Comparison of Machine-Learning and CASA Models for Predicting Apple Fruit Yields from Time-Series Planet Imageries

Abstract

Apple (Malus domestica Borkh. cv. “Fuji”), an important cash crop, is widely consumed around the world. Accurately predicting preharvest apple fruit yields is critical for planting policy making and agricultural management. This study attempted to explore an effective approach for predicting apple fruit yields based on time-series remote sensing data. In this study, time-series vegetation indices (VIs) were derived from Planet images and analyzed to further construct an accumulated VI (∑VIs)-based random forest (RF∑VI) model and a Carnegie–Ames–Stanford approach (CASA) model for predicting apple fruit yields. The results showed that (1) ∑NDVI was the optimal predictor to construct an RF model for apple fruit yield, and the R2, RMSE, and RPD values of the RF∑NDVI model reached 0.71, 16.40 kg/tree, and 1.83, respectively. (2) The maximum light use efficiency was determined to be 0.499 g C/MJ, and the CASASR model (R2 = 0.57, RMSE = 19.61 kg/tree, and RPD = 1.53) performed better than the CASANDVI model and the CASAAverage model (R2, RMSE, and RPD = 0.56, 24.47 kg/tree, 1.22 and 0.57, 20.82 kg/tree, 1.44, respectively). (3) This study compared the yield prediction accuracies obtained by the models using the same dataset, and the RF∑NDVI model (RPD = 1.83) showed a better performance in predicting apple fruit yields than the CASASR model (RPD = 1.53). The results obtained from this study indicated the potential of the RF∑NDVI model based on time-series Planet images to accurately predict apple fruit yields. The models could provide spatial and quantitative information of apple fruit yield, which would be valuable for agronomists to predict regional apple production to inform and develop national planting policies, agricultural management, and export strategies.

Apple yield prediction mapping using machine learning techniques through the Google Earth Engine cloud in Kashmir Valley, India

“…Many studies had only concentrated on vegetation indices based on fruit yield prediction. 12,26 In some cases, the mapping of crop yield parameters was limited to the specific climatic and soil conditions through traditional statistical methods. Apple has been growing in Kashmir valley for ages; however, the use of GIS/remote sensing and ML models has been limited in the region.…”

“…Many studies had only concentrated on vegetation indices based on fruit yield prediction 12 , 26 . In some cases, the mapping of crop yield parameters was limited to the specific climatic and soil conditions through traditional statistical methods.…”

“…11 It provides a robust decision-support framework for the development of apple and its postprocessing industry. Bai et al 12 predicted apple fruit yields using a plant phenology pattern and planet images with an RM ML model in China. Pejak et al 13 improved the efficiency of the different ML systems using the multiple Sentinel 2A multispectral images and soil parameters in Upper Austria during 2018 to 2020 period.…”

“…It provides a robust decision-support framework for the development of apple and its postprocessing industry. Bai et al 12 . predicted apple fruit yields using a plant phenology pattern and planet images with an RM ML model in China.…”

Prediction of Blueberry (Vaccinium corymbosum L.) Yield Based on Artificial Intelligence Methods

“…Plant data that are input into prediction models using indirect data most often detail information about the growth status of plants or their organs in successive vegetation phases expressed in the form of vegetation indices, the degree of plant compactness (canopy/biomass), the time and rate of reaching characteristic developmental phases, e.g., flowering, and fruit setting. For the most part, these data come from remote sensing (RS), satellites or UAVs [27][28][29][30].…”