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In Italy, most of the traditional sweet chestnut (Castanea sativa Mill.) orchards are still non-irrigated since they are located in mountain-hill areas with climate conditions that used to be optimal to sustain the vegetative and reproductive growth of this nut tree species. Nowadays, the increase of summer temperatures and the decrease of rainfall (due to climate change) are affecting negatively chestnut physiological performances. The aim of this experiment was to study sweet chestnut burr growth in two seasons, one warm/dry and one mild/rainy (2017 and 2018, respectively). The study was carried out in a traditional rainfed chestnut orchard. The seasonal burr growth was measured weekly from 30 days after full bloom (DAFB) to the beginning of burr valves opening. Air temperature and daily precipitation were measured at a nearby weather station. The results of this study highlighted that chestnut burr growth seems to be affected by seasonal weather conditions. Indeed, in 2017, the high summer temperatures and the moderate rainfall in summer (227 mm) and winter-spring (385 mm) appeared to affect negatively burr absolute growth rate (AGR; 0.31 mm day-1 ) and consequently final burr size (46.2 mm). The mild and rainy weather conditions that occurred in 2018 (663 and 340 mm of winter-spring and summer precipitation, respectively) positively influenced burr AGR (0.54 mm day-1 ) and therefore its final size (60.8 mm). These preliminary results suggest that the introduction of irrigation as a common management practice for chestnut orchards may promote their resilience to climate change with a positive effect on their productivity and fruit quality.

The seasonal and daily growth of sweet chestnut (C. sativa Mill.) husks have been studied in a commercial orchard in Monterenzio (Bologna, Italy). The seasonal growth pattern in the maximum and minimum diameter of 20 husks were measured every 7 days from 30 days after full bloom (DAFB) - corresponding at the beginning of June - to a week before harvest (120 DAFB). The absolute and relative growth rate (AGR, RGR) have also been calculated. Growth of 6 husks was monitored from the 4th to the 11th of August 2017 through very precise fruit gauges, connected to a custom-built, Arduino-based data logger, which was monitored through a wireless network (AWSN). The 6 husks were selected on 3 trees (2 husks per tree) and their diameters were recorded every 15 minutes for the entire week. Temperature (0C) and daily precipitation (mm/day) were sourced from a close (2 km) meteorological station belonging to ARPAE (Regional agency for environmental control). The husk exhibited a seasonal diameter growth pattern resembling a double sigmoid. AGR responded positively to rainfalls, as no irrigation was provided. Interestingly, daily husk growth started mid-morning and continued till dark (from 10:00 to 21:00), followed by shrinkage during night-time. This pattern of growth appears opposite if compared to other fruit species as Prunus persica L. or Malus domestica Bork., which exhibit growth during the last part of the day/night and shrinkage/no growth during the day. Our data show a quick increase (within the following 24 hours) in husk growth rate after a rainfall event. These preliminary data show the need for further studies on C. sativa physiology and their potential impact on improving irrigation practices for this intriguing crop.

During recent years, with the increase of production in agriculture, the need for more precise tools and practices has increased. One of those practices is the estimation of the fruit number in the tree. Computer vision techniques such as histogram of oriented gradients and edge/color detection have been used to extract features thus recognizing fruit based on shape and color. Existing methods usually rely heavily on computing multiple image features, making the whole system complex and computationally expensive. In this paper we compare those classical detection algorithms to new state-of-the-art convolution neural networks. Specifically, we compare two types of algorithms for apple detection in the tree. The first approach refereed as hard-coded uses commonly feature extraction filters (edge detector, color filtering, corners). On the other side are techniques using CNNs convolution neural networks like (residual networks, sliding window, regional dividers). More than thousand images of apple trees were taken during the season from flowering time to harvest. Same pictures have been processed through both techniques and based on results and the trade-offs of both techniques have been compared. For hard-coded algorithms, with few pictures we were able to see the performance of algorithm, while with CNNs, huge number of labeled pictures were needed for the algorithm to be more than 50% accurate. However, when a different picture from another date or completely new cultivar was used, the hard-coded algorithm failed to detect thus had to be rewritten to accommodate new changes. In other hand CNNs were very flexible and were able to detect apples even though the picture taken-date was changed or picture from another cultivar was used.

In the last decade computer vision had an enormous evolution and its application in agriculture is expanding quickly with a lot of research done and several solutions already available on the market. This fact is due to the willing and the necessity to robotize agricultural process so to ease the spread of "smart agriculture" approaches and techniques to be more precise in responding to plants, environment, and human needs. Fruit crops sector is one of the most difficult agricultural sectors on which apply robotization because of its high level of complexity both at orchard and tree level. It is recognized that a simplification of the tree and orchard environment will certainly help in automate activity in fruit production so, lately the diffusion of less complex two-dimensional tree shapes is happening. This study wants to evaluate improvement in computer vision application for fruit detection problems, that 2D training systems should bring with them. To the knowledge of the authors this could be the first paper trying to quantify that. In the trial a YOLOv3 neural network was trained on three datasets containing 2D, 3D and mixed apple training system images. Two model specialized on 2D and 3D training system, and one specialized in mixed situation were obtained. These models were then cross evaluated to define their performances in each training system condition (2D, 3D and mixed). In add to that a ground truthing dataset, with a known number of real fruits, was utilized to evaluate which percentage of the real fruit number can be directly detected by the models and how much the different training system affect this capability. Results show that the developed models present generally poor performance for field application with max F1-score of 0.68. For all the model*dataset combination, mixed model resulted always the best, followed by 2D or 3D model when applied to their relative training system. Bests performances were achieved by two models out of three in 2D training system dataset suggesting that this shape improve fruit detection. 2D model performed better than 3D in mixed situation suggesting better training phase done with 2D system images. From ground truthing analysis, 2D training system improved models result from 2.4 to 11.5%.

Wastewater may represent an alternative source for irrigation and mineral nutrients in intensive agriculture, offering potential agronomical and environmental advantages. This work investigates the effect of STW (secondary treated wastewater) used for irrigation, on the nutritional and physiological response of 3-year-old nectarine trees. To this end, trees ('Big Top'/'GF 677') were individually grown on 40-L pots filled with a sandy-loamy soil and drip irrigated with: a) Tap water (U) (unfertilized trees); b) Tap water (M) (mineral fertilized trees) and c) Secondary treated wastewater (W) (trees did not receive mineral fertilizers). Each treatment was applied to five individual trees. Regardless of the water source, trees received 360 L tree‑1 season‑1 and only M-trees received 14.2, 2.35, 8.96, and 0.72 g tree‑1 season‑1 of N, P, K and Mg, respectively, from commercial fertilizers throughout the season. Shoot length and photosynthetic daily assimilation rates were promoted by STW, compared to U trees, although M trees showed the highest values. This response is likely related to the amount of nutrients supplied to the trees along the season. Although STW provided a “fertigation-like” effect, results suggest that this strategy did not completely fulfill the tree nutrient demand. On the other hand, the STW recycling as irrigation water was not detrimental to plant growth. Treatments affected mainly leaf rather than fruit mineral concentration. Mineral concentration resulted mostly in the optimal range for all the treatments, except the U, which showed nutritional deficiencies. Heavy metal concentration both in leaves and fruit tissues was unaffected by treatments, with concentrations within international limits imposed for the human consumption. Results indicate how STW may be conveniently recycled as water source in the irrigation strategies of perennial species grown in temperate environments although this strategy may only contribute to partial fulfilment of plant nutrient requirements.

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.

Image processing through the implementation of manually coded algorithms has been adopted to detect fruit damage during post-harvest operations. This study tested convolution neural networks with “You Only Look Once” (YOLO) architecture using a commercial online platform to detect physiological disorders and ripening stage in ‘Abbe Fetel’ pear. Disorders such as superficial scald and the starch pattern index (SPI) were assessed. Three different models were trained to detect: I) individual fruit within the boxes; II) superficial scald or senescence scald on pear skin; III) the SPI value of pears was assessed using the Lugol solution. Preliminary statistics show that the model to count the fruit inside the boxes reached 64.70% of true positives with 0.5 intersection over union. The second had less accuracy (up to 20% of true positives) but maintained a good average precision (60%) with different confidence thresholds (40% and 20%). The third showed good performances compared to the Ctifl and Laimburg scales, with an F1 score of 0.36 and 0.59, respectively. The effectiveness of the transfer learning method was demonstrated. However, further image labelling and modelling research is needed to improve the accuracy of the simulations and to develop an application for portable devices for pre- and post-harvest factor mapping. These results could lead to improvements in the management of fruit boxes and thus help ensure good fruit quality for consumers.

This preliminary study investigated innovative materials as organic photovoltaic (OPV) plastic films for application in orchards. OPV, if placed above the hail net structure, might allow the orchard to become an energy source and to reduce its environmental impact. Moreover, thanks to their shading effect, OPV strips can reduce water needs of trees, enhancing orchard water use efficiency. This technology could facilitate the adoption of precision orchard management (POM), thanks to the availability of electrical power in the field, which may ease the use of sensing technologies. A small mock-up (about 12 m long) of an orchard row was built out of steel tubing, to support a standard hail net on either side of a single row of potted, 1-year-old apple trees. OPV materials were laid upon the hail net in 3 configurations (90, 60 and 30% of the hail net surface covered). Generation of electricity and plant physiological parameters were recorded. Only 90% coverage with OPV materials affected plant performance significantly, while no differences were found at 30 and 60% coverage, in respect to control (hail net only). The generation of electricity was low, due to low efficiency of conversion of the materials used. We are expanding this work by creating multi-function covers, that combine hail, rain, insect, light protection while generating electricity.

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 processing has recently been adopted for fruit damage detection in post-harvest operations. Through the implementation of hard-coded feature extraction algorithms, high accuracy has been found. The present study tested the fast and operational convolution neural networks with “YOLO v3” architecture using the online platform Supervise.ly to detect on pear fruit ‘Abbe Fetel’ physiological disorders such as superficial scald. Two different models were trained: I) one to detect the individual pear fruits within the batches; II) one to detect superficial scald or senescence scald on pear skin. Preliminary statistics show that the model to count the fruit inside the batches reaches an accuracy of 64.70% with a 0.5 of Intersection of Units. The second one has less accuracy (up to 20% of true positive) but maintains a good level of average precision (0.6) with different confidence thresholds (0.4 and 0.2). Further research is needed to improve the accuracy of both models and to map quality pre- and post-harvest. These results will help the packing house to manage fruit batches and to ensure good fruit quality for consumers.

This work investigates the different physiological responses induced by rootstock vigor on cherry trees, 'Black Star' grafted on semi-vigorous (CAB6P) and on semi-dwarfing (Gisela 6) rootstocks. Shoot and fruit growth were monitored during one season. The daily patterns of stem, leaf and fruit water potentials (Ψ) as well as leaf gas exchanges were assessed during post-veraison, while productivity and fruit quality were determined at harvest. Trees on Gisela 6 showed lower shoot growth rates and lower Ψstem and Ψfruit compared to trees on CAB6P, while no significant differences were found on Ψleaf, gas exchanges and fruit daily growth rates. As a consequence of the relative changes in Ψ, trees on Gisela 6 showed lower daily Ψ gradients between stem and leaves, which may have reduced shoot strength as a sink for water and carbohydrates and thus shoot growth, compared to CAB6P. On the contrary, a similar Ψ gradient between stem and fruit was recorded on Gisela 6, with likely positive consequences on fruit strength as a sink. This hypothesis is confirmed by the higher productivity and fruit soluble solid content found at harvest on trees grafted on Gisela 6. These results suggest that fruit on trees grafted on dwarfing rootstocks may increase their resource allocation toward shoots, thus leading to improved yield and fruit quality at harvest.

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.

•A red net shading 50% increases apple tree water use, limiting fruit growth.•A red net shading 50% is not advisable for apple production when water is limited.•A white net shading 50% lowers crop water use, representing a useful tool to save water.•A blue net shading 50% improves fruit growth, despite intermediate sap flow rates.