Qualitative and biochemical characteristics of pomegranate fruit grown using reclaimed water and low input fertigation treatments at harvest and during storage.

In recent years, severe climate change leading to by water scarcity reduced water quality has increased the need for effective irrigation strategies for agricultural production. Among these, the reuse of reclaimed water represents a non-expensive and reliable solution. The effect of conventional or reclaimed water, applying convention or smart fertigation system, were investigated during two irrigation seasons on yield, qualitative and biochemical traits of pomegranates fruit (cv Wonderful One) at harvest, and after storage at 7 °C. The results of this study showed that using reclaimed waters with different fertigation systems did not affect the pH values, total soluble solids, and titratable acidity on pomegranates fruit showing slight decrease changes only during postharvest storage. On the other hand, the respiration rate was not affected by water quality. Furthermore, the antioxidant activity was also preserved during storage in pomegranates fruit from plants irrigated with reclaimed water by applying conventional or smart fertigation. The analysis also identified 52 compounds by UHPLC-MSn and HPLC-UV-Vis analyses. A slight decrease (about 17 %) at harvest and during storage in polyphenols content was shown in fruit grown using reclaimed water. The study demonstrates that using reclaimed water is a sustainable and effective way to limit the use of conventional water for irrigating pomegranate crops without significant reduction in yield, or in qualitative and nutritional values of the fruit at harvest and during storage.

Architectural approach to evaluate the design and management of almond cultivars suitable for super high-density orchards.

Introduction: The almond tree is a major global nut crop, and its production has surged dramatically in recent years. Super high-density (SHD) planting systems, designed to optimize resource efficiency and enhance precocity, have gained prominence in almond cultivation. A shift in cropping systems toward sustainable intensification (SI) pathways is imperative, and so maximizing branching density within the canopies of SHD trees is crucial to establish and maintain productive potential, especially for hedge-pruned trees. This study investigates the influence of different almond cultivars grafted onto a novel growth-controlling rootstock on tree architectural and growth parameters in a SHD orchard. This open field research provided valuable insights for the development and application of new tools and methods to increase productivity and sustainability in almond growing. Methods: Three cultivars (Lauranne® Avijour, Guara Tuono, and Filippo Cea) were evaluated in Gravina in Puglia (BA) over a two-year period. Canopy growth parameters, such as canopy volume and trunk cross-sectional area, and architectural traits, like branching density, branching angle, number and length of subterminal shoots, and number of brachyblasts, were measured through qualitative and quantitative measurements. Results and discussion: Results revealed significant differences in tree height, canopy thickness, width, volume, and vigor among the cultivars. Architectural traits, including branch parameters, brachyblast parameters, and subterminal shoots, varied among the cultivars. Lauranne displayed a more compact well-distributed canopy and exhibited the lowest vigor. Filippo Cea showed the highest vigor and the greatest canopy volume. Tuono had a higher number of buds and bud density. The best ideotype for SHD orchards is a smaller tree, with high branching density and smaller trunk diameters, i.e. the vigor. Cv. Lauranne seemed to be the best cultivar, mostly with the lowest tree vigor of all the cultivars involved. These findings provide valuable insights for almond growers and breeders seeking to optimize orchard design and management for enhanced SHD orchards productivity and sustainability. Future research will explore the relationship between canopy architecture and yield parameters, considering different scion/rootstock combinations in different environmental conditions.

Predicting carob tree physiological parameters under different irrigation systems using Random Forest and Planet satellite images.

Introduction: In the context of climate change, monitoring the spatial and temporal variability of plant physiological parameters has become increasingly important. Remote spectral imaging and GIS software have shown effectiveness in mapping field variability. Additionally, the application of machine learning techniques, essential for processing large data volumes, has seen a significant rise in agricultural applications. This research was focused on carob tree, a drought-resistant tree crop spread through the Mediterranean basin. The study aimed to develop robust models to predict the net assimilation and stomatal conductance of carob trees and to use these models to analyze seasonal variability and the impact of different irrigation systems. Methods: Planet satellite images were acquired on the day of field data measurement. The reflectance values of Planet spectral bands were used as predictors to develop the models. The study employed the Random Forest modeling approach, and its performances were compared with that of traditional multiple linear regression. Results and discussion: The findings reveal that Random Forest, utilizing Planet spectral bands as predictors, achieved high accuracy in predicting net assimilation (R² = 0.81) and stomatal conductance (R² = 0.70), with the yellow and red spectral regions being particularly influential. Furthermore, the research indicates no significant difference in intrinsic water use efficiency between the various irrigation systems and rainfed conditions. This work highlighted the potential of combining satellite remote sensing and machine learning in precision agriculture, with the goal of the efficient monitoring of physiological parameters.

In-Field Automatic Identification of Pomegranates Using a Farmer Robot.

Ground vehicles equipped with vision-based perception systems can provide a rich source of information for precision agriculture tasks in orchards, including fruit detection and counting, phenotyping, plant growth and health monitoring. This paper presents a semi-supervised deep learning framework for automatic pomegranate detection using a farmer robot equipped with a consumer-grade camera. In contrast to standard deep-learning methods that require time-consuming and labor-intensive image labeling, the proposed system relies on a novel multi-stage transfer learning approach, whereby a pre-trained network is fine-tuned for the target task using images of fruits in controlled conditions, and then it is progressively extended to more complex scenarios towards accurate and efficient segmentation of field images. Results of experimental tests, performed in a commercial pomegranate orchard in southern Italy, are presented using the DeepLabv3+ (Resnet18) architecture, and they are compared with those that were obtained based on conventional manual image annotation. The proposed framework allows for accurate segmentation results, achieving an F1-score of 86.42% and IoU of 97.94%, while relieving the burden of manual labeling.