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Accurate fruit detection is crucial for automated fruit picking. However, real-world scenarios, influenced by complex environmental factors such as illumination variations, occlusion, and overlap, pose significant challenges to accurate fruit detection. These challenges subsequently impact the commercialization of fruit harvesting robots. A tomato detection model named YOLO-SwinTF, based on YOLOv7, is proposed to address these challenges. Integrating Swin Transformer (ST) blocks into the backbone network enables the model to capture global information by modeling long-range visual dependencies. Trident Pyramid Networks (TPN) are introduced to overcome the limitations of PANet's focus on communication-based processing. TPN incorporates multiple self-processing (SP) modules within existing top-down and bottom-up architectures, allowing feature maps to generate new findings for communication. In addition, Focaler-IoU is introduced to reconstruct the original intersection-over-union (IoU) loss to allow the loss function to adjust its focus based on the distribution of difficult and easy samples. The proposed model is evaluated on a tomato dataset, and the experimental results demonstrated that the proposed model's detection recall, precision, F1 score, and AP reach 96.27%, 96.17%, 96.22%, and 98.67%, respectively. These represent improvements of 1.64%, 0.92%, 1.28%, and 0.88% compared to the original YOLOv7 model. When compared to other state-of-the-art detection methods, this approach achieves superior performance in terms of accuracy while maintaining comparable detection speed. In addition, the proposed model exhibits strong robustness under various lighting and occlusion conditions, demonstrating its significant potential in tomato detection. [ABSTRACT FROM AUTHOR]

Positioning of mango picking points is a crucial technology for the realisation of automated robotic mango harvesting. Herein, this study reported a visualised end-to-end system for mango picking point positioning using improved YOLOv8 architecture with object detection and instance segmentation, as well as an algorithm of picking point positioning. At first, the improved YOLOv8n model, incorporating the BiFPN structure and the SPD-Conv module, was utilised to enhance the detection performance of mango fruits and stems. This model achieved a detection precision of 98.9% in fruits and 97.1% in stems, with recall of 99.5% and 94.6% respectively. Then, the YOLOv8n-seg model was used for segment the stem ROI (Region of interest), leading to 81.85% in MIoU and 88.69% in mPA. Finally, a skeleton line of the stem region was obtained on the basis of the segmentation image, and a picking point positioning algorithm was developed to determine the coordinates of the optimal picking point. Subsequently, the positioning success rate of coordinates, absolute errors, and relative errors were calculated by comparing the automatic positioned coordinates with the manually positioned stem region. Experimental results indicated that this study achieved an average positioning success rate of 92.01%, with an average absolute error of 4.93 pixels and an average relative error of 13.11%. Additionally, the average processing time for processing 640 images using the picking point positioning system is 72.75 ms. This study demonstrates the reliability and effectiveness of positioning mango picking points, laying the technological basis for the automated harvesting of mango fruits. • Simultaneous detection of mango fruits and fruiting stems. • A picking point positioning algorithm is proposed based on instance segmentation. • Development of an end-to-end mango picking point positioning system. [ABSTRACT FROM AUTHOR]

The article proposes a real‐time apple picking method based on an improved YOLOv5. This method accurately recognizes different apple targets on fruit trees for robots and helps them adjust their position to avoid obstructions during fruit picking. Firstly, the original BottleneckCSP module in the YOLOv5 backbone network is enhanced to extract deeper features from images while maintaining lightweight. Secondly, the ECA module is embedded into the improved backbone network to better extract features of different apple targets. Lastly, the initial anchor frame size of the network is adjusted to avoid recognizing apples in distant planting rows. The results demonstrate that this improved model achieves high accuracy rates and recall rates for recognizing various types of apple picking methods with an average recognition time of 0.025s per image. Compared with other models tested on six types of apple picking methods, including the original YOLOv5 model as well as YOLOv3 and EfficientDet‐D0 algorithms, our improved model shows significant improvements in mAP by 1.95%, 17.6%, and 12.7% respectively. This method provides technical support for robots' picking hands to actively avoid obstructions caused by branches during fruit harvesting, effectively reducing apple loss. [ABSTRACT FROM AUTHOR]

Accurate fruit detection is crucial for automated fruit picking. However, real-world scenarios, influenced by complex environmental factors such as illumination variations, occlusion, and overlap, pose significant challenges to accurate fruit detection. These challenges subsequently impact the commercialization of fruit harvesting robots. A tomato detection model named YOLO-SwinTF, based on YOLOv7, is proposed to address these challenges. Integrating Swin Transformer (ST) blocks into the backbone network enables the model to capture global information by modeling long-range visual dependencies. Trident Pyramid Networks (TPN) are introduced to overcome the limitations of PANet's focus on communication-based processing. TPN incorporates multiple self-processing (SP) modules within existing top-down and bottom-up architectures, allowing feature maps to generate new findings for communication. In addition, Focaler-IoU is introduced to reconstruct the original intersection-over-union (IoU) loss to allow the loss function to adjust its focus based on the distribution of difficult and easy samples. The proposed model is evaluated on a tomato dataset, and the experimental results demonstrated that the proposed model's detection recall, precision, F1 score, and AP reach 96.27%, 96.17%, 96.22%, and 98.67%, respectively. These represent improvements of 1.64%, 0.92%, 1.28%, and 0.88% compared to the original YOLOv7 model. When compared to other state-of-the-art detection methods, this approach achieves superior performance in terms of accuracy while maintaining comparable detection speed. In addition, the proposed model exhibits strong robustness under various lighting and occlusion conditions, demonstrating its significant potential in tomato detection. [ABSTRACT FROM AUTHOR]

Edible coatings play a critical role in reducing postharvest losses during storage and supply chain of horticultural commodities. The present study evaluated the efficacy of different concentrations of moringa leaf extract (MLE) combined with carboxymethyl cellulose (CMC) edible coating in preserving the quality and extending the shelf life of "Hass" avocado. Fruit were harvested at different stages of maturity and evaluated by dry matter content. Different concentrations of moringa (8 and 16%) extracted with chilled ethanol (100%) and functionalized with CMC (5%), were used to treat the fruit. Treated fruit were then stored at 5.5 ± 1°C and 90 ± 5% RH for 28 days plus an additional 7 days at 23°C. The changes in physicochemical and biochemical fruit attributes were evaluated at weekly intervals. The application of moringa and CMC-based edible coatings preserved the phenolics, flavonoids, and antioxidant activity of "Hass" avocado. The treatments significantly (p < 0.05) reduced the loss of weight and firmness. Furthermore, treated fruits were found to have a delayed color change and reduction in sugar concentration, particularly mannoheptulose, compared to the control treatment. Therefore, edible coatings prepared by combining CMC and MLE could be the best alternative for substituting the currently used health-compromising synthetic chemicals. [ABSTRACT FROM AUTHOR]

Copyright of Food & Fermentation Industries is the property of Food & Fermentation Industries and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Cormus domestica (L.) is a monophyletic wild fruit tree belonging to the Rosaceae family, with well-documented use in the Mediterranean region. Traditionally, these fruits are harvested and stored for at least 2 weeks before consumption. During this period, the fruit reaches its well-known and peculiar organoleptic and texture characteristics. However, the spread of more profitable fruit tree species, resulted in its progressive erosion. In this work we performed proteomic and metabolomic fruit analyses at three times after harvesting, to characterise postharvest physiological and molecular changes, it related to nutritional and organoleptic properties at consumption. Results: Proteomics and metabolomics analysis were performed on fruits harvested at different time points: freshly harvested fruit (T0), fruit two weeks after harvest (T1) and fruit four weeks after harvest (T2). Proteomic analysis (Shotgun Proteomic in LC-MS/MS) resulted in 643 proteins identified. Most of the differentially abundant proteins between the three phases observed were involved in the softening process, carbohydrate metabolism and stress responses. Enzymes, such as xyloglucan endotransglucosylase/hydrolase, pectin acetylesterase, beta-galactosidase and pectinesterase, accumulated during fruit ripening and could explain the pulp breakdown observed in C. domestica. At the same time, enzymes abundant in the early stages (T0), such as sucrose synthase and malic enzyme, explain the accumulation of sugars and the lowering of acidity during the process. The metabolites extraction from C. domestica fruits enabled the identification of 606 statistically significant differentially abundant metabolites. Some compounds such as piptamine and resorcinol, well-known for their antimicrobial and antifungal properties, and several bioactive compounds such as endocannabinoids, usually described in the leaves, accumulate in C. domestica fruit during the post-harvest process. Conclusions: The metabolomic and proteomic profiling of the C. domestica fruit during the postharvest process, evaluated in the study, provides a considerable contribution to filling the existing information gap, enabling the molecular and phytochemical characterisation of this erosion-endangered fruit. Data show biochemical changes that transform the harvested fruit into palatable consumable product. [ABSTRACT FROM AUTHOR]

Robotic fruit harvesting has great potential to revolutionize agriculture, but detecting cherry tomatoes in farming environments still faces challenges in accuracy and efficiency. To overcome the shortcomings of existing cherry tomato detection methods for harvesting, this study introduces a deep-learning-based cherry tomato detection scheme for robotic harvesting in greenhouses using multimodal RGB-D perception and an improved YOLOv7-tiny Cherry Tomato Detection (YOLOv7-tiny-CTD) network, which has been modified from the original YOLOv7-tiny by eliminating the "Objectness" output layer, introducing a new "Classness" method for the prediction box, and incorporating a new hybrid non-maximum suppression. Acquired RGB-D images undergo preprocessing such as color space transformation, point cloud normal vector angle computation, and multimodal regions of interest segmentation before being fed into the YOLOv7-tiny-CTD. The proposed method was tested using an AGV-based robot in a greenhouse cherry tomato farming facility. The results indicate that the multimodal perception and deep learning method improves detection precision and accuracy over existing methods while running in real time, and the robot achieved over 80% successful picking rates in two-trial mode in the greenhouse farm, showing promising potential for practical harvesting applications. [ABSTRACT FROM AUTHOR]

Fruit size and sugar content are key determinants of fruit quality, influenced by environmental factors and agronomic practices and sink strength provided by the genetic potential. Strawberry (Fragaria ×ananassa) produces fruits arranged in inflorescences, whose growth is affected by carbon competition between them. The competitive ability is termed as sink strength, which can be quantified as the potential growth rate under sufficient resource supply and/or no carbon competition among sinks, referred to as non-limiting conditions. Most previous studies did not observe potential growth, thereby failing to adequately evaluate sink strength and to assess the influence of environmental factors and agronomic practices on fruit growth. This study aimed to investigate the potential growth of strawberry fruits and analyze its sink strength dynamics. Non-limiting conditions were established by removing flowers to one fruit per inflorescence in a greenhouse experiment with plants grown in soil and given water and nutrients through drip irrigation. Fruits were harvested every 5 days from 5 to 55 days after anthesis (DAA), measuring the size, weight, and concentrations of major soluble carbohydrates in strawberry (sucrose, glucose, and fructose) and starch. Sink strength was represented by absolute growth rate based on fruit weight, and its components, sink size and sink activity, were represented by weight and relative growth rate, respectively. Fruit volume and weight showed a gradual linear increase at 5 DAA and then rapidly increased, following a single sigmoid curve between 30 and 55 DAA. Fruits primarily accumulated glucose and fructose during early growth, shifting to sucrose after 35 DAA. Starch concentration peaked at 5 DAA and then exponentially decreased. Sink strength exhibited a single peak between 40 DAA and 45 DAA. Sink strength gradually increased with sink size until 30 DAA, whereas sink activity significantly decreased until 30 DAA. Thereafter, sink strength and sink activity exhibited a peak, whereas sink size continued to increase. These results suggest that the major determinant of sink strength was sink size during early fruit growth, shifting to sink activity during late growth. [ABSTRACT FROM AUTHOR]

Light downconversion films can modulate incident light wavebands on crops, converting less utilised wavebands in an efficient way. In this experiment, red (conversion of green into red light wavebands), pink (conversion of UV and green into blue and red light but to a smaller degree than red film), and blue (conversion of UV into blue light) light downconversion films were used to cover blackberry plants throughout all phenological stages (from leaf emergence to fruit harvesting). The plants' physiological and biometric performance, and fruit yield and quality were evaluated. Plants under blue and red films showed a higher net photosynthetic rate with +23.1% and +14.9%, respectively, and a higher stomatal conductance with +56.0% and +23.6%, respectively, with respect to controls, maintaining stability across stages, except for a decrease under the red film during fruiting. Both films significantly boosted the fruit yield, with the red film increasing the fruit number (+49.8%) and the blue film enhancing the berry shape (+10.7) and fresh weight (+36.6). Notably, no significant differences in nutraceutical quality, including total flavonoid and anthocyanin content, were observed. These findings suggest that light downconversion films, particularly red and blue films, can effectively enhance the photosynthetic performance and fruit production in blackberry plants without compromising the fruit quality. Future research on this topic should focus on balancing plant growth, fruit productivity, and enhancing fruit nutraceutical properties. [ABSTRACT FROM AUTHOR]

Bananas are economically important fruits, but they are vulnerable to mechanical damage during harvesting and transport. This study examined the effects of methyl jasmonate (MeJA) on the cell membrane integrity and membrane lipid metabolism of wounded banana fruits after harvest. The results showed that 10 and 50 μM MeJA treatments on mechanically wounded bananas significantly delayed ripening and senescence in comparison with the control. At the end of storage, MeJA-treated groups showed a significant reduction in electrolyte leakage and malondialdehyde content, indicating that MeJA protected cell membrane integrity. MeJA also led to a significant decrease in the activity of antioxidant enzymes, including lipoxygenase, diacylglycerol kinase, and lipid phosphate phosphatase. Furthermore, MeJA reduced phospholipase (C and D), phosphatidic acid, and diacylglycerol levels, as well as slowed down the decrease in phosphatidylcholine and phosphatidylinositol contents. Compared to the control, MeJA significantly downregulated the expression of MaPLDγ, MaPLDα, and MaPLDζ. Therefore, MeJA treatment could be a reliable method to delay the senescence of harvested banana fruits subjected to mechanical wounding. [ABSTRACT FROM AUTHOR]

The application of fruit harvesting robots is becoming increasingly mature. However, the utilization of fruit macroscopic mechanics as the picking threshold in the robotic end-effector may result in fruit damage, as the internal microscopic mechanics of the fruit may have varying stress thresholds. This work proposed a modeling method for the micro-analysis of tomato fruit peel based on image processing techniques. Leveraging programming techniques, optical microscopic images of fruit peel cell tissue are obtained and processed to extract the porous structure, enabling the subsequent reconstruction of geometric models. An enhanced generalized Maxwell model is employed in this study, wherein INP files were autonomously generated through programming and imported into Abaqus for microscale mechanical simulation analysis of fruit peel. Based on this methodology, it was possible to simulate the viscoelastic mechanical behavior of fruit peel solely through the optical tissue images. By examining the stress relaxation curves of fruit peel, a more comprehensive understanding of the microscopic mechanical behavior of the fruit was attained. This study contributed to providing a more precise reference standard for the gripping force threshold during robotic harvesting, thereby enhancing the efficiency of harvesting robot. [ABSTRACT FROM AUTHOR]

The precise detection of Chinese bayberry locations using object detection technology is a crucial step to achieve unmanned harvesting of these berries. Because of the small size and easy occlusion of bayberry fruit, the existing detection algorithms have low recognition accuracy for such objects. In order to realize the fast and accurate recognition of bayberry in fruit trees, and then guide the robotic arm to carry out accurate fruit harvesting, this paper proposes a detection algorithm based on an improved YOLOv7-tiny model. The model introduces partial convolution (PConv), a SimAM attention mechanism and SIoU into YOLOv7-tiny, which enables the model to improve the feature extraction capability of the target without adding extra parameters. Experimental results on a self-built Chinese bayberry dataset demonstrate that the improved algorithm achieved a recall rate of 97.6% and a model size of only 9.0 MB. Meanwhile, the precision of the improved model is 88.1%, which is 26%, 2.7%, 4.7%, 6.5%, and 4.7% higher than that of Faster R-CNN, YOLOv3-tiny, YOLOv5-m, YOLOv6-n, and YOLOv7-tiny, respectively. In addition, the proposed model was tested under natural conditions with the five models mentioned above, and the results showed that the proposed model can more effectively reduce the rates of misdetections and omissions in bayberry recognition. Finally, the improved algorithm was deployed on a mobile harvesting robot for field harvesting experiments, and the practicability of the algorithm was further verified. [ABSTRACT FROM AUTHOR]

The traditional shrub fruits harvesting method is manual picking, while the efficiency is low, which seriously restricts the development of Lycium barbarum L. industry. In order to mechanize the harvesting process of Lycium barbarum L. and improve the correct picking rate while reducing the damage rate of Lycium barbarum L. harvesting, it is very important to analyze the kinematic model of the fruit-bearing branch during vibration harvesting. Through the measurement and analysis of the natural characteristics and physical parameters of the branches, a simplified model of Lycium barbarum L. shrub fruit-bearing branch was built by Solidworks 2023 software, and the appropriate material properties were selected. Through modal analysis and harmonious response analysis, the response characteristics data of fruit-bearing branches of Lycium barbarum L. shrub were obtained. In Qinghai Nuomuhong Farm, the field vibration harvesting kinematic model feature analysis test was carried out, and the acceleration data of the vibration harvesting process were collected by using the acceleration sensor, and through the analysis of the frequency spectrum characteristics of the data, it was concluded that when the excitation frequency was maintained between 8 and 14 Hz, the Lycium barbarum L. fell off well and the picking rate can reach 97.56%, the efficiency can reach 6.88 pieces of fruit per second, and the branch damage was acceptable, which theoretically met the needs of harvesting. [ABSTRACT FROM AUTHOR]

This study assessed the metabolite content and bioactivities of Kakadu plum (KP) from Western Australia (WA). LC-MS/MS and UHPLC-PDA analyzed sugar, vitamin C, and ellagic acid (EA). Functional properties were evaluated by spectroscopic technique, agar well diffusion, and microplate dilution methods. WA KP exhibited higher total sugar (16.3 ± 1.0 g/100 g DW) and free ellagic acid (EA) (23.2 ± 1.7 mg/g DW), along with abundant vitamin C (25.20 ± 0.16 to 131.50 ± 0.20 mg/g DW) compared to Northern Territory KP fruits. The fruit showed strong antioxidant activities, α-glucosidase inhibition, and effectiveness against bacteria, with positive correlations to total phenolic content (TPC), vitamin C, and EA. These findings highlight WA KP's potential for functional foods and pharmaceuticals, emphasizing the importance of TPC, vitamin C, and EA in selecting high-quality fruit. [ABSTRACT FROM AUTHOR]

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Banana, also known as plátano in some places, is a fruit consumed and appreciated around the world. Its scientific name is Musa paradisiaca, belonging to the Musaceae family. It is native to Southeast Asia and is currently grown in 130 countries in tropical and subtropical regions. This fruit is harvested throughout the year; 75% is generated mainly in India, Ecuador, Brazil, Colombia, Costa Rica, and China. Post-harvest technology enables efficient processing, storage, transportation, and distribution while preserving the quality and safety of the fruit to reduce economic losses. Currently, challenges are being investigated for post-harvest treatments to minimize the environmental impact, reduce polluting emissions, and the requirement for less energy consumption. The most-used options for bananas are de-greening, atmospheric modification, coatings, and frigoconservation, which are important for achieving safe, healthy, and high-quality food in the XXI century. This review details the post-harvest mechanical damage, handling of environmental parameters (temperature and relative humidity), control of gases involved in storage and transport, wax treatment, coatings, the use of antifungal compounds, and packaging necessary for the export of the fruit. [ABSTRACT FROM AUTHOR]

Recent developments in affordable depth imaging hardware and the use of 2D Convolutional Neural Networks (CNN) in object detection and segmentation have accelerated the adoption of machine vision in a range of applications, with mainstream models often out-performing previous application-specific architectures. The need for the release of training and test datasets with any work reporting model development is emphasized to enable the re-evaluation of published work. An additional reporting need is the documentation of the performance of the re-training of a given model, quantifying the impact of stochastic processes in training. Three mango orchard applications were considered: the (i) fruit count, (ii) fruit size and (iii) branch avoidance in automated harvesting. All training and test datasets used in this work are available publicly. The mAP 'coefficient of variation' (Standard Deviation, SD, divided by mean of predictions using models of repeated trainings × 100) was approximately 0.2% for the fruit detection model and 1 and 2% for the fruit and branch segmentation models, respectively. A YOLOv8m model achieved a mAP50 of 99.3%, outperforming the previous benchmark, the purpose-designed 'MangoYOLO', for the application of the real-time detection of mango fruit on images of tree canopies using an edge computing device as a viable use case. YOLOv8 and v9 models outperformed the benchmark MaskR-CNN model in terms of their accuracy and inference time, achieving up to a 98.8% mAP50 on fruit predictions and 66.2% on branches in a leafy canopy. For fruit sizing, the accuracy of YOLOv8m-seg was like that achieved using Mask R-CNN, but the inference time was much shorter, again an enabler for the field adoption of this technology. A branch avoidance algorithm was proposed, where the implementation of this algorithm in real-time on an edge computing device was enabled by the short inference time of a YOLOv8-seg model for branches and fruit. This capability contributes to the development of automated fruit harvesting. [ABSTRACT FROM AUTHOR]

A gripper is the critical component of the robot end effector for the automatic harvesting of fruit, which determines whether the fruit can be harvested intact or undamaged. In this paper, a robotic gripper mechanism based on three-finger and variable-angle design is designed and analyzed for spherical or cylindrical fruit picking. Among the three fingers of the mechanical gripper, two fingers are rotatable through a pair of synchronous gears to ensure enough contact area for the grasping surfaces, which adapt to fruits of different sizes, such as cherry, loquat, zucchini, and so on. Furthermore, the mathematical relationship between gripper driving force and finger gripping force is obtained by the kinematic analysis of the gripper to realize stable grasping, and a grasping index is employed for the structural parameter optimization of our gripper. The grasping motion is analyzed, and the kinematic simulations are carried out, when the driving speeds of the gripper are 5 mm/s, 10 mm/s, and 15 mm/s, respectively. The system transfer function related to driving speed is obtained by curve fitting. Then, the grasping experiments are conducted with various spherical and cylindrical fruit, of which the weights are between 8 and 300 g and the diameters are from 9 to 122 mm. The experimental results demonstrate that our gripper has good kinematic performance and fruit adaptability. At the same time, the grasping is stable and reliable while no obvious damage appears on the fruit surface. [ABSTRACT FROM AUTHOR]

In the agricultural sector, utilizing robots for tasks such as fruit harvesting poses significant challenges, particularly in achieving accurate 6D pose estimation of the target objects, which is essential for precise and efficient harvesting. Particularly, fruit harvesting relies heavily on manual labor, leading to issues with an unstable labor supply and rising costs. To solve these problems, agricultural harvesting robots are gaining attention. However, effective harvesting necessitates accurate 6D pose estimation of the target object. This study proposes a method to enhance the performance of fruit-harvesting robots, including the development of a dataset named HWANGMOD, which was created using both virtual and real environments with tools such as Blender and BlenderProc. Additionally, we present methods for training an EfficientPose-based model for 6D pose estimation and ripeness classification, and an algorithm for determining the optimal harvest sequence among multiple fruits. Finally, we propose a multi-object tracking method using coordinates estimated by deep learning models to improve the robot's performance in dynamic environments. The proposed methods were evaluated using metrics such as A D D and A D D S , showing that the deep learning model for agricultural harvesting robots excelled in accuracy, robustness, and real-time processing. These advancements contribute to the potential for commercialization of agricultural harvesting robots and the broader field of agricultural automation technology. [ABSTRACT FROM AUTHOR]

Copyright of Revista Brasileira de Engenharia Agricola e Ambiental - Agriambi is the property of Revista Brasileira de Engenharia Agricola e Ambiental and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The general objective of this study was to evaluate response of selected watermelon growth and yield attributes to pig manure in Owerri, South Eastern Nigeria. Specifically, it determined response of watermelon to four rates of pig manure in terms of number of leaves produced per plant, vine length per plant, number of fruits harvested per plant and weight of fruits harvested per plant. The field experiments were conducted in 2020 and 2021 cropping seasons in the Teaching and Research Farm, Agricultural Science Department, Alvan Ikoku Federal University of Education, Owerri, Imo State, Nigeria. The investigation was carried out in a Randomized Complete Block Design with three replications. Treatments were composed of pig manure rates of 0, 5, 10, 15 tons per hectare. Parameters studied were number of leaves per plant, vine length per plant, number of fruits harvested per plant and weight of harvested fruits per plant. Data were subjected to Analysis of Variance (ANOVA) test and significant treatment means were separated using Least Significant Difference (LSD) protocol. Results obtained from the two experiments conducted show plants that received application of pig manure at the rate of 15 tons per hectare were outstanding in terms of vine length at 4weeks after planting (79.96 cm in 2020 and 58.93cm in 2021 ), 6 weeks after planting ( 162.46cm in 2020 and 89.73cm in 2021 ) and 8 weeks after planting (201.36cm in 2020 and 187.06 cm), number of leaves at 4 weeks after planting(25.50 in 2020 and 33.96 in 2021), 6 weeks after planting (28.50 in 2020 and 39.63 in 2021), 8 weeks after planting (31.10 in 2020 and 42.96 in 2021), number of fruits ( 4 fruits/plant in 2020 and 6 fruits/plant) as well as fruit weight(10kg in 2020 and 8kg in 2021) and is therefore to recommended for watermelon cultivation in Owerri, South Eastern Nigeria. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate the impact of harvesting stages (turning-color fruit and light red color) and postharvest treatments (distilled water, hot water at 35 °C, 10% Aloe vera, 2% CaCl2, 5% Mint, and 5% Catnip) for 5 min on the quality and storability of tomato fruits cv. Sangaw stored at 10 ± 1 °C and a relative humidity of 90%–95% for 20 days. Fruit harvested at the turning-color fruit stage presented significantly lower weight loss, greater firmness, and higher amounts of vitamin C, total phenol, and calcium (3.22%, 1118.31 g mm/s, 15.83 mg 100 g−1, 95.49 mg 100 mL−1 FW, and 0.14%, respectively). However, the tomatoes harvested from the light red color fruit stage presented the highest contents of total soluble sugars, total sugars, and lycopene (4.36%, 3.99%, and 41.49 mg kg−1, respectively). Notably, the postharvest treatment of tomato fruits with 2% CaCl2 significantly decreased weight loss and resulted in greater firmness, pH, total sugar, total phenol, and calcium contents (3.90%, 1212.39 g mm/s, 4.83, 3.85%, 95.60 mg 100 mL−1 FW, and 0.18%, respectively) than the control. Hence, coating with 10% Aloe vera resulted in the highest amount of total soluble solids and the highest amount of vitamin C. Tomato picked at the turning-color fruit stage and immersed in 5% Mint significantly lowered the loss of fruit weight, increased the total titratable acidity, and had the lowest content of lycopene. Additionally, the fruits harvested at the same stage and immersed in 2% CaCl2 retained greater firmness, total phenol content, and calcium content. On the other hand, fruits harvested in the light red stage and dipped in 5% Mint presented the highest total soluble sugars and total sugar contents. Finally, the harvested tomato fruits coated with 10% Aloe vera retained a relatively high level of vitamin C, indicating the storage life and quality of the tomato fruits. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Plant Production is the property of Egyptian National Agricultural Library (ENAL) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In China, most blueberry varieties are characterized by tightly clustered fruits, which pose challenges for achieving precise and non-destructive automated harvesting. This complexity limits the design of robots for this task. Therefore, this paper proposes adding a jetting step during harvesting to separate fruit clusters and increase the operational space for mechanical claws. First, a combined approach of flow field analysis and pressure-sensitive experiments was employed to establish design criteria for the number, diameter, and inclination angle parameters of two types of nozzles: flat tip and round tip. Furthermore, fruit was introduced, and a fluid–structure coupling method was employed to calculate the deformation of fruit stems. Simultaneously, a mechanical analysis was conducted to quantify the relationship between jet characteristics and separation gaps. Simulation and pressure-sensitive experiments show that as the number of holes increases and their diameter decreases, the nozzle's convergence becomes stronger. The greater the inclination angle of the circular nozzle holes, the more the gas diverges. The analysis of the output characteristics of the working section indicates that the 8-hole 40° round nozzle is the optimal solution. At an air compressor working pressure of 0.5 MPa, force analysis and simulation results both show that it can increase the picking space for the mechanical claw by about 5–7 mm without damaging the blueberries in the jet area. The final field experiments show that the mean distance for Type I (mature fruit) is 5.41 mm, for Type II (red fruit) is 6.42 mm, and for Type III (green fruit) is 5.43 mm. The short and curved stems of the green fruit are less effective, but the minimum distance of 4.71 mm is greater than the claw wall thickness, meeting the design requirements. [ABSTRACT FROM AUTHOR]

BACKGROUND: Late‐ripening citrus plays an important role in the stability of the global citrus industry. However, the regreening phenomenon in Valencia oranges impacts the peel color and commercial value. Ethylene degreening is an effective technique to improve the color of citrus fruits, but this effect may be delayed in regreened oranges. To better clarify this phenomenon, plastid morphology, pigment and phytohormone content in ethephon‐degreened Midknight Valencia oranges harvested in different stages were evaluated. RESULTS: Results showed that in fruits harvested at the turning stage, ethephon degreening treatment induced a chloroplast‐to‐chromoplast transition, and chlorophyll degradation and carotenoid accumulation were accelerated. Conversely, in fruits harvested at the regreening stage, the changes in plastid morphology were minimal, with delayed changes in chlorophyll and carotenoids. Genes related to ethylene biosynthesis and signaling pathways supported these responses. Variations in endogenous auxin, jasmonic acid, abscisic acid and gibberellins could partially explain this phenomenon. CONCLUSION: The response of Midknight Valencia oranges to ethephon degreening was delayed in the regreening stage, possibly due to the dynamic variations in endogenous phytohormones. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Copyright of Revista Brasileira de Engenharia Agricola e Ambiental - Agriambi is the property of Revista Brasileira de Engenharia Agricola e Ambiental and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: The harvest of fruit can be significantly advanced with the thriving development of intelligent and automated robot technologies. Nevertheless, the picking success rate of tomato fruit still requires improvement as some fruits are unexpectedly damaged inside, which is imperceptible by machine vision. Herein, a modelling method based on modified Voronoi algorithm is proposed to reconstruct the cellular structure of tomato pericarp. Results: Based on the reconstructed micro‐model, the compression physical behaviour of the pericarp cells is simulated to observe internal local stress and potential damage. It is revealed that the simulation result for pericarps of tomatoes with different ripeness is highly consistent to the experimental tests, which has well validated the feasibility of this modelling and simulation method. Conclusion: A Voronoi‐based modelling method is proposed for micro‐reconstruction of tomato pericarp, and the corresponding compression simulation results agree well with the experimental tests. Such result can be utilized as reference to improve the grasping force control for harvesting robot to avoid invisible damage induced by accident overload issue. With the predicting result, superior success rate can be achieved to enhance robot performance. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Fermenting fruit juices with lactic acid bacteria (LAB) is a sustainable method to enhance fruit harvests and extend shelf life. This study focused on blackberries, rich in antioxidants with proven health benefits. In this research, we examined the effects of fermentation (48 h at 37 °C) at 28 days on whey-supplemented (WH, 1:1) blackberry juice (BJ) inoculated with two LAB mixtures. Consortium 1 (BJWH/C1) included Levilactobacillus brevis, Lactiplantibacillus plantarum, and Pediococcus acidilactici, while consortium 2 (BJWH/C2) comprised Lacticaseibacillus casei and Lacticaseibacillus rhamnosus. All of the strains were previously isolated from aguamiel, pulque, and fermented milk. Throughout fermentation and storage, several parameters were evaluated, including pH, lactic acid production, viscosity, stability, reducing sugars, color, total phenolic content, anthocyanins, and antioxidant capacity. Both consortia showed a significant increase in LAB count (29–38%) after 16 h. Sample BJWH/C2 demonstrated the best kinetic characteristics, with high regression coefficients (R2 = 0.97), indicating a strong relationship between lactic acid, pH, and fermentation/storage time. Despite some fluctuations during storage, the minimum LAB count remained at 9.8 log CFU/mL, and lactic acid content increased by 95%, with good storage stability. Notably, sample BJWH/C2 increased the total phenolic content during storage. These findings suggest that adding whey enhances biomass and preserves physicochemical properties during storage. [ABSTRACT FROM AUTHOR]

Mechanized harvesting of fruit has been the ever-increasing trend in smart agriculture. However, the picking quality has been confined to the commonly-used unstructured planting for fruit trees. The robots can easily encounter the collisions with the dense branches and leaves during harvesting. The random spatial distribution of orchard obstacles can be highly influenced by the variation in the lighting conditions. It is very challenging on the real-time reconstruction of obstacles. In this study, a real-time reconstruction of orchard obstacles was proposed to combine the semantic segmentation and 3D point cloud analysis. Firstly, taking guava orchard as an example, the image acquisition was carried out using Intel RealSense D435i depth camera at a distance of about 15-50 cm from the guava fruit trees with a resolution of 640×480 in the orchard of Haiou Island Jiashuo Farm, Guangzhou City, Guangdong Province, China. Data enhancement was performed to improve the generalization and robustness of the model, according to the mirroring, luminance enhancement, luminance attenuation, adding Gaussian noise and pretzel noise. A total of 1,250 samples were obtained to randomly divided into the training, the validation and the test set in a ratio of 6:3:1, where there were 750 samples in the training set and 375 samples in the validation set; The test set was 125 samples. Then, a real-time semantic segmentation model was built using DeepLabV3+. The CoT module was introduced on the low-level feature map and high-level feature map of the encoder, with the MobileNetV3-large as the backbone network. The segmentation accuracy was improved on the thin obstacles like twig groups. The detail information of feature maps was strengthened to suppress the useless information. Furthermore, an edge-assisted loss function was proposed using the Sobel operator. Laplace edge loss function was used to further increase the accuracy of semantic segmentation, in order to improve the accuracy of picture edge segmentation. Next, the intrinsic parameters of the depth camera were combined to transform the semantic map of obstacles into a 3D point cloud. Statistical analysis was applied to remove the outliers. The voxelization was used to approximate the reconstruction of tree trunks and branches. Moreover, Euclidean clustering was employed to identify the individual 3D fruits. Among them, the minimum bounding box of the fruit was served as the reconstruction. Finally, the pose change matrix between the camera and the robot was obtained using the hand-eye calibration with a chessboard pattern. The minimum bounding box of the fruit and the voxelized cubes of branches and tree trunks were transformed into the working space of robot picking. The experimental results show that the MIoU (mean intersection over union) of obstacles in MobileNetV3-large-DeepLabV3+ was improved from 69.7% to 74.3% after the introduction of the CoT block, indicating an increase of 4.6 percentage points; The Sobel algorithm also considered both horizontal and vertical gradient information, indicating some anti-noise performance. The MIoU of the obstacle was further improved from 74.3% to 77.2%, indicating an improvement of 2.9 percentage points. The overall MIoU of the improved model was 7.0 percentage points, 0.6 percentage points, and 1.0 percentage points higher than those of Xception-DeepLabV3+, BranchNet, and LRASPP, respectively. There was 1.7 percentage points lower than BiSeNetV3; The ACC was 3.1 percentage points, 0.5 percentage points, and 0.6 percentage points higher than those of Xception-DeepLabV3+, BranchNet, and LRASPP, respectively, while there was 2.2 percentage points lower than BiSeNetV3; The FPS of the improved model was as high as 78.8 frames per second, which was faster than the comparison; The number of parameters was 4.4 M, which is 8% and 31% of Xception-DeepLabV3+ and BiSeNetV3, respectively, while BranchNet and LRASPP are 68% and 73% of the parameters in this paper, respectively. Besides, the IoU (intersection over union) values of the voxelized cubes for the fruit and branches were 65.7% and 56.6%, respectively, and the voxelization time was 0.34 s and 0.84 s, respectively. The better robustness and real-time performance can fully meet the obstacle avoidance requirements of guava picking robots. The real-time semantic segmentation of irregular obstacles was optimized to utilize more sophisticated attention modules or backbone networks. The finding can provide the better ways to enhance the effects of obstacle reconstruction. [ABSTRACT FROM AUTHOR]

Plastic covers have been used to prevent environmental constraints negatively affecting sweet cherry production in Southern Chile. However, less information is available on agronomic practices and their effects on fruit quality in sweet cherry covered orchards. Thus, in this study, we evaluated the impact of fruit load regulation on cherries' antioxidant-related parameters and the quality and condition at harvest and postharvest in sweet cherry (Prunus avium) cv. Regina that was cultivated under a plastic cover in Southern Chile. For this, four fruit load treatments were manually applied—(i) 100% fruit load (the control), (ii) 80% fruit load, (iii) 60% fruit load, and (iv) 40% fruit load—in a commercial sweet cherry orchard for two seasons (2021/2022 and 2022/2023). The results revealed that the yield and fruit load were not significantly different between the treatments. Interestingly, the 60% and 40% fruit loads increased the fresh weight, fruit size, and firmness (20.3%) compared to the control (the 100% fruit load) during both seasons. Likewise, the 60% and 40% fruit load treatments exhibited the highest fruit size distribution of 30 mm, while the 100 and 80% fruit load treatments showed the highest fruit distribution with fruit sizes between 28 mm and 24 mm. The total soluble solids (TSSs) did not vary among the fruit load treatments, while a significant increase was found in the titratable acidity (TA) in the 60 and 40% fruit load treatments during both seasons. No significant differences in antioxidant activity (AA) and total phenols (TPHs) among the treatments were observed during both seasons. Overall, the results revealed that the fruit load treatments, mainly 40%, increased the fruit weight and firmness and reduced pitting in fruits by 39.4% at postharvest. Thus, fruit thinning might be an important agronomical practice to regulate fruit load, positively affecting fruit quality at harvest and during postharvest storage in sweet cherry cv. Regina cultivated under a plastic cover. However, more biochemical and molecular studies are needed to elucidate the mechanism involved in this improvement. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To examine how different harvesting periods affect the storage quality of 'Satsuma' plum fruits, this study analyzed 13 quality and physiological metrics post-harvest and utilized principal component analysis (PCA) and mathematical models to assess (0±0.5)°c low temperature storage quality across three different harvesting periods (100 d after flowering, 103 d after flowering, 106 d after flowering). The results showed that harvesting at 103 days after full bloom led to the best preservation effect. Additionally, the hardness and total acidity of plum fruit decreased by 37.37% and 27.06% respectively until the end of the storage period, which was the slowest. Furthermore, changes in relative conductivity, anthocyanins, total soluble solids, malondialdehyde, total phenols, and flavonoids were relatively smooth. The activity of polyphenol oxidase was low, while peroxidase, superoxide dismutase, and catalase activities were high, specifically catalase activities. After conducting PCA analysis, it was determined that PC1, PC2, and PC3 accurately capture 87.472% of the relevant information. These results suggested that varying harvesting periods would have significant impacts on all indicators. The mathematical model analysis revealed that the plum fruits harvested at 100, 103, and 106 days after bloom, and stored for 56 days, had comprehensive scores of 1.06, 0.99, and 2.69, respectively. Lower comprehensive scores indicated better quality, so the order of preservation effect was as follows: 103 days after bloom>100 days after bloom>106 days after bloom. The study provides a theoretical framework for evaluating postharvest preservation and quality of plum fruits, along with technical parameters to enable timely harvesting. [ABSTRACT FROM AUTHOR]

The production of Lycium barbarum L. is a labor-intensive industry. Multiple manual harvests are required during the harvesting season, which contributes to the high harvesting costs. The cultivation conditions of L. barbarum were investigated to increase efficiency and mitigate harvesting damage. A torsion harvester was designed according to the characteristic of infinite inflorescence and the distribution of detachment force, and the kinematics model of the harvester was established. The vibration responses of ripe and unripe fruit were obtained through ADAMS simulation of the branch model, and the influencing factors and value range of the torsion harvester were also determined. The mathematical models of ripe fruit harvesting rate, unripe fruit harvesting rate, ripe fruit damage rate and torsion angle, vibration rods distance, and vibration frequency were established by the Box-Behnken test. The influences of various factors on ripe fruit harvesting rate, unripe fruit harvesting rate, and ripe fruit damage rate were analyzed, and the best parameter combination was obtained: torsion angle 73.66°, vibration rods distance 35.51 mm and vibration frequency 19.12 Hz. Field experiment showed that the harvesting rate of ripe fruit is 95.67%, the harvesting rate of unripe fruit is 4.68%, and the damage rate of ripe fruit is 3.70%. The research results can promote the mechanization process of L. barbarum harvest, and provide a reference for vibration harvest of berries. [ABSTRACT FROM AUTHOR]

Dropping during transportation is a critical issue for tomato fruits, as it triggers ethylene production and affects quality parameters, leading to lower quality and a reduced storage life. Thus, this study was conducted to assess the physiological alterations in tomato fruits subjected to dropping. This study involved tomatoes harvested at green and red stages, subjected to the following five dropping treatments: 0 cm, 10 cm, 30 cm, 50 cm, and 100 cm. The results revealed that dropping from 100 cm induced the highest ethylene production, particularly in green fruits, where production began within one hour and peaked within 48 h. Red fruits exhibited a dose-dependent response to mechanical stress, with a notable decrease in ethylene production starting from the second week post-dropping, suggesting a regulatory mechanism. CO2 production peaked at 350.1 µL g−1 h−1 in green fruits and 338.2 µL g−1 h−1 in red fruits one day after dropping from 100 cm. Dropping also significantly influenced fruit color, firmness, electrolyte leakage, and vitamin C content. Principal component analysis (PCA) revealed distinct changes in metabolite profiles, with methionine and ACC (1-aminocyclopropane-1-carboxylate), key ethylene precursors, increasing in response to dropping, particularly in red fruits. These findings underscore the critical role of mechanical stress in modulating fruit physiology, with implications for post-harvest handling practices aimed at enhancing fruit quality and shelf life. [ABSTRACT FROM AUTHOR]

Tomatoes are a critical economic crop. The realization of tomato harvesting automation is of great significance in solving the labor shortage and improving the efficiency of the current harvesting operation. Accurate recognition of fruits is the key to realizing automated harvesting. Harvesting fruit at optimum ripeness ensures the highest nutrient content, flavor and market value levels, thus maximizing economic benefits. Owing to foliage and non-target fruits obstructing target fruits, as well as the alteration in color due to light, there is currently a low recognition rate and missed detection. We take the greenhouse tomato as the object of research. This paper proposes a tomato recognition model based on the improved YOLOv8 architecture to adapt to detecting tomato fruits in complex situations. First, to improve the model's sensitivity to local features, we introduced an LSKA (Large Separable Kernel Attention) attention mechanism to aggregate feature information from different locations for better feature extraction. Secondly, to provide a higher quality upsampling effect, the ultra-lightweight and efficient dynamic upsampler Dysample (an ultra-lightweight and efficient dynamic upsampler) replaced the traditional nearest neighbor interpolation methods, which improves the overall performance of YOLOv8. Subsequently, the Inner-IoU function replaced the original CIoU loss function to hasten bounding box regression and raise model detection performance. Finally, the model test comparison was conducted on the self-built dataset, and the test results show that the mAP0.5 of the YOLOv8-Tomato model reached 99.4% and the recall rate reached 99.0%, which exceeds the original YOLOv8 model detection effect. Compared with faster R-CNN, SSD, YOLOv3-tiny, YOLOv5, and YOLOv8 models, the average accuracy is 7.5%, 11.6%, 8.6%, 3.3%, and 0.6% higher, respectively. This study demonstrates the model's capacity to efficiently and accurately recognize tomatoes in unstructured growing environments, providing a technical reference for automated tomato harvesting. [ABSTRACT FROM AUTHOR]

In the Vase system, the most common training system for peach-growing countries for more than a century, light distribution to the canopy is uneven, and access to the canopy for pruning, thinning, and harvest labor is difficult. It is important to identify alternative systems to the Vase system considering the cultivar and growing environment to facilitate labor and enhance productivity and quality. In Türkiye, one of the important centers of peach growing worldwide, detailed research has yet to be published on the applicability of training systems alternative to the widely used Vase system. Therefore, this study aimed to evaluate the effect of different training systems (Vase, Catalan Vase, Quad-V, Tri-V) on growth, yield, fruit quality, and labor costs of peach cultivars (ExtremeVR 314, ExtremeVR 436, ExtremeVR 568). The experiment was conducted from 2017 to 2022. Although the distance between rows in all training systems is 5 m, the distance between trees on the row is determined as 4 m in Vase, 3 m in Catalan Vase, 2.5 m in Quad-V, and 2 m in Tri-V. In the experiment, vegetative development parameters, such as canopy volume, trunk sectional area, and the amount of winter pruning weights, differed according to the training system. In the final year, the Vase system, which produces the most pruning weight, generates 48.0% more pruning weight compared with the Tri-V system, which produces the least. Concerning yield per tree and hectare, trained to the Vase system yielded higher fruit per tree regardless of cultivar, while the Quad-V and Tri-V systems yielded more fruit per hectare. The training system and cultivar affected the fruit size; the largest fruits were obtained from the ExtremeVR 568 cultivar trained according to the Vase system. The most time needed for winter pruning was obtained from the Vase (79.4 min/tree) system, and the Tri-V (57.4 min/tree) and Quad-V (60.3 min/tree) systems required the least time. The Catalan Vase (31.1 min/tree) system required the least time for summer pruning. The most fruit harvest in an hour was obtained from the trees trained according to the Tri-V (164.5 kg/h) and Quad-V (132.02 kg/h) systems. These results suggest that Quad-V and Catalan Vase systems performed well and could be alternatives to the Vase system. [ABSTRACT FROM AUTHOR]

Lycium barbarum, a plant belonging to the Solanaceae family, is widely used in China due to its abundant nutritional value. Although the current mechanized harvesting method of L. barbarum has effectively minimized production expenses, it continues to have the challenge of inconsistent quality of the produced L. barbarum. The objective of this paper is to evaluate the correlation of the separating force and hardness concerning the timing of harvesting, maturity, and variety. Thus, the optimal time for harvesting ripe L. barbarum can be determined to enhance the quality of selectively mechanized harvesting of this fruit. The experiment was conducted in a L. barbarum plantation located in Qinghai Province during the 2023 harvest period. Two occasions were studied focusing on the primary cultivars Ningqi No. 1 and Ningqi No. 7, examining the three ripening stages of L. barbarum harvested at various times throughout the day. The finding of this study showed that the separation force and hardness of L. barbarum fruits were influenced by the harvesting time, the fruit variety, and the level of maturity. The optimal timing for harvesting different types of L. barbarum varies. It was observed that Ningqi No.1 was best to be harvested in the late afternoon and evening (17:00–21:00), whereas Ningqi No.7 was most suitable to be harvested in the morning (7:00–9:00). [ABSTRACT FROM AUTHOR]

Pumpkins (Cucurbita moschata Duchesne ex Poir) are extensively cultivated in Brazil, gaining prominence due to their importance in nutrition and potential health benefits. The objective of this study was to investigate the influence of harvest time and storage conditions on the postharvest quality and contents of bioactive compounds in 'Brasileirinha' pumpkin. The experimental design employed was a completely randomized design in a 3 × 6 factorial scheme, involving three harvest times (70, 90, and 110 days after planting) and six storage periods (0, 7, 14, 21, 28, and 35 days), with five replicates. The results highlighted variations in the external fruit brightness during storage, with a notable increase at 70 days after planting (DAP) and a consistent decrease in fruits harvested at 110 DAP. The a and b coordinates of the outer skin indicated that the harvest time influenced fruit ripening, with fruits harvested at 110 DAP showing higher values, suggesting a more advanced ripening stage. Fruit firmness decreased over storage time, regardless of the harvest time. Fruits harvested at 110 DAP exhibited higher soluble solids content, while pH increased during storage. Bioactive compounds, such as the ascorbic acid content, increased during storage, being higher in fruits harvested at 110 DAP. Total chlorophyll decreased, while carotenoids increased with storage, especially in fruits harvested at 110 DAP. Phenolic compounds and flavonoid varied with harvest time and storage, emphasizing the importance of considering these factors in assessing the nutritional quality of 'Brasileirinha' pumpkin. This study provided valuable information about changes in the quality and composition of bioactive compounds in 'Brasileirinha' pumpkin during storage, with an emphasis on the influence of harvest time. These results have significant implications for the food and nutrition industry, aiming to optimize the use of this vegetable as a healthy nutrient source in the human diet. [ABSTRACT FROM AUTHOR]

Avocado fruits are considered unusual because of the large amounts of oil and seven-carbon (7-C) carbohydrates (mannoheptulose and perseitol) in the fruit's flesh and skin. The fruit may be held on the tree unripe until required for marketing, and in some producing regions, this may extend past the next flowering period. This prolonged period on the tree is associated with increased oil content and decreased 7-C carbohydrates. There has been relatively less research into soluble hexose sugars and starch. In this research, the inter-relationships between fruit maturation, storage, and ripening have been investigated for both 7-C and six-carbon non-structural carbohydrates using 'Hass' fruit harvested from the same trees between 11 and 14 months after flowering. Significant differences were identified in both fruit flesh and skin for most compounds, affected by maturity, storage, and ripening. It is concluded that the non-structural carbohydrate composition of 'Hass' fruit is variable, with significant changes occurring associated with maturation, storage, and ripening. The compositions of the flesh and skin tissues are not consistently proportionate. Maturation provides the initial baseline composition from which any further change through storage or ripening can occur. The changes with maturation appear to be associated with the tree's phenology, with tree-to-tree differences in the timing or degree of change. [ABSTRACT FROM AUTHOR]

During postharvest cold storage, fruit sustains physical and biochemical changes, which may result in physiological rind disorders that consequently affect the fruit's marketability. Preharvest conditions are known to affect postharvest fruit quality, with the effect of preharvest shade netting being currently unknown. To this end, 'Nadorcott' mandarin (Citrus reticulata Blanco) fruit, grown under shade netting and without it, was harvested during two consecutive seasons from an orchard in Citrusdal, Western Cape, South Africa. The fruit was evaluated prior to, as well as after a 7-day shelf-life period following cold storage at either −0.6 or 4 °C for 14, 27 and 34 days, respectively, for changes in rind and pulp colour, rind carotenoids, soluble solid content (SSC), citric acid content and SSC/citric acid ratio. Weight loss and the incidence of rind physiological disorders (staining) were also recorded. The results showed that shade net did not affect the storage behaviour of the fruit, as no treatment differences were seen. However, a storage duration effect for both treatments was evident in some internal and external quality parameters, viz., weight loss percentage and carotenoid content increased over the storage duration. Inconsistency regarding the storage duration effect on the rind colour, SSC and acid content were evident between seasons, at both temperatures. Staining only occurred in the first season after 34 days at both temperatures. The results indicated that shade netting had no negative effect on the cold-storage behaviour of 'Nadorcott' fruit. However, contrasting findings from this study emphasized that the condition of the fruit at harvest plays a significant role in the postharvest behaviour of the fruit during cold storage, and different growing conditions may also be a contributable factor. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The cultivation model for spindle-shaped apple trees is widely used in modern standard apple orchards worldwide and represents the direction of modern apple industry development. However, without an effective obstacle avoidance path, the robotic arm is prone to collision with obstacles such as fruit tree branches during the picking process, which may damage fruits and branches and even affect the healthy growth of fruit trees. To address the above issues, a three-dimensional path -planning algorithm for full-field fruit obstacle avoidance harvesting for spindle-shaped fruit trees, which are widely planted in modern apple orchards, is proposed in this study. Firstly, based on three typical tree structures of spindle-shaped apple trees (free spindle, high spindle, and slender spindle), a three-dimensional spatial model of fruit tree branches was established. Secondly, based on the grid environment representation method, an obstacle map of the apple tree model was established. Then, the initial pheromones were improved by non-uniform distribution on the basis of the original ant colony algorithm. Furthermore, the updating rules of pheromones were improved, and a biomimetic optimization mechanism was integrated with the beetle antenna algorithm to improve the speed and stability of path searching. Finally, the planned path was smoothed using a cubic B-spline curve to make the path smoother and avoid unnecessary pauses or turns during the harvesting process of the robotic arm. Based on the proposed improved ACO algorithm (ant colony optimization algorithm), obstacle avoidance 3D path planning simulation experiments were conducted for three types of spindle-shaped apple trees. The results showed that the success rates of obstacle avoidance path planning were higher than 96%, 86%, and 92% for free-spindle-shaped, high-spindle-shaped, and slender-spindle-shaped trees, respectively. Compared with traditional ant colony algorithms, the average planning time was decreased by 49.38%, 46.33%, and 51.03%, respectively. The proposed improved algorithm can effectively achieve three-dimensional path planning for obstacle avoidance picking, thereby providing technical support for the development of intelligent apple picking robots. [ABSTRACT FROM AUTHOR]