Prediction of Optimal Harvest Timing for Melons Through Integration of RGB Images and Greenhouse Environmental Data: A Practical Approach Including Marker Effect Analysis

Non-destructive prediction of harvest timing is increasingly important in greenhouse melon cultivation, yet image-based methods alone often fail to reflect environmental factors affecting fruit development. Likewise, environmental or fertigation data alone cannot capture fruit-level variation. This gap calls for a multimodal approach integrating both sources of information. This study presents a fusion model combining RGB images with environmental and fertigation data to predict optimal harvest timing for melons. A YOLOv8n-based model detected fruits and estimated diameters under marker and no-marker conditions, while an LSTM processed time-series variables including temperature, humidity, CO2, light intensity, irrigation, and electrical conductivity. The extracted features were fused through a late-fusion strategy, followed by an MLP for predicting diameter, biomass, and harvest date. The marker condition improved detection accuracy; however, the no-marker condition also achieved sufficiently high performance for field application. Diameter and weight showed a strong correlation (R2 > 0.9), and the fusion model accurately predicted the actual harvest date of 28 August 2025. These results demonstrate the practicality of multimodal fusion for reliable, non-destructive harvest prediction and highlight its potential to bridge the gap between controlled experiments and real-world smart farming environments.