Traditional agricultural drying methods have been a fundamental part of crop post-processing, yet they are frequently characterized by their labor-intensive nature and susceptibility to significant crop losses due to inconsistent drying practices. This paper introduces image-based drying crop predictive models designed to automate and optimize the chili pepper drying process by continuously monitoring the crops’ drying state. The proposed model leverages crop images, weight measurements, and time data to accurately assess the drying state of chili peppers. By systematically analyzing visual characteristics and temporal weight changes, the model learns to identify key indicators of the drying process. The performance of the model, based on the ResNet architecture, achieved an MAE of 0.025, RMSE of 0.032, and R2 score of 0.985. These results indicate high precision in predicting the drying state, which can significantly enhance the reliability and efficiency of the drying process. These technologies provide continuous automatic monitoring and enable the analysis of crop drying conditions using collected image datasets and real-time adjustments, thereby enhancing the efficiency of traditional, experience-based, laborious agricultural drying operations.
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