The relative contributions of xylem, phloem, and transpiration to fruit growth and the daily patterns of their flows have been determined in peach, during the two stages of rapid diameter increase, by precise and continuous monitoring of fruit diameter variations. Xylem, phloem, and transpiration contributions to growth were quantified by comparing the diurnal patterns of diameter change of fruits, which were then girdled and subsequently detached. Xylem supports peach growth by 70%, and phloem 30%, while transpiration accounts for ;60% of daily total inflows. These figures and their diurnal patterns were comparable among years, stages, and cultivars. Xylem was functional at both stage I and III, while fruit transpiration was high and strictly dependent on environmental conditions, causing periods of fruit shrinkage. Phloem imports were correlated to fruit shrinkage and appear to facilitate subsequent fruit enlargement. Peach displays a growth mechanism which can be explained on the basis of passive unloading of photoassimilates from the phloem. A pivotal role is played by the large amount of water flowing from the tree to the fruit and from the fruit to the atmosphere.

Image/video processing for fruit detection in the tree using hard-coded feature extraction algorithms has shown high accuracy on fruit detection during recent years. While accurate, these approaches even with high-end hardware are still computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks architecture based on single-stage detectors. Using deep-learning techniques eliminates the need for hard-code specific features for specific fruit shapes, color and/or other attributes. This architecture takes the input image and divides into AxA grid, where A is a configurable hyper-parameter that defines the fineness of the grid. To each grid cell an image detection and localization algorithm is applied. Each of those cells is responsible to predict bounding boxes and confidence score for fruit (apple and pear in the case of this study) detected in that cell. We want this confidence score to be high if a fruit exists in a cell, otherwise to be zero, if no fruit is in the cell. More than 100 images of apple and pear trees were taken. Each tree image with approximately 50 fruits, that at the end resulted on more than 5000 images of apple and pear fruits each. Labeling images for training consisted on manually specifying the bounding boxes for fruits, where (x, y) are the center coordinates of the box and (w, h) are width and height. This architecture showed an accuracy of more than 90% fruit detection. Based on correlation between number of visible fruits, detected fruits on one frame and the real number of fruits on one tree, a model was created to accommodate this error rate. Processing speed is higher than 20 FPS which is fast enough for any grasping/harvesting robotic arm or other real-time applications. HIGHLIGHTS Using new convolutional deep learning techniques based on single-shot detectors to detect and count fruits (apple and pear) within the tree canopy.

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The fruit growth model adopted by this species changes during the season due to anatomical modifications in the fruit features.

Along with sucrose, sorbitol represents the main photosynthetic product and form of translocated carbon in peach. This study aimed at determining whether peach fruit carbohydrate metabolism is affected by changes in source-sink balance, and specifically whether sorbitol or sucrose availability regulates fruit enzyme activities and growth. In various trials, different levels of assimilate availability to growing fruits were induced in vivo by varying crop load of entire trees, leaf : fruit ratio (L:F) of fruiting shoots, or by interrupting the phloem stream (girdling) to individual fruits. In vitro, fruit tissue was incubated in presence/absence of sorbitol and sucrose. Relative growth rate (RGR), enzyme activities and carbohydrates were measured at different fruit growth stages of various peach cultivars in different years. At stage III, high crop load induced higher acid invertase (AI, EC 3.2.1.26) activities and hexose : sucrose ratios. Both sorbitol and sucrose contents were proportional to L:F, while sorbitol dehydrogenase (SDH, EC 1.1.1.14) activity was the only enzyme activity directly related to L:F in both fruit growth stages. Girdling reduced fruit RGR and all major carbohydrates after 4 days and SDH activity already after 48 h, but it did not affect sucrose synthase (SS, EC 2.4.1.13), AI and neutral invertase (NI, EC 3.2.1.27). Fruit incubation in sorbitol for 24 h induced higher SDH activities than in buffer alone. In general, assimilate availability affected both sorbitol and sucrose metabolism in peach fruit, and sorbitol may function as a signal for modulating SDH activity. Under highly competitive conditions, AI activity may be enhanced by assimilate depletion, providing a mechanism to increase fruit sink strength by increasing hexose concentrations.

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