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Climate change is leading to higher plant water requirements and rootstock can play a role in tree adaptation, since the more vigorous ones are also likely to be more stress resistant. Pear trees of the cv. Abbé Fetél grafted on BA29 (more vigorous) and SYDO (more dwarfing) quince were irrigated according to three different treatments: 110 C, 80 DI and 60 DI, corresponding to 110%, 80% and 60% of the crop evapotranspiration rate (ETc), respectively. Shoot and fruit growth, water potentials, leaf gas exchanges and dry matter content were monitored during the season. Fruit quality was evaluated at harvest and after 6 months of storage at 1 °C. Results show how for both rootstocks, 60 DI significantly decreased their stem (Ψstem) and leaf (Ψleaf) water potentials as well as leaf gas exchanges. In SYDO, final fruit size was affected by irrigation, with lower values on 60 DI, but in BA29, no differences were found between treatments. After storage, BA29 60 DI fruit showed a higher soluble solid content, while in SYDO fruit, firmness was more affected by irrigation level. In conclusion, despite a slight decrease in fruit size, reduced irrigation led to fruit with higher quality features that were also maintained after a long period of storage.

Although superficial scald (SS) is well characterized on apples, there are only a few insights concerning the influence that agronomic and management variability may have on the occurrence of this physiological disorder on pears. In this study, we aimed to improve our understanding of the effect of different preharvest factors on SS development using a multivariate statistical approach. Pears (Pyrus communis L.) cv “Abate Fetel” were picked during two consecutive seasons (2018-2019 and 2019-2020) from twenty-three commercial orchards from three growing areas (Modena, Ferrara, and Ravenna provinces) in the Emilia-Romagna region of Italy. Bioclimatic indices such as weather and soil, agronomic management such fertilization and irrigation, orchard features such as rootstock and training systems, and SS incidence were carried out at harvest and periodically postharvest in all producers. Two different storage scenarios (regular atmosphere and use of 1-MCP) were also evaluated. Our data in both seasons showed high heterogeneity between farms for SS symptoms after cold storage either in the regular atmosphere or with 1-MCP treatment. Nevertheless, in 2018, all the producers showed SS at the end of the storage season, but in 2019 some of them did not exhibit SS for up to 5 months. In fact, some preharvest factors changed considerably between the two seasons such as yield and weather conditions. Indeed, some factors seem to affect SS in both growing seasons. Some can increase its occurrences such as physiological and agronomical factors: high yields, late date of blooming, heavy downpours, improper irrigation management (low watering frequency and high volumes), nitrogen (included that deriving from organic matter), soil texture (presence of clay), orchard age, and canopy volume in relation to training system and rootstock. Others can decrease SS such as climatic and management factors: late harvest dates, rain, gibberellins, calcium, manure, absence of antihail nets or use of photoselective nets, and site (probably related to better soils toward the Adriatic coast). Initial preharvest variability is an important factor that modulates physiological plant stress and, subsequently, the SS after cold storage in “Abate Fetel” pears. Multivariate techniques could represent useful tools to identify reliable multiyear preharvest variables for SS control in pear fruit different batches.

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.

Rootstock vigor is well known to affect yield and productive performance in many fruit crops and the dwarfing trait is often the preferred choice for modern orchard systems thanks to its improved productivity and reduced canopy volume. This work investigates the different physiological responses induced by rootstock vigor on cherry, by comparing shoot and fruit growth, water relations, leaf gas exchanges as well as fruit vascular and transpiration in/outflows of “Black Star” trees grafted on semi-vigorous (CAB6 P) and on semi-dwarfing (Gisela™6) rootstocks. The daily patterns of stem (Ψstem), leaf (Ψleaf) and fruit (Ψfruit) water potential, leaf photosynthesis, stomatal conductance and transpiration, shoot and fruit growth, fruit phloem, xylem and transpiration flows were assessed both in pre- and post-veraison, while productivity and fruit quality were determined at harvest. At both stages, no significant differences were found on Ψleaf, photosynthesis, fruit daily growth rates as well as fruit vascular and transpiration flows, while trees on Gisela™6 showed lower shoot growth rates and lower Ψstem and Ψfruit than trees on CAB6 P. The resulting decrease in stem-to-leaf Ψ gradient on Gisela™6 trees determined a reduction in shoot growth by decreasing shoot strength as sinks for water and carbohydrates. On the other hand, Gisela™6 fruit lowered their Ψfruit thanks to a higher osmotic accumulation and increased their competitiveness towards shoots, as confirmed by the higher productivity and fruit soluble solid content found at harvest for these trees. These results indicate that rootstock vigor alters resource competition between vegetative and reproductive growth, which can affect water use efficiency, yield, and fruit quality.