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In the winter pruning operation of deciduous fruit trees, the number of pruning branches and the structure of the main branches greatly influence the future growth of the fruit trees and the final harvest volume. Terrestrial laser scanning (TLS) is considered a feasible method for the 3D modeling of trees, but it is not suitable for large-scale inspection. The simultaneous localization and mapping (SLAM) technique makes it possible to move the lidar on the ground and model quickly, but it is not useful enough for the accuracy of plant detection. Therefore, in this study, we used UAV-SfM and 3D lidar SLAM techniques to build 3D models for the winter pruning of peach trees. Then, we compared and analyzed these models and further proposed a method to distinguish branches from 3D point clouds by spatial point cloud density. The results showed that the 3D lidar SLAM technique had a shorter modeling time and higher accuracy than UAV-SfM for the winter pruning period of peach trees. The method had the smallest RMSE of 3084 g with an R2 = 0.93 compared to the fresh weight of the pruned branches. In the branch detection part, branches with diameters greater than 3 cm were differentiated successfully, regardless of whether before or after pruning. [ABSTRACT FROM AUTHOR]

The biggest challenge in the classification of plant water stress conditions is the similar appearance of different stress conditions. We introduce HortNet417v1 with 417 layers for rapid recognition, classification, and visualization of plant stress conditions, such as no stress, low stress, middle stress, high stress, and very high stress, in real time with higher accuracy and a lower computing condition. We evaluated the classification performance by training more than 50,632 augmented images and found that HortNet417v1 has 90.77% training, 90.52% cross validation, and 93.00% test accuracy without any overfitting issue, while other networks like Xception, ShuffleNet, and MobileNetv2 have an overfitting issue, although they achieved 100% training accuracy. This research will motivate and encourage the further use of deep learning techniques to automatically detect and classify plant stress conditions and provide farmers with the necessary information to manage irrigation practices in a timely manner. [ABSTRACT FROM AUTHOR]

• The disorder was confirmed to be related to fruit quality factors. • These factors included total soluble solids, pH, fruit weight and firmness. • Mineral element analyses indicated that calcium levels also relate to the disorder. • The involvement of calcium was confirmed by CaCl 2 application and bagging of fruit. • The involvement of calcium depended on its interaction with other factors. Water-soaked brown flesh disorder in peach [ Prunus persica (L.) Batsch] fruit is a known physiological disorder which can affect the economic value of peach crops. The disorder is more severe in high-quality fruit when maturity and/or ripening advanced. Principal component analysis (PCA) of fruit with different qualities and mineral concentrations showed that factors contributing to the first component (PC1) were total soluble solids (TSS), fruit weight, pH, B and Ca concentrations, the ratios of Ca:K, Ca:Mg and Ca:(Mg + K), and the severity of the disorder. Firmness was not grouped in PC1. When firmness was low, the disorder appeared in fruit with high TSS (> 15 %) and low Ca concentration (< 24 mg kg−1 FW). CaCl 2 application significantly increased Ca concentration in the fruit and reduced the symptoms of the disorder. A bagging treatment to minimize fruit transpiration significantly decreased the Ca concentration during the growing period and in the apoplast of the flesh at harvest, resulting in increased symptoms of the disorder. These results indicated that as well as fruit quality factors, such as TSS, pH and fruit weight, Ca concentration is involved in this disorder. Because the contribution of Ca was not strong compared to the other factors, the effect of Ca on the disorder should be considered in terms of its relationships with other contributing factors, such as the condition of the tree and its environment. [ABSTRACT FROM AUTHOR]