Net application in agriculture has a long history. Nets were usually used for the protection of plants against different hazards (hail, wind, birds, pests, excessive sun radiation) and, lately, from insects (nets with smaller mesh size). In recent years, photoselective netting technology has emerged, which adds desired plant responses caused by light quality changes to their basic protective properties. A combination of anti-insect and photoselective net technology (anti-insect photoselective nets) may present a notable contribution to the sustainable food production concept. Notable positive effects of this eco-friendly approach on agroecosystems are mainly achievable due to its non-pesticide pest protection of cultivated plants and, at the same time, promotion of special beneficial morphological and physiological plant responses. Although netting has been extensively studied over the last decade, there is a pronounced lack of publications and analyses that deal with their mode of action on fruit trees, which is especially true for new netting concepts. A better understanding of such mechanisms can lead to improved development and/or utilization of this technology and enhanced generation of value-added products. This review was based on a revision of the literature regarding netting in agriculture, with emphasis on fruit cultivation, and the following databases were used: Web of Science, ScienceDirect, Scopus, and Google Scholar. Although this study aims to comprehend a majority of fruit species, it narrows down to those usually net-protected and, hence, studied, such as apple, peach or nectarine, kiwifruit, blueberry, etc. Nets mainly differ in their mesh size and color, which are the parameters that mostly determine their capacity for light quantity and quality modification. Such light modifications, directly or indirectly (e.g., change in microclimate), initiate different fruit tree responses (in some cases, mechanisms) through which the final effect is realized on their vegetative and generative traits. For instance, some of them include a shade avoidance mechanism (initiated by changes in red to a far-red ratio, blue light levels, etc.), source–sink relationship, and carbohydrate availability (actualized by changes in photosynthesis efficiency, vegetative and generative growth, etc.), plant stress response (actualized by microclimate changes), etc. In most cases, these responses are interconnected, which contributes to the complexity of this topic and emphasizes the importance of a better understanding of it.

To date, many machine learning models have been used for peach maturity prediction using non-destructive data, but no performance comparison of the models on these datasets has been conducted. In this study, eight machine learning models were trained on a dataset containing data from 180 ‘Suncrest’ peaches. Before the models were trained, the dataset was subjected to dimensionality reduction using the least absolute shrinkage and selection operator (LASSO) regularization, and 8 input variables (out of 29) were chosen. At the same time, a subgroup consisting of the peach ground color measurements was singled out by dividing the set of variables into three subgroups and by using group LASSO regularization. This type of variable subgroup selection provided valuable information on the contribution of specific groups of peach traits to the maturity prediction. The area under the receiver operating characteristic curve (AUC) values of the selected models were compared, and the artificial neural network (ANN) model achieved the best performance, with an average AUC of 0.782. The second-best machine learning model was linear discriminant analysis with an AUC of 0.766, followed by logistic regression, gradient boosting machine, random forest, support vector machines, a classification and regression trees model, and k-nearest neighbors. Although the primary parameter used to determine the performance of the model was AUC, accuracy, F1 score, and kappa served as control parameters and ultimately confirmed the obtained results. By outperforming other models, ANN proved to be the most accurate model for peach maturity prediction on the given dataset.

Peaches (Prunus persica (L.) Batsch) are a popular fruit in Europe and Croatia. Maturity at harvest has a crucial influence on peach fruit quality, storage life, and consequently consumer acceptance. The main goal of this study is to develop a machine learning model that will detect the most important features for predicting peach maturity by first training models and then using the importance ratings of these models to detect nonlinear (and linear) relationships. Thus, the most important peach features at a given stage of its ripening could be revealed. To date, this method has not been used for this purpose, and at the same time, it has the potential to be applied to other similar peach varieties. A total of 33 fruit features are measured on the harvested peaches, and three imbalanced datasets are created using firmness thresholds of 1.84, 3.57, and 4.59 kg·cm⁻². These datasets are balanced using the SMOTE and ROSE techniques, and the Random Forest machine learning model is trained on them. Permutation Feature Importance (PFI), Variable Importance (VI), and LIME interpretability methods are used to detect variables that most influence predictions in the given machine learning models. PFI shows that the h° and a* ground color parameters, COL ground color index, SSC/TA, and TA inner quality parameters are among the top ten most contributing variables in all three models. Meanwhile, VI shows that this is the case for the a* ground color parameter, COL and CCL ground color indexes, and the SSC/TA inner quality parameter. The fruit flesh ratio is highly positioned (among the top three according to PFI) in two models, but it is not even among the top ten in the third.

This study attempts to clarify the effect of canopy position on the physico-chemical parameters of apples cv. Braeburn. The experiments were carried out on fruit from the inner and outer part of the canopy in two growing seasons and at two harvest dates. Light measurements revealed that the average value of photo active radiation (PAR) for the inside and outside canopy amounted to 30.3 μmol/m²/s and 133.7 μmol/m²/s, respectively. Production year and canopy position significantly influenced ground color parameters a*, b*, C*, and h°, while the harvest date influenced all color parameters studied. For additional (red blush) coloration, the production year significantly influenced only the L* parameter, harvest date influenced all color parameters, and canopy position influenced L, a*, and C*. Only the fruits of the second harvest date showed more intense additional (red blush) coloration. The production year significantly affected fruit mass, firmness, total soluble solids (SSC), titratable acidity (TA), SSC/TA ratio, DPPH radical scavenging assay (AOP), total phenolic content (TPC), and total flavonoid content (TFC). The harvest date significantly influenced fruit mass, SSC, TA, SSC/TA, AOP, TPC, and TFC. The canopy position significantly influenced SSC, TA, AOP, TPC, and TFC. Regarding mineral content, the production year significantly affected the content of Fe, Ni, Cu, and Ca and the K/Ca ratio. The harvest date significantly affected Fe, Cu, Sr, K and K/Ca. The canopy position affected Fe, Ni, Zn, Sr, Ca, and K/Ca ratio, with a clear significant trend regarding the effect of canopy position only for Ca content (first and second year of the second harvest date) and K/Ca ratio (first year of both harvest dates). PCA analyses identified distinguishing features between apples, with differences defined specifically by AOP, TPC, TFC, Rb, Sr, Ca, and K/Ca on the PC 1 and Mn, Fe, Ni, Cu, and Zn on PC 2.

Postharvest shelf life has always been one of the main shortcomings in the cultivation of perishable fruit, of which strawberries are one of the most important. Hence, this study aimed to maintain the postharvest quality of fully ripe strawberries with the utilization of sustainable and edible...

The effect of anti-insect photoselective yellow (Tenax (Italy); mesh size of 2.4 · 4.8 mm) and Stop Drosophila Normal (Artes Politecnica (Italy); mesh size of 0.90 · 1 mm) nets on the generative and vegetative traits of peach 'Suncrest' was studied at an orchard near the city of Čakovec, Croatia. Netting significantly affected some vegetative parameters (leaf surface, leaf length and leaf shape index) but there was no significant effect on productivity parameters (yield, yield efficiency, fruit mass and share of decayed fruit). Application of nets significantly affected b* and C* fruit background colour parameters and L*, b*, C*, h° fruit additional colour parameters. Nets significantly affected majority of inner fruit quality parameters (fruit firmness, titratable acidity and total soluble solids / titratable acidity ratio) as well as of bioactive compounds (total polyphenolic content, antioxidant activity, anthocyanin content and share of alkali-soluble pectin). Since application of yellow net only slightly reduced peach fruit quality (in contrast to control) it can be recommended for application as an anti-insect net. However, it cannot be said for Stop Drosophila Normal net because it more notably reduced quality parameters (especially additional fruit colour).