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BackgroundIn this research, the effects of exogenous application of certain biostimulants [amino acid (AA), humic acid (HA), fulvic acid (FA), and seaweed extract (SE)] on the fruit yield and quality, leaf mineral contents, and some critical physio-chemical characteristics of grapevine (Vitis vinifera L.) cv. ‘Yaghouti’ were investigated under well-watered (WW) and drought-stressed (DS) conditions.ResultsDrought stress caused a remarkable reduction in the weight of 20 berries and fruit yield, and meanwhile a marked increase in the titratable acidity (TA) and total soluble solid (TSS) content of fruits. Application of biostimulants, especially SE, enhanced the weight of 20 berries, fruit yield, and TSS content, and decreased TA in fruits of DS vines. Although drought stress had a negative effect on the chlorophyll content of grapevine, this effect was alleviated by the application of biostimulants, especially SE. Moreover, drought stress made the accumulation of abscisic acid (ABA), proline, total phenol, and soluble carbohydrates, the level of hydrogen peroxide (H2O2) and malondialdehyde (MDA), as well as the activity of guaiacol peroxidase (GPX) and catalase (CAT) enzymes increased in leaves. Application of biostimulants, especially SE, further increased the accumulation of ABA, proline, total phenol, and soluble carbohydrates and the activity of the antioxidant enzymes, but reduced the level of MDA and H2O2 in DS vines. Under drought stress conditions, concentrations of N, P, and K increased, and concentrations of Fe and Zn decreased; however, DS grapevines treated with biostimulants and especially SE accumulated a higher level of these mineral nutrients than CON vines.ConclusionIn sum, as evidenced by the study results, biostimulants have a high potential for promoting fruit yield and quality of grapevine in drought-prone regions.

Xanthoceras sorbifolium Bunge (XSB) is one of the most promising trees for sustainable development, the low fruit yield of which, however, has become a restriction in the fruit product industrialization. The contents of soluble sugar and starch in leaves, two-year-old leaf branch (TYLB), current-leaf branch (CLBr) and flowering branch (FlBr) and the relationship among those carbohydrates and fruit yields were measured to evaluate whether the demand of XSB fruit growth and carbohydrate supplement were synchronized. The contents of soluble sugar and starch in TYLB decreased from germination to leafing stage. The obvious fluctuation of the carbohydrates in the four plant organs were exhibited at flowering stage with the maximal soluble sugar content of 226.93 mg g −1 in leaf and maximal starch content of 82.23 mg g −1 in TYLB. The soluble sugar in FlBr and leaf decreased after fruit ripening, while the content in CLBr increased at that period. The starch contents in TYLB at leafing and fruit formation stages and FlBr at flowering stage exhibited the first three high positive coefficients to the large fruit yield. While the starch contents in leaf, CLBr and TYLB at leafing, flowering and fruit formation stages exhibited the first three high positive coefficients to the middle fruit yield. The results indicated that the shortage of carbohydrate to the fruit cluster at the flowering and fruitlet developing stages resulted in the fruitlet abscission. This study indicates that the loading capacity of nutrients in vegetative organs at the critical period of fruit development should be considered first to ensure the yield target of XSB fruit in the future.

The fruit supply chain (FSC) involves different stages that need to be planned at least two months in advance. Therefore, having a good fruit yield forecast with anticipation allows making timely correct decisions for providing the resources, transport, and cold storage contracts, among others. Therefore, fruit yield over or underestimation could cause important inefficiencies with regards to FSC. Because of its relevance, a method based on machine learning (ML) techniques that uses spectrometric vegetation data is proposed. This method, known as Spectrometry Based Method for Fruit Production Forecast (SBM-Fruit), allows exploring the georeferenced Normalized Difference Vegetation Index (NDVI), collected in different phenological stages, aiming to capture spatial and temporal dependency in the fruit yield forecast. In the first step of SBM-Fruit, several clusters are obtained in a clustering process using the georeferenced NDVI in all phenological stages as input, while, in the second step, two validation functions are used for determining the best clustering. Finally, in the third step, the predictor variables of the best clustering are incorporated into an artificial neural network (ANN) for predicting the fruit yield. The SBM-Fruit was applied to forecast table grape yield of an orchard located in the Valparaíso Region, Chile. The results show fruit yield estimations with mean errors around 0.013 percent for every spatial zone of the orchard, forecasted at least two months in advance. The use of the SBM-Fruit would allow FSC stakeholders to make better decisions, improving the coordination of the FSC stages, and reducing costs and fruit losses.

The hyperspectral imaging technology introduced in this study not only significantly enhances the accuracy of citrus yield prediction but also provides crucial data for determining the optimal harvesting time and managing pests and diseases. Recent advancements in drone technology and hyperspectral sensors have greatly expanded their applicability in the agricultural sector. This research utilizes these cutting-edge technologies to propose a new methodology for improving yield prediction accuracy in citrus orchards. In this study, we estimated the yield of Satsuma mandarins ( Citrus unshiu Markovich and Citrus reticulata Blanco) using a drone and hyperspectral sensor at three locations in Jeju Island. The collected data were pre-processed (corrected and processed into a mosaic), and dimensions were reduced using a minimum noise fraction. Twenty endmembers were extracted and classified into three groups (background, citrus leaves, and citrus) using the support vector machine (SVM) classification method. The overall accuracy and kappa coefficient, which represent classification accuracy, were 98.87% and 0.83, respectively. Thirty trees were randomly selected from three test areas (Citrus Research Institute, Sinheung-ri, and Odeung-dong test sites), and the pixel values of the extracted citrus fruits and actual weight values of the citrus fruits harvested per tree were compared and analyzed. The corresponding linear regression function was y = y0 + ax; where, a was 0.0555, y0 was 5.7358, and the R-squared value was 0.8099. The yields predicted by substituting the pixel values of citrus fruits into the function were 1,316.20, 12,151.74, and 3,903.56 kg at the Citrus Research Institute, Sinheung-ri, and Odeung-dong test sites, respectively. There was a difference of 5.00–13.30% compared to the actual yield of citrus fruits at each test site.

A two-year (2020-21) study was conducted to investigate the possibility of relying of ten-years old pear trees grown on sandy loam soil irrigated by drip on citric acid (CA), gibberellic acid (GA3) and humic acid (HA). The CA was applied at the concentrations of 500, 1000 and 1500 ppm, GA3 at 50, 100 and 150 ppm and HA at 3, 4 and 5%, whereas water spray was used as the control. The results of our study proved that CA, GA3 and HA improved the shoot length, shoot thickness, leaf area and leaf chlorophyll of pear as compared with the control. Moreover, they also positively increased the fruit set percentage and final yield of ‘Le Conte’ pear. The fruit weight, size and firmness were also improved under the influence of aforementioned treatments. The fruit soluble solids, total sugars, leaf nitrogen, leaf phosphorus and leaf potassium of pear were also enhanced as compared with the control. Additionally, spraying of GA3 at 150 ppm, as well as, HA at 5 and 4% were the superior treatments and showed the most significant impact on plant growth, yield, fruit quality and leaf mineral content of pear. This study provides a basis for the future elucidation of HA-, GA3- and CA-modulated molecular mechanisms in pear, which can make a significant contribution in the scientific community.

The rise in the productivity of sweet orange in Brazil has been related to the use of superior rootstocks and higher tree density, among other factors. In order to investigate whether the cropping system and the land use efficiency would benefit from more intensive cultivation, the performance of Valencia sweet orange was evaluated over nine years on four rootstocks, which induced contrasting vigor, at 513, 696 and 1000 trees·ha−1. Agronomic Institute of Campinas (IAC) 1697 and IAC 1710 citrandarins, and diploid and allotetraploid (4×) Swingle citrumelos were classified as semi-dwarfing, super-standard, standard, and dwarfing rootstocks, respectively. The fruit yield per tree was decreased at higher tree densities, notably for more vigorous rootstocks. Conversely, the cumulative productivity was increased over the evaluation period by 27% at 1000 trees·ha−1, irrespective of the rootstock, and the most vigorous rootstock resulted in 2.5 times higher production than the dwarfing one on average. Most fruit quality parameters were seldom influenced by the tree density, while the rootstock was a decisive factor in improving the quality and the soluble solids content. Dwarfing rootstocks allowed for harvesting 17% more fruit per minute by manual pickers. Because the tree row volume per area is lower with such rootstocks, even at higher tree density, spray volume can be reduced, although appropriate equipment should be developed for better spray coverage on smaller trees. Nine years after planting under strict vector control, the cumulative incidence of huanglongbing-symptomatic trees on IAC 1710 was double that on Swingle 4×. Taken together, the results suggested that the land use efficiency in the citrus industry can be further improved by planting vigorous rootstocks at moderate to high tree densities. Nevertheless, obtaining highly productive semi-dwarfing and dwarfing rootstocks is the sine qua non for making high-density pedestrian sweet orange orchards more profitable.

Eco-friendly pest control options are highly needed in food crop production systems to mitigate the hazards of synthetic chemical pesticides. Entomopathogenic fungal biopesticides—Metarhizium anisopliae strains ICIPE 20 (oil-formulation containing 1.0 × 109 conidia/mL) and ICIPE 69 (commercialized biopesticide known as Mazao Campaign®)—were evaluated against Tuta absoluta on tomato through inundative foliar spray and compared with the commonly used pesticide Dudu Acelamectin 5% EC (Abamectin 20 g/L + Acetamiprid 3%) and untreated plot. All the treatments were arranged in a randomized complete block design with three replicates. The field experiments were conducted for two consecutive cropping seasons in Mukono district, Uganda. Tuta absoluta infestation, injury severity on leaves and fruits, fruit yield loss, marketable fruit yield gain and cost–benefit ratio of the treatments were assessed. The results during both seasons showed a significant lower fruit yield loss in M. anisopliae ICIPE 20-treated plots compared to untreated plots, with a marketable fruit yield gain exceeding 22% and a cost–benefit ratio greater than 2.8 (BCR~3). Dudu Acelamectin 5% EC outperformed all the other treatments, but needs to be considered with caution due to its non-target effect and resistance development, whereas M. anisopliae ICIPE 69 performed the least well. In addition, the findings showed the high degree of efficacy and economic viability of these biopesticides as a potential T. absoluta control option in the field. However, it is important to further explore different formulations of these eco-friendly biopesticides, inoculum delivery approach, application frequency, their effectiveness in different agro-ecological zones and compatibility with commonly used pesticides in tomato production systems for sustainable management of T. absoluta.

To maintain a constant supply of fresh fruit from May to November, producers increase the area of strawberry cultivation under shelters and grow strawberries that repeat fruiting. An additional problem is the reduction of available pesticides caused by the recommendations of the European Green Deal. For these reasons, the authors undertook to compare cultivars to determine which had the best quality fruits and whichplant wasmost resistant to the most dangerous pests.The purpose of this study was to evaluate the effect of the method of plant protection on the health and quality of the fruit yield of three remontant strawberry cultivars grown in a soilless medium. This study evaluated fruit yield and fruit quality as well as the contribution of pathogens to yield losses. For this purpose, standard phytopathological methods were used to identify the causes of disease symptoms on the fruit. At the same time, laboratory tests were carried out on the quality of the harvested strawberries, i.e., firmness and acidity of the fruit, soluble solids content, and respiration rate. The applied protection methods had little effect on the marketable yield and fruit size but had a significant impact on reducing fruit losses caused by the most common diseases. The effectiveness of individual protection methods inreducing the incidence of the tested pathogens and the effect on fruit quality parameters depended on the cultivar and growing season.

• Regulated deficit irrigation was assessed in almond, peach and plum over 3 years.• Fruit-growth slowdown stages are appropriate periods to apply deficit irrigation.• Peach yields were unaffected under a regulated deficit irrigation of 75% ETC.• Regulated deficit irrigation of 50% ETC maintained yields of almond and plum.• Fruit quality improved under regulated deficit irrigation.The effects of regulated deficit irrigation (RDI) on the performance of almond cv. Tuono, peach cv. JH-Hall and plum cv. Stanley were assessed on the Saiss Plain (NW, Morocco) over three consecutive growing seasons (2011–2013). Irrigation treatments consisted of a control, irrigation applied to fully satisfy crop water requirements (100% ETC), and two RDI treatments, irrigation applied to 75% ETC (RDI-75) and 50% ETC (RDI-50). These three treatments were applied during fruit-growth slowdown periods corresponding to Stages II and III in almond and Stage II in peach and plum. Yield and fruit quality traits were determined. The effect of RDI differed between species. Yield and fruit size were reduced significantly only in peach under RDI-50. Fruit quality improved in this species in the first year of the experiment, with an increase of sugar/acid ratio and polyphenol content. Plum quality also improved but the effects were significant only in the second and third years. Similar results were recorded in almond kernel, but their epidermal grooves were deeper under RDI-50, and this may have affected their commercial value. It is concluded that water can be saved during the fruit-growth slowdown period by up to 25% in peach and 50% in almond and plum with improvements in fruit quality without affecting total yield.

Exact yield estimation of fruits on plants guaranteed fine and timely decisions on harvesting and marketing practices. Automatic yield estimation based on unmanned agriculture offers a viable solution for large orchards. Recent years have witnessed notable progress in computer vision with deep learning for yield estimation. Yet, the current practice of vision-based yield estimation with successive frames may engender fairly great error because of the double counting of repeat fruits in different images. The goal of this study is to provide a wise framework for fruit yield estimation in sequence images. Specifically, the anchor-free detection architecture (CenterNet) is utilized to detect fruits in sequence images from videos collected in the apple orchard and orange orchard. In order to avoid double counts of a single fruit between different images in an image sequence, the patch matching model is designed with the Kuhn–Munkres algorithm to optimize the paring process of repeat fruits in a one-to-one assignment manner for the sound performance of fruit yield estimation. Experimental results show that the CenterNet model can successfully detect fruits, including apples and oranges, in sequence images and achieved a mean Average Precision (mAP) of 0.939 under an IoU of 0.5. The designed patch matching model obtained an F1-Score of 0.816 and 0.864 for both apples and oranges with good accuracy, precision, and recall, which outperforms the performance of the reference method. The proposed pipeline for the fruit yield estimation in the test image sequences agreed well with the ground truth, resulting in a squared correlation coefficient of R2apple = 0.9737 and R2orange = 0.9562, with a low Root Mean Square Error (RMSE) for these two varieties of fruit.