关键词: 苹果直径, 生长模型, 气象因子, 深度学习, 长短期记忆模型(LSTM)

Abstract:

【Objective】 Meteorological factors are closely related to fruit diameter during growth process, but this relationship between them tends to be non-linear and non-stationary, thus making it hard to monitor the fruit and trunk diameter continuously. Comparing the simulation capabilities of various growth models for fruit diameter could provide scientific support for fruit growth monitoring and predicting, timely irrigation and fertilization, and the regulation of growth environment. 【Method】 Taking apples in Zhaotong, Yunan Province as an example, this study first analyzed the characteristics of diameter change during apple growth in 2019 and 2020 and its relationship with environmental and climate factors. Subsequently, a deep learning method of Long Short-Term Memory (LSTM) model was adopted to simulate and predict the fruit diameter by integrating these factors, which was evaluated with the multi-linear regression (MLR) model and machine learning methods including Decision Tree (DT) and Random Forests (RF) using three sampling methods. 【Result】 The apple diameter had obvious diurnal cycle characteristics, which shrunk in the daytime and expanded in the nighttime. The maximum diameter was in the morning, while the minimum diameter was near the sunset. The growth rate of apple diameter was higher in the early growth period than near mature. The hourly and daily mean apple diameters were moderately or highly-positive correlated with soil temperature and soil moisture, while there was a highly-negative correlation with UVI. The daily mean increase (FMDG), daily increase (FDG), and maximum daily shrinkage (MDFS) of apple diameter had a weak negative correlation with 60 cm soil temperature as well as 20 and 40 cm soil moisture (-0.5≤R<-0.3). The simulation accuracy of the LSTM model was significantly higher than that of MLR, DT and RF model. The correlation coefficient (R) of LSTM model increased (3% -20%) compared with MLR, and the RMSE and MAE were approximately decreased by 50%-75%. The machine learning methods showed relatively poor performance in apple diameter simulation and might have overfitting problems. 【Conclusion】 Compared to statistics and machine learning approaches, the LSTM model demonstrated higher accuracy and robust performance because of the incapability of considering the complex non-linear correlations in the fruit growth simulation.

Key words: apple diameter, growth model, meteorological factors, deep learning, Long Short-Term Memory (LSTM)