RMSE of 0.07 (95% Confidence Interval (CI): 0.03–0.12), MAE of 0.06 (95% CI: 0.01–0.12), and \({R}^{2}\) of 0.82 (95% CI: 0.56–1.08) further validated the model's performance. These results indicate the potential of the proposed DNN model to significantly enhance precision agriculture practices.