Volume 4,Issue 3
Study on Light Response Mechanism and Yield Prediction of Tropical Dragon Fruit Integrating Multimodal Data and Machine Learning
In off-season dragon fruit cultivation in Hainan, artificial supplementary lighting has become a critical method for improving winter flowering rates. However, current lighting management predominantly relies on empirical approaches, resulting in suboptimal parameters, high energy consumption, and unstable yields. To address these challenges, this study proposes a research framework integrating multimodal data and machine learning to elucidate light response mechanisms in dragon fruit and achieve precise yield prediction. The study first establishes a multimodal dataset incorporating environmental conditions, image data, and physiological indicators through field orthogonal experiments. Next, interpretable machine learning algorithms are employed to quantify nonlinear relationships between lighting parameters and flowering rates, enabling optimal lighting strategies. Subsequently, YOLO object detection combined with LSTM/Transformer time-series models facilitates automated flower-fruit tracking and dynamic yield prediction. Finally, an integrated intelligent lighting decision support system prototype is developed. This research aims to transition from experience-driven approaches to data-driven intelligent solutions, providing theoretical foundations and technical references for precision cultivation of tropical fruit trees.
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