The Yield Estimation of Apple Trees Based on the Best Combination of Hyperspectral Sensitive Wavelengths Algorithm

Abstract

Agriculture’s sustainable growth necessitates the application of advanced science and technology to ensure the sensible use of resources and improve the agricultural economy’s long-term stability. In this study, apple trees were employed as research objects throughout the spring (NSS) and autumn shoot stop-growing stage (ASS), and the data source was canopy hyperspectral data of fruit trees collected using ASD near-earth sensors, which was then combined with multiple sensitive wavelength screening algorithms and machine learning models to create an efficient and accurate apple yield estimation system. This is critical for guiding fruit farmers’ production, maintaining market supply and demand balances, fostering stable agricultural economy development, and providing a scientific basis and technical support for agricultural sustainability. Firstly, the fruit tree canopy hyperspectral data and apple tree yield data were collected, and the Savitsky–Golay convolution smoothing method (SG) was used to preprocess the canopy hyperspectral data. Secondly, six algorithms—Competitive Adaptive Re-weighting Sampling (CARS), Genetic Algorithm (GA), Successive Projections Algorithm (SPA), Uninformative Variable Elimination Algorithm (UVE), Variable Iteration Spatial Shrinking Algorithm (VISSA), and Variable Combination Population Algorithm (VCPA)—were employed to screen for the sensitive wavelengths related to apple tree yield, then preferring three methods for two-by-two combinations to determine the optimal algorithm combinations. Finally, using the best algorithm combinations, we built the apple yield linear model partial least squares regression (PLSR) and three machine learning models, Random Forest (RF), Cubist, and XGBoost, to screen for the best estimation model. The results demonstrated that ASS was the best fertility period for estimating yield; the validation set of the model constructed using each algorithm in ASS had a higher R2 of 0.05–0.51 and a lower RMSE of 0.21–5.33 than those in NSS. The three algorithms preferred were CARS, GA, and VISSA. After combining the three algorithms in two combinations, the best combination of VISSA-CARS was found. The RF model established based on the best VISSA-CARS combination algorithm is the best model for apple yield estimation, with a validation set R2 = 0.78 and RMSE = 6.03. The findings of this study may provide a new concept for accurately and quickly estimating apple yield, allowing fruit growers to improve production efficiency and promote agricultural sustainability.