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

## TL;DR

This study improves crop yield prediction in plant factories using high-resolution canopy images and compares 28 models to find the best one for real-time use.

## Contribution

A novel method for accurate crop canopy area recognition and the identification of the optimal prediction model for plant factories.

## Key findings

- The method achieved an R2 of 0.98 for crop canopy projection area recognition.
- The Wide Neural Network model showed the best performance with an R2 of 0.95 and high prediction speed.
- The model's compact size (7039 bytes) makes it suitable for real-time deployment.

## 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.