Evaluation and Optimization of Prediction Models for Crop Yield in Plant Factory

Abstract

This study focuses on enhancing crop yield prediction in plant factory environments through precise crop canopy image capture and background interference removal. This method achieves highly accurate recognition of the crop canopy projection area (CCPA), with a coefficient of determination (R2) of 0.98. A spatial resolution of 0.078 mm/pixel was derived by referencing a scale ruler and processing pixel counts, eliminating outliers in the data. Image post-processing focused on extracting the canopy boundary and calculating the crop canopy area. By incorporating crop yield data, a comparative analysis of 28 prediction models was performed, assessing performance metrics such as MSE, RMSE, MAE, MAPE, R2, prediction speed, training time, and model size. Among them, the Wide Neural Network model emerged as the most optimal. It demonstrated remarkable predictive accuracy with an R2 of 0.95, RMSE of 27.15 g, and MAPE of 11.74%. Furthermore, the model achieved a high prediction speed of 60,234.9 observations per second, and its compact size of 7039 bytes makes it suitable for efficient, real-time deployment in practical applications. This model offers substantial support for managing crop growth, providing a solid foundation for refining cultivation processes and enhancing crop yields.

Plant phenotyping relevance

作物キャノピー画像から面積を抽出し、その形質を用いた収量予測モデルを比較・最適化しており、画像取得・抽出と予測ワークフローが中心的な方法論的貢献である。

Code and data availability

The paper reports smartphone canopy images (60 cabbage images), yield weights, and 28 MATLAB-style prediction models, but the Data Availability Statement says data are contained within the article only. No public dataset, image repository, code deposit, or trained model checkpoint with an authors' URL is mentioned.

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