Citrus yield estimation for individual trees integrating pruning intensity and image views

Accurately estimating the yield of citrus fruit on individual trees is essential for precise orchard management and the income of producers. However, estimating the yield of citrus fruit from images of trees remains challenging among different processes of tree pruning and image acquisition. This study adopted a deep learning based detection model to count fruit in tree images and machine learning models to estimate the yield of individual trees from the fruit count. Trees under four levels of pruning intensity (no pruning, 0–5 %, 5–10 %, and 10–15 % of new sprouts pruned) and imaged from three different views (two, four, and six images per tree) to determine the optimal conditions for yield estimation. The variables considered for yield estimation included fruit count, pruning intensity and image views. Dataset containing 1200 tree images were used to train and test four machine learning models: random forest, support vector machine , extreme gradient boosting (XGBoost), and generalized linear model. The XGBoost model achieved the lowest errors in both training and testing. The optimal yield estimation occurs when there are two, four, and six image views and trees that have been pruned >10 %, 5–10 %, and ≤5 %, respectively. The findings can enhance the accuracy of image based citrus fruit yield estimation for individual trees and reveal the influences of pruning and image views. • Fruit detection and machine learning estimate citrus yield of individual trees. • Pruning intensity and image views impact citrus yield estimation significantly. • Tree height and width do NOT impact citrus yield estimation significantly. • XGBoost shows the lowest estimation errors in training and testing. • Optimal image views identified under different conditions for accurate estimation.