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Breeding programmes have selected cultivars with a lower chill requirement. Apple production in these regions is socioeconomically valuable since fruit is harvested earlier than cooler regions. Understanding the adaptability and phenotypic stability in subtropical climate regions across multiple cropping seasons is important to ensure high productivity. The aim here was to evaluate the adaptability and phenotypic stability of seven apple cultivars that have been selected for a subtropical climate region. Phenology and productivity was measured including the mean number of fruit per plant, fruit weight, production per plant, mean estimated yield, and sum of chill hours, from four production cycles. Fruit quality was assessed for fruit from two production cycles. The genotype × environment interaction was analysed by the GGE biplot method. The Z selection index, the seasonal variation contributed significantly to the performance of each cultivar. Eva had the highest accumulated yield during the four production cycles and Imperatriz and Gala, had the lowest yields. Chilling requirements were the lowest for the cultivars Princesa, Eva and Julieta. Eva, Princesa and Julieta were the most stable for yield and also for the Z index. Eva and Julieta were the most adaptable and stable in subtropical climate regions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Horticulture & Postharvest Research is the property of University of Birjand 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.)

Coffee is one of the most popular and widely used drinks worldwide. At present, how to judge the maturity of coffee fruit mainly depends on the visual inspection of human eyes, which is both time-consuming and labor-intensive. Moreover, the occlusion between leaves and fruits is also one of the challenges. In order to improve the detection efficiency of coffee fruit maturity, this paper proposes an improved detection method based on YOLOV7 to efficiently identify the maturity of coffee fruits, called ASD-YOLO. Firstly, a new dot product attention mechanism (L-Norm Attention) is designed to embed attention into the head structure, which enhances the ability of the model to extract coffee fruit features. In addition, we introduce SPD-Conv into backbone and head to enhance the detection of occluded small objects and low-resolution images. Finally, we replaced upsampling in our model with DySample, which requires less computational resources and is able to achieve image resolution improvements without additional burden. We tested our approach on the coffee dataset provided by Roboflow. The results show that ASD-YOLO has a good detection ability for coffee fruits with dense distribution and mutual occlusion under complex background, with a recall rate of 78.4%, a precision rate of 69.8%, and a mAP rate of 80.1%. Compared with the recall rate, accuracy rate and mAP of YOLOv7 model, these results are increased by 2.0%, 1.1% and 2.1%, respectively. The enhanced model can identify coffee fruits at all stages more efficiently and accurately, and provide technical reference for intelligent coffee fruit harvesting. [ABSTRACT FROM AUTHOR]

The function of abscission zones (AZs) determines the timing of fleshy fruit abscission, with important consequences not only for the optimal fruit harvest, but also on the overall final fruit quality. In this context, chemical treatments are commonly used at different stages of fruit development to control fruit abscission, which can also have positive or negative effects on fruit quality. In the current review, we examine commonly used chemicals that affect the metabolic activity in the AZs of fleshy fruit, in addition to their effects on fruit quality characteristics. The main hormone metabolism and signaling in the AZ include that of ethylene, auxin, abscisic acid and jasmonates, and the molecular components that are involved are covered and discussed, in addition to how these hormones work together to regulate AZ activity and hence, affect fruit quality. We focus on studies that have provided new insight into possible protein complexes that function in the AZ, including multiple MADS-box transcription factors, with potential overlapping regulatory roles which exist between AZ development, ethylene production, AZ activation, fruit ripening and overall fruit quality. The view of the AZ as a cross roads where multiple pathways and signals are integrated is discussed. [ABSTRACT FROM AUTHOR]

Climate changes increase environmental stress pressure, limiting the yields of crops, e.g., strawberries. The green transformation introduced in the European Union, eliminating the use of chemical plant protection agents, requires the development of a technology that will simultaneously mitigate stresses and increase plant yields. The basis of this type of technology may be the targeted application of stabilized orthosilicic acid. The validation of this silicon-based technology was carried out through the pot cultivation of strawberries cv. 'Falco' in controlled conditions, compatible with their production. The experiment consisted of the foliar and intra-root (A) application of stabilized orthosilicic acid at concentrations of 0, 240, and 360 g Si·ha−1 (B). A significant increase in the total and marketable yield, the weight of single fruits, and the number of fruits in the silicon-treated variants was noted in this study. The intra-root application of silicon had a more potent effect on the yield performance than foliar feeding. The intra-root application of the tested silicon doses significantly reduced the occurrence of gray mold (Botrytis cinerea) during the fruit harvest period. The application of the tested silicon doses in strawberry cultivation exerted a positive effect on the post-harvest shelf life of the fruits. Higher levels of Lascorbic acid, nitrates (V), and TSS were determined in strawberry fruits treated with stabilized orthosilicic acid. The leaves of plants treated with stabilized orthosilicic acid had lower contents of nitrogen, calcium, magnesium, iron, manganese, zinc, and boron and higher levels of potassium and copper. [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.)

Early fruit size prediction in greenhouse tomato (Solanum lycopersicum L.) is crucial for growers managing cultivars to reduce the yield ratio of small-sized fruit and for stakeholders in the horticultural supply chain. We aimed to develop a method for early prediction of tomato fruit size at harvest with machine learning algorithm, and three machine learning models (Ridge Regression, Extra Tree Regrreion, CatBoost Regression) were compared using the PyCaret package for Python. For constructing the models, the fruit weight estimated from the fruit diameter obtained over time for each cumulative temperature after anthesis was used as explanatory variable and the fruit weight at harvest was used as objective variable. Datasets for two different prediction periods after anthesis of three tomato cultivars ("CF Momotaro York," "Zayda," and "Adventure.") were used to develop tomato size prediction models, and their performance was evaluated. We also aimed to improve the model adding the average temperature during the prediction period as an explanatory variable. When the estimated fruit size data at cumulative temperatures of 200°C d, 300°C d, and 500°C d after anthesis were used as explanatory variables, the mean absolute percentage error (MAPE) was lowest for "Zayda," a cultivar with stable fruit diameter, at 9.8% for Ridge Regression. When the estimated fruit size at cumulative temperatures of 300°C d, 500°C d, and 800°C d after anthesis were used as explanatory variables for Ridge Regression, the MAPE decreased for all cultivars: 10.1% for "CF Momotaro York," 8.8% for "Zayda," and 10.0% for "Adventure." In addition, incorporating the average temperature during the fruit size prediction period as an explanatory variable slightly increased model performance. These results indicate that this method could effectively predict tomato size at harvest in three cultivars. If fruit diameter data acquisition could be automated or simplified, it would assist in cultivation management, such as tomato thinning. [ABSTRACT FROM AUTHOR]

Malaysia ranks among the world's top 20 pineapple producers, driven by the success of the MD2 variety in meeting domestic and international demand. However, postharvest losses due to pathological diseases remain a challenge. Black rot, a major postharvest disease, causes significant economic losses in pineapples. Despite its presence in various cultivars, its aetiology, specifically in MD2 pineapples remains unclear. This study was conducted to identify the principal causative pathogen of black rot disease in pineapple from three different regions. In addition, critical factors influencing black rot disease were investigated, such as the minimum inoculum concentration, appropriate storage temperature, and maturity index required to initiate infection. Thielaviopsis paradoxa was identified as the primary pathogen causing black rot, with 50 and 45% occurrence at two specific cultivation sites. Other associated pathogens included Lasiodiplodea theobromae , Trichoderma asperellum , Curvularia eragrostidis , Neoscytalidium dimidiatum , Aspergillus assiutensis , and Aspergillus aculeatus. Fruits stored at ambient temperature with a maturity index of 2 showed higher disease progression than those in cold storage. A minimum inoculum concentration of 1 × 104 CFU/mL was sufficient for infection at both storage conditions. The Pearson correlation analysis revealed a weak positive link (r > 0.39, p < 0.0001) between harvesting index and fruit pH, while pH and storage temperature had a strong positive correlation (r = 0.83, p < 0.0001). The increments in pH correlated with lesion length and infected area (r = 0.83 and r = 0.82, respectively). The harvesting index showed a strong positive correlation with the proportion of infected area (r = 0.86, p < 0.0001). The telomorph state of T. paradoxa , identified as Ceratocystis paradoxa , persists in soil and decaying plant material, acting as a quiescent pathogen, increasing cross-contamination risks. Urgent measures are required to reduce postharvest losses and maintain the quality of pineapples for international markets. [ABSTRACT FROM AUTHOR]

Recently, Japan has been witnessing an increase in the average age of agricultural workers and a decrease in the number of new entrants into farming, both of which are progressing year by year due to the country's declining birthrate and aging population. As a result, expectations for substitution by robots and human-robot collaboration are rising. Therefore, we propose a robot arm built using straight-fiber-type pneumatic artificial muscle (SF-PAM) and a noncircular pulley. SF-PAM is sealed and has no sliding parts; thus, it has excellent dustproof and waterproof properties and is suitable for work on farms. However, due to its structure, the SF-PAM has a nonlinear relationship between the contraction force and the amount of contraction, and the output torque is insufficient near the limit of its range of motion. As a solution to this problem, a noncircular pulley is introduced to compensate for the output torque and expand the range of motion. Based on this, this study aims to realize fruit harvesting operation using a robot arm. In this paper, a two-degree-of-freedom robot arm was developed, and position control experiments were conducted to verify the tracking with the target value. As a result, the mechanical equilibrium model of the wire-pulley mechanism was found to be valid for this robot arm. However, issues were found due to the arrangement of the SF-PAM and the shape of the noncircular pulley. [ABSTRACT FROM AUTHOR]

To address the challenges of unbalanced class labels with varying maturity levels of tomato fruits and low recognition accuracy for both fruits and stems in intelligent harvesting, we propose the YOLOX-SE-GIoU model for identifying tomato fruit maturity and stems. The SE focus module was incorporated into YOLOX to improve the identification accuracy, addressing the imbalance in the number of tomato fruits and stems. Additionally, we optimized the loss function to GIoU loss to minimize discrepancies across different scales of fruits and stems. The mean average precision (mAP) of the improved YOLOX-SE-GIoU model reaches 92.17%. Compared to YOLOv4, YOLOv5, YOLOv7, and YOLOX models, the improved model shows an improvement of 1.17–22.21%. The average precision (AP) for unbalanced semi-ripe tomatoes increased by 1.68–26.66%, while the AP for stems increased by 3.78–45.03%. Experimental results demonstrate that the YOLOX-SE-GIoU model exhibits superior overall recognition performance for unbalanced and scale-variant samples compared to the original model and other models in the same series. It effectively reduces false and missed detections during tomato harvesting, improving the identification accuracy of tomato fruits and stems. The findings of this work provide a technical foundation for developing advanced fruit harvesting techniques. [ABSTRACT FROM AUTHOR]

The physicochemical properties of fruits at different maturity stages using grafting technology are of great importance since grafting can alter the nutritional and functional parameters of the fruit. In this study, grafted yellow pitahaya (Selenicereus megalanthus Haw.) fruit, grown on live tutors, was evaluated from stages 0 to 5. The following response variables were recorded: fruit weight, diameter, and length; pulp weight with seed and peel; color; firmness; total soluble solids content; titratable acidity; pH; total flavonoid content; total polyphenol content; and antioxidant activity determined using FRAP and ABTS. The results show that fruits harvested from grafted plants have better physical characteristics such as fruit weight, diameter, and length. However, the total soluble solids content and titratable acidity were similar in fruits from grafted and ungrafted plants. The highest content of total polyphenols, flavonoids, and antioxidant activity determined by ABTS and FRAP were found in fruits at maturity stage 0, and the content decreased as the fruits ripened. A positive correlation was found between the total polyphenol content, total flavonoid content, and antioxidant capacity with protein content. The S. megalanthus grafting technique is a promising technology for sustainable production because it reduces pesticide use by combatting soil pathogens and not modifying fruit quality. [ABSTRACT FROM AUTHOR]

Due to the short time, high labor intensity and high workload of fruit and vegetable harvesting, robotic harvesting instead of manual operations is the future. The accuracy of object detection and location is directly related to the picking efficiency, quality and speed of fruit-harvesting robots. Because of its low recognition accuracy, slow recognition speed and poor localization accuracy, the traditional algorithm cannot meet the requirements of automatic-harvesting robots. The increasingly evolving and powerful deep learning technology can effectively solve the above problems and has been widely used in the last few years. This work systematically summarizes and analyzes about 120 related literatures on the object detection and three-dimensional positioning algorithms of harvesting robots over the last 10 years, and reviews several significant methods. The difficulties and challenges faced by current fruit detection and localization algorithms are proposed from the aspects of the lack of large-scale high-quality datasets, the high complexity of the agricultural environment, etc. In response to the above challenges, corresponding solutions and future development trends are constructively proposed. Future research and technological development should first solve these current challenges using weakly supervised learning, efficient and lightweight model construction, multisensor fusion and so on. [ABSTRACT FROM AUTHOR]

Recently, computer vision methods have been widely applied to agricultural tasks, such as robotic harvesting. In particular, fruit harvesting robots often rely on object detection or segmentation to identify and localize target fruits. During the model selection process for object detection, the average precision (AP) score typically provides the de facto standard. However, AP is not intuitive for determining which model is most efficient for robotic harvesting. It is based on the intersection-over-union (IoU) of bounding boxes, which reflects only regional overlap. IoU alone cannot reliably predict the success of robotic gripping, as identical IoU scores may yield different results depending on the overlapping shape of the boxes. In this paper, we propose a novel evaluation metric for robotic harvesting. To assess gripping success, our metric uses the center coordinates of bounding boxes and a margin hyperparameter that accounts for the gripper's specifications. We conducted evaluation about popular object detection models on peach and apple datasets. The experimental results showed that the proposed gripping success metric is much more intuitive and helpful in interpreting the performance data. [ABSTRACT FROM AUTHOR]

Timely harvesting of fresh apples faces challenges due to labour shortage, and the modern approach of robotic harvesting has the potential to address this issue. The prevailing process of apple harvest robotics could not meet the demands of practical applications, mainly due to the lack of a suitable manipulator, because the existing ones are associated with low picking rates, fruit damage, and high costs. A prototype apple harvesting manipulator was developed, which includes a vacuum three-revolute-degrees-of-freedom end-effector, a three-prismatic-degrees-of-freedom Cartesian system, an RGB-D camera, and system integration. The vision positioning system and controller were designed to realise precise positioning and detachment of the manipulator. The major contribution of the current study is the three-revolute-degrees-of-freedom vacuum suction end-effector, whose performance evaluation was conducted in a commercial apple orchard. Experimental results showed that a 33 ϕ mm diameter suction cup achieved superior performance over a 43 ϕ mm cup. The method of rotation followed by pull proved to be more effective than only pulling for apple detachment. The results indicated that the apple's equatorial region was the optimal area for suction. Furthermore, the vacuum pressure should be at least −65 kPa to guarantee successful detachment. Experimental results showed that 83.1% of harvested apples had stems intact. For the developed manipulator, a 33 ϕ mm diameter suction cup, a rotate-and-pull separation method, and −65 kPa were recommended for practical applications. With the integrated new manipulator, the developed apple harvest robot has been demonstrated to have the potential to realise robotic apple harvesting. • A 3R-DoF end-effector for apple-picking robot was developed. • Apple's equatorial region recommended for suction. • -65 kPa pressure with a 33 mm diameter cup is recommended for use. • Rotation followed by retraction is recommended apple detachment. [ABSTRACT FROM AUTHOR]

Copyright of BIOAGRO is the property of Revista BIOAGRO 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.)

This article provides a comprehensive review of the current results of pear-picking technology, delving into the development process, classification, application status, and development trends of picking machinery, picking robots, and intelligent technology. By analyzing the key technologies in pear fruit harvesting, this paper explores the working principles of harvesting machinery, the technical characteristics of harvesting robots, and the potential applications of intelligent technology. Furthermore, a bibliometric analysis was employed to examine two decades of the research literature on Korla fragrant pear, spanning from January 2004 to June 2024, utilizing the core collection of the Web of Science and the China National Knowledge Infrastructure database as the retrieval platforms. The visualization of the analysis results indicates that the focal points of research in this field are predominantly aspects such as the quality and storage conditions of fragrant pears, with a scarcity of studies directed toward mechanized harvesting. Additionally, this study addresses the existing challenges and issues within pear-picking technology and delineates potential avenues for future development, with the objective of providing a foundation for subsequent research on Korla fragrant pear-harvesting technology. [ABSTRACT FROM AUTHOR]

Introduction: The accurate and rapid detection of ginseng fruits in natural environments is crucial for the development of intelligent harvesting equipment for ginseng fruits. Due to the complexity and density of the growth environment of ginseng fruits, some newer visual detection methods currently fail to meet the requirements for accurate and rapid detection of ginseng fruits. Therefore, this study proposes the YOLO-Ginseng detection method. Methods: Firstly, this detection method innovatively proposes a plug-and-play deep hierarchical perception feature extraction module called C3f-RN, which incorporates a sliding window mechanism. Its unique structure enables the interactive processing of cross-window feature information, expanding the deep perception field of the network while effectively preserving important weight information. This addresses the detection challenges caused by occlusion or overlapping of ginseng fruits, significantly reducing the overall missed detection rate and improving the long-distance detection performance of ginseng fruits; Secondly, in order to maintain the balance between YOLO-Ginseng detection precision and speed, this study employs a mature channel pruning algorithm to compress the model. Results: The experimental results demonstrate that the compressed YOLO-Ginseng achieves an average precision of 95.6%, which is a 2.4% improvement compared to YOLOv5s and only a 0.2% decrease compared to the uncompressed version. The inference time of the model reaches 7.4ms. The compressed model exhibits reductions of 76.4%, 79.3%, and 74.2% in terms of model weight size, parameter count, and computational load, respectively. Discussion: Compared to other models, YOLO-Ginseng demonstrates superior overall detection performance. During the model deployment experiments, YOLO-Ginseng successfully performs real-time detection of ginseng fruits on the Jetson Orin Nano computing device, exhibiting good detection results. The average detection speed reaches 24.9 fps. The above results verify the effectiveness and practicability of YOLO-Ginseng, which creates primary conditions for the development of intelligent ginseng fruit picking equipment. [ABSTRACT FROM AUTHOR]

With the decreasing and aging agricultural workforce, fruit harvesting robots equipped with higher degrees of freedom (DoF) manipulators are seen as a promising solution for performing harvesting operations in unstructured and complex orchard environments. In such a complex environment, guiding the end-effector from its starting position to the target fruit while avoiding obstacles poses a significant challenge for path planning in automatic harvesting. However, existing studies often rely on manually constructed environmental map models and face limitations in planning efficiency and computational cost. Therefore, in this study, we introduced a collision-free path planning method for a 6-DoF orchard harvesting manipulator using an RGB-D camera and the Bi-RRT algorithm. First, by transforming the RGB-D camera's point cloud data into collision geometries, we achieved 3D obstacle map reconstruction, allowing the harvesting robot to detect obstacles within its workspace. Second, by adopting the URDF format, we built the manipulator's simulation model to be inserted with the reconstructed 3D obstacle map environment. Third, the Bi-RRT algorithm was introduced for path planning, which performs bidirectional expansion simultaneously from the start and targets configurations based on the principles of the RRT algorithm, thereby effectively shortening the time required to reach the target. Subsequently, a validation and comparison experiment were conducted in an artificial orchard. The experimental results validated our method, with the Bi-RRT algorithm achieving reliable collision-free path planning across all experimental sets. On average, it required just 0.806 s and generated 12.9 nodes per path, showing greater efficiency in path generation compared to the Sparse Bayesian Learning (SBL) algorithm, which required 0.870 s and generated 15.1 nodes per path. This method proved to be both effective and fast, providing meaningful guidance for implementing path planning for a 6-DoF manipulator in orchard harvesting tasks. [ABSTRACT FROM AUTHOR]

Harvest time is one of the key factors for obtaining high-quality cherry tomatoes. This parameter depends on environmental conditions and tomato variety. In plant factories with artificial lighting (PFALs), it is possible to control environmental conditions to enhance tomato production and quality. Since the ripening status of tomato fruit is correlated with cumulative temperature (CT), and the temperature inside PFALs can be easily controlled, CT could be used as an alternative method to predict tomato harvest time. In this study, three experiments were conducted to determine the optimal CT for harvesting high-quality cherry tomatoes in a PFAL. The experiments aimed to (1) evaluate the yield and quality of cherry tomatoes as affected by different harvest times based on CT (ranging from 900 to 1400 °C), (2) comparatively evaluate the yield and quality of cherry tomatoes that were still on the plant and off the plant (in storage) based on the same CT levels (i.e., 1100, 1200, and 1300 °C), and (3) investigate the fruit-cracking percentage during the ripening stage based on CT levels. The results showed that the fruit harvested at lower CTs exhibited higher hardness values, while those harvested at higher CTs had a higher sugar content. The on-the-plant treatment resulted in a higher yield and sugar content compared with the off-the-plant treatment, indicating that harvesting tomatoes early would come at the expense of a certain yield and sweetness. Moreover, the fruit-cracking percentage tended to increase with increasing CT, possibly due to the fast fruit growth rate and increased internal turgor pressure. These results indicated that producers can use CT as an index to predict the harvest time, thereby optimizing profits in cherry tomato production. [ABSTRACT FROM AUTHOR]

Nighttime pineapple detection for automated harvesting robots is a significant challenge in intelligent agriculture. As a crucial component of robotic vision systems, accurate fruit detection is essential for round-the-clock operations. The study compared advanced end-to-end style transfer models, including U-GAT-IT, SCTNet, and CycleGAN, finding that CycleGAN produced relatively good-quality images but had issues such as the inadequate restoration of nighttime details, color distortion, and artifacts. Therefore, this study further proposed an enhanced CycleGAN approach to address limited nighttime datasets and poor visibility, combining style transfer with small-sample object detection. The improved model features a novel generator structure with ResNeXtBlocks, an optimized upsampling module, and a hyperparameter optimization strategy. This approach achieves a 29.7% reduction in FID score compared to the original CycleGAN. When applied to YOLOv7-based detection, this method significantly outperforms existing approaches, improving precision, recall, average precision, and F1 score by 13.34%, 45.11%, 56.52%, and 30.52%, respectively. These results demonstrate the effectiveness of our enhanced CycleGAN in expanding limited nighttime datasets and supporting efficient automated harvesting in low-light conditions, contributing to the development of more versatile agricultural robots capable of continuous operation. [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]

The cosmetics industry is constantly growing and occupies an important place in South American countries' economies. Formulations increasingly incorporate ingredients from natural sources to promote sustainable and innovative productions, as well as to gain greater consumer acceptance. According to FAO, waste from post-harvest and food processing in developing countries exceeds 40%, generating significant environmental impacts and stimulating interest in adding value to these wastes, particularly in the fruit and vegetable sector in South American countries, thus contributing to the achievement of the UN Sustainable Development Goals (SDGs). By-products from harvesting and fruit processing of Passiflora species such as leaves, stems, peel, and seeds are a source of bioactive compounds; however, most of them are wasted. This study aims to compile reports on the chemical composition of cultivated Passiflora species, find evidence of the cosmetic activity of their extracts, and estimate their potential for inclusion in cosmetic formulations. [ABSTRACT FROM AUTHOR]

Understanding jujube tree dynamic characteristics is crucial for the design and invention of a catch-and-shake machine for fruit harvesting. Currently, the study of vibration characteristics based on the finite element method is the mainstream method for different types of fruit trees. However, limited by the lack of an accurate 3D tree model, there are still gaps between existing simulation analysis and actual tests to explore vibration characteristics. Specifically, the vibration mechanism of winter jujube trees is still unclear in jujube orchards. To address the issue, a multi-view 3D reconstruction technique is employed to acquire precise 3D tree models for simulation analysis. The obtained results from experiments indicate that the determination coefficient R 2 of the trunks and branches diameter are 0.96 and 0.91 between reconstructed and actual measurement results. Subsequently, material properties of jujube tree are measured to conduct model analysis and harmonic response analysis to find the optimal frequency range (10–20 Hz) in which a considerable vibration response can be obtained at low vibration energies. Moreover, transient analysis and test experiments are conducted to explore the energy transfer properties under different vibration frequency. Results showed that the acceleration response gradually increased from the bottom to the top of the branch on most branches at non-resonant frequencies. The proposed method can provide informative insights on the design of high-efficiency and low-energy jujube catch-and-shake harvesters. [Display omitted] • Accurate 3D models for jujube trees are reconstructed using SfM. • Vibration transfer characteristics are explored using transient analysis. • The optimal harvesting parameters are determined by the finite element method. • The field experiments validate the reliability of the simulation results. [ABSTRACT FROM AUTHOR]

Apples are widely cultivated primarily for fresh consumption. During mechanized harvesting, the extraction of fruit stalks can significantly impact the storage duration of fresh apples. The tensile force applied to the abscission layers is a critical factor in retaining the stalks; yet, few researchers have focused on preventing stalk pull-out during picking. In this research, we studied the phenomenon of missing stalks during mechanical picking by analyzing the tensile force exerted on the abscission layer during picking and optimizing the attitude of the end effector to achieve the highest stalk retention rate. Firstly, the tangential and normal energy release rates of the abscission layer were used as key parameters to model the cohesive zone of the abscission layer, a finite element model of the fruit–stalk–branch system was developed, based on which the actual fruit picking process using direct-pulling and twisting was simulated. Subsequently, the data obtained from the simulation were analyzed using response surface analysis, and the maximum tensile force at the time of fracture of the delamination and the time of its fracture were used as optimization parameters to find the optimal solution of the angle, direct-pulling speed, and twisting speed d to achieve the highest stalk retention rate. Finally, through field experiments, it was demonstrated that the optimal picking conditions could effectively improve the picking success rate and stalk retention rate. The results show that, when the end effector picks close to the fruit at about 58°, the stalk retention rate can reach 94.0%. [ABSTRACT FROM AUTHOR]

In this study, we address the question of the most important factors influencing yield and fruit quality in highbush blueberries. An experiment was carried out to investigate the relationship between yield components and (i) plant yield, (ii) fruit quality traits, and (iii) fruit firmness post-harvest in two Vaccinium corymbosum L. blueberry cultivars ('Duke' and 'Brigitta'). In a field in central Chile (35°15′39″ S; 71°14′32″ W) during the growing season 2018–2019, we measured the number of canes (NC), length of the first shoot (LFS), age of the first shoot (AFS), diameter of the first shoot (DFS), flower buds per cane (FBC), number of one-year shoots per cane (SPC), flowers per bud (FPB), fruit set percentage (FSP), yield (YLD), fruit weight (FW), fruit firmness at harvest (FF), fruit diameter at harvest (FD), soluble solids/acid ratio at harvest (SS:AC), and fruit firmness after harvest (FFpos). The most important factors affecting yield and fruit quality were FBC, SPC, and FF. Our results suggest that FBC and SPC could be managed agronomically to optimize fruit load and light interception. [ABSTRACT FROM AUTHOR]

Changes in physicochemical parameters, fruit softening enzymes and cell wall polysaccharides at four different maturation stages were investigated in two jackfruit genotypes ('Accession 242', 'Accession 341'). For the first three maturity stages, fruit were harvested at 90, 110, and 130 days after flowering (Stage I, II and III, respectively), while Stage IV was determined based on the presence of a dull hollow tapping sound. The fruit edible portion and seed percentage increased, whilst the core and rag percentage decreased with advancement in fruit maturation and ripening. The fruit harvested at Stage IV had comparatively higher soluble solids content (SSC), ascorbic acid and flavonoids, along with lower titratable acidity (TA) and phenolics, than other maturity stages. Bulb firmness was higher at Stage I in both genotypes, along with higher total pectin, protopectin and cellulose compared to other maturity stages. The activity of cell wall hydrolases was higher during later maturity stages. Fruit harvested at Stage IV had higher edible portions, carotenoids, flavonoids and SSC, as well as better colour attributes, while those harvested at Stage I exhibited higher phenolics, TA, pectin and cellulose. These findings could serve as a baseline for future research related to the intended use and maturity standardisation of jackfruit. [ABSTRACT FROM AUTHOR]

Postharvest internal disorders (IDs) in mango fruit present a significant challenge to the industry, with their underlying causes still unclear. This study investigated the relationship between fruit maturity and the susceptibility of vapor heat-treated (VHT) 'B74' mangoes to IDs in three experiments. In the first experiment, fruit were categorized into three maturity groups based on dry matter content (DMC): <15%, 15–17%, and >17%, using a handheld near-infrared device. Half of the fruit in each group underwent VHT, while the remainder were untreated controls. Flesh cavity with white patches (FCWP) was the only disorder observed exclusively in VHT fruit. The incidence and severity of FCWP was significantly higher (p < 0.05) in fruit with <15% DMC, with 12.4% incidence and a severity score of 0.2 on a 0–3 scale (0: healthy and 3: severely affected), compared to more mature fruit. In the second experiment, the fruits were harvested at early and late maturity stages, with average DMC values of 14.5% and 17.4%, respectively. The fruit was subjected to no VHT, VHT, and VHT following a 12 h pre-conditioning period at 37 ± 1 °C. Consistent with the first experiment, FCWP was observed only in VHT fruit, with early-harvested fruit displaying a significantly higher (p < 0.05) FCWP incidence (26.9%) and severity (0.3) compared to late-harvested fruit (8.3% incidence and 0.1 severity). Pre-conditioning significantly reduced FCWP, particularly in early-harvested fruit. In the third experiment, fruit maturity sorted based on density was assessed, followed by VHT and simulated sea freight under controlled (CA) and ambient atmospheres. Fruit density did not effectively differentiate maturity considering DMC as a maturity indicator. Storage conditions significantly reduced (p < 0.05) flesh browning incidence from 71.1% under ambient conditions to 33.3% under CA. This study highlights fruit maturity as a key factor in the susceptibility of 'B74' mangoes to postharvest IDs following VHT. Therefore, sorting fruit based on DMC at harvest or at the packing facility prior to VHT serves as a valuable decision support for reducing IDs in VHT fruit. Further research will explore advanced technologies to enable rapid and efficient fruit sorting based on DMC. [ABSTRACT FROM AUTHOR]

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Camellia oil is one of the most significant woody oil crops in the hilly and mountainous regions of south China. Manual harvesting cannot fully meet the large-scale production, due to the minimal site conditions, tree management, and mechanization levels. Fruit harvesting has significantly hindered the rapid development of the Camellia oil industry, particularly for the labor-intensive, time-consuming, and costly. Therefore, mechanical harvesting has been crucial to increase the self-sufficiency rate for the large-scale production of Camellia oil. In this study, a wheeled integrated harvester was designed to harvest the Camellia oleifera fruits. There was the walking mechanism (chassis, and control system), a connecting mechanism (connecting device), and an operating mechanism (harvesting device, collection and conveying device), with a power rating of 17 kW. The walking wheel was designed as the iron high-pattern rubber wheel. The grip has fully met the requirement of walking on slopes. Rapid harvesting and automated collection were realized for the Camellia oleifera fruits. The machine was measured by 2 340 mm in length, 800 mm in width, and 1 150 mm in height. The picking device and collection system were connected to the chassis via a lifting device. The harvesting mechanism was maintained perpendicular to the trunk during operation on slopes. The lifting device was used to control the position of the operating mechanism, according to the passing performance and operational requirements of the machine. The structure of the whole harvesting machine was more compact, according to the vibration theory of fruit picking. The tree body was vibrated to keep the machine still. It was necessary to reasonably arrange the machine components for the vibration isolation. Specifically, the vibration generator was placed behind the conveying mechanism and flexibly suspended on the frame. A chain delivery device was designed to meet the requirements of Camellia oleifera fruit delivery. The comparative tests were conducted on the speed and transmission at the linear speed of 2.0 m/s. The key contact components were optimized to obtain a final fruit harvesting rate of 93.1% and a damaged fruit rate of 1.1%. Elongated holes were used to remove some impurities on the bottom of the equipment's distribution device and the concave surface of the contact part. Some impurities were removed to automatically screen during delivery. The wheeled chassis power and structure were matched to meet the requirements of chassis layout and power transmission. The installation angle of the diesel engine was adjusted with the slope. The main transmission was used as a worm gear system. Safe parking was realized to ensure the engine's reliability and safety on slopes. Test results show that the harvester was operated efficiently to cover 40 trees per hour, with a picking net rate exceeding 85% and a flower loss rate below 8%. The speeds of the machine ranged from 0 to 5 km/h, with a height adjustment range of 0 to 300 mm. The machine was used to climb the slopes with an angle of up to 19° and a maximum lateral tipping angle of 15°, meeting all operational requirements for slope terrain. Standardized and mechanized Camellia oleifera planting is crucial to the structural design and efficient use of machinery. The integrated machinery can be expected to achieve the maximum efficiency of production. [ABSTRACT FROM AUTHOR]

The current harvesting arms used in harvesting robots are developed based on standard products. Due to design constraints, they are unable to effectively avoid obstacles while harvesting tomatoes in tight spaces. To enhance the robot's capability in obstacle-avoidance picking of tomato bunches with various postures, this study proposes a geometric parameter optimization method for a 7 degree of freedom (DOF) robotic arm. This method ensures that the robot can reach a predetermined workspace with a more compact arm configuration. The optimal picking posture for the end-effector is determined by analyzing the spatial distribution of tomato bunches, the main stem position, and peduncle posture, enabling a quantitative description of the obstacle-avoidance workspace. The denavit–hartenberg (D-H) model of the harvesting arm and the expected collision-free workspace are set as constraints. The compactness of the arm and the accessibility of the harvesting space serve as the optimization objectives. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) multi-objective genetic algorithm is employed to optimize the arm length, and the results were validated through a virtual experiment using workspace traversal. The results indicate that the optimized structure of the tomato harvesting arm is compact, with a reachability of 92.88% in the workspace, based on the collision-free harvesting criteria. This study offers a reference for structural parameter optimization of robotic arms specialized in fruit and vegetable harvesting. [ABSTRACT FROM AUTHOR]

With the advancement of computer vision technology, vision-based target perception has emerged as a predominant approach for harvesting robots to identify and locate fruits. However, little attention has been paid to the fact that fruits may be obscured by stems or other objects. In order to improve the vision detection ability of fruit harvesting robot, a fruit target selection and location approach considering obstacle perception was proposed. To enrich the dataset for tomato harvesting, synthetic data were generated by rendering a 3D simulated model of the tomato greenhouse environment, and automatically producing corresponding pixel-level semantic segmentation labels. An attention-based spatial-relationship feature extraction module (SFM) with lower computational complexity was designed to enhance the ability of semantic segmentation network DeepLab v3+ in accurately segmenting linear-structured obstructions such as stems and wires. An adaptive K-means clustering method was developed to distinguish individual instances of fruits. Furthermore, a barrier-free fruit selection algorithm that integrates information of obstacles and fruit instances was proposed to identify the closest and largest non-occluded fruit as the optimal picking target. The improved semantic segmentation network exhibited enhanced performance, achieving an accuracy of 96.75%. Notably, the Intersection-over-Union (IoU) of wire and stem classes was improved by 5.0% and 2.3%, respectively. Our target selection method demonstrated accurate identification of obstacle types (96.15%) and effectively excluding fruits obstructed by strongly resistant objects (86.67%). Compared to the fruit detection method without visual obstacle avoidance (Yolo v5), our approach exhibited an 18.9% increase in selection precision and a 1.3% reduction in location error. The improved semantic segmentation algorithm significantly increased the segmentation accuracy of linear-structured obstacles, and the obstacle perception algorithm effectively avoided occluded fruits. The proposed method demonstrated an appreciable ability in precisely selecting and locating barrier-free fruits within non-structural environments, especially avoiding fruits obscured by stems or wires. This approach provides a more reliable and practical solution for fruit selection and localization for harvesting robots, while also being applicable to other fruits and vegetables such as sweet peppers and kiwis. [ABSTRACT FROM AUTHOR]

In the Vidarbha region of Maharashtra and the surrounding parts of central India, Waywar (citrus greening bacterium) fruit disorder, also known as wasteful fruit disorder, is a common manifestation of Nagpur mandarin fruit. In Vidarbha, the condition is also known as cock bund, while in China, it is called ‘Red Nose.’ According to early research, this condition is caused by abiotic stress factors, such as poor nutrition, too much moisture in the root zone, and other incorrect cultural practices. Recent research, however, has unequivocally shown that Candidatus Liberobacter asiaticus, the citrus greening bacterium, is responsible for this illness. When compared to normal growing fruits (0.82–0.89), the length to diameter ratio of Waywar fruits at the peak of incidence was reported in the range of 0.95–1.09, indicating their oblong shape. Premature Waywar fruits had higher acidity levels (1.75 to 2.04%) than normal fruits (1.65 to 2.03%) and produced seeds that were not viable. The cultural treatments applied to reduce the ill effects of the disorder improved the physico-chemical characteristics of Nagpur mandarin fruit at the time of harvesting. When compared to the untreated control, foliar spraying with growth regulators 2,4-D (15 ppm) and GA3 (15 ppm) enhanced the amount of fruit retained per plant at harvest. The critical fruit quality parameters like TSS (10.10%) and vitamin C (39.37 mg/100 ml) were highest in GA3 treated fruits. Maximum TSS/Acid ratio (14.00) and minimum acidity (0.69%) were noted with phosphorus applied at three times the RDF level at harvest. [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]

Fruit firmness in sweet cherries (Prunus avium L.) is a critical quality parameter highly valued by consumers as it is associated with fruit freshness. In general, firm fruit also cope better with storage and handling. Gibberellic acid (GA) is commonly used by sweet cherry producers to increase firmness, soluble solids content and fruit size. This study evaluated the effects of GA on the rheological properties of sweet cherry fruit at harvest and postharvest storage. Specifically, GA's influence on susceptibility to mechanical damage during handling was evaluated. The following GA treatments were applied to two sweet cherry cultivars 'Bing' and 'Lapins': T0, control, T30—GA at 15 ppm applied at pit-hardening and straw-colour stages; T45—GA at 25 ppm at pit-hardening and GA at 20 ppm at straw-colour; and T60—GA at 30 ppm applied at pit-hardening and straw-colour. The results indicate that GA delayed harvest by two to four days in both cultivars, with 'Lapins' also showing a significant increase in fruit size. Regardless of spray concentration, GA increased the modulus of elasticity and fruit resistance evaluated as stress at the maximum point at harvest. These effects persisted after 35 days of storage at 0 °C and an additional three days of shelf-life at 15 °C. While the strain or deformation capacity of the fruit at bioyield at harvest was constant across treatments, it was, however, lower in the GA-treated fruit than in the controls during storage at 0 °C under the high-humidity conditions of modified atmosphere packaging. The less mature fruit harvested at colour 3.0 (red/mahogany) were stiffer (reduced deformation) and more sensitive to induced mechanical injury than the fruit harvested later at colour 3.5 (mahogany). The GA treatments increased fruit resistance to damage without increasing tissue deformability. Other questions associated with stiffer tissues and lower deformability during storage at 0 °C under high humidity should be further studied, specifically cultivars that are naturally high in box-cracking sensitivity during storage. [ABSTRACT FROM AUTHOR]

The tomato plant is one of the most important vegetable crops, with a global production of around 188 million tones. The greatest losses in quantity and quality occur during storage, transport, and sale. The aim of the study was to determine the effect of irradiation on the quality and storability of the tomato 'Tomimaru Muchoo'. Fruit harvested at the turning ripening stage were illuminated for the first two weeks at 15 °C with four visible LED light spectra, with different percentages of blue, green, and red light (BGR). The illumination times were 4 and 8 h per day (hpd). After illumination, the tomatoes were stored at 20 °C in the dark for 4 weeks. Immediately after 14 d of illumination, all tomatoes were fully ripe, although they showed varying red color intensity. In addition, all fruit retained very good quality and freshness. During further storage at 20 °C, there was a gradual decrease in tomato quality. However, LED lighting helped delay softening, reduce rotting, and thus maintain better tomato quality. Longer daily irradiation (8 h) delayed tomato senescence to a greater extent than shorter irradiation (4 hpd). Comparing the spectra, the greatest reduction in softening and rotting occurred in tomatoes illuminated with the spectrum containing the highest amount of blue light (56%). These tomatoes also maintained the lowest color index (a*/b*) throughout storage at 20 °C, which was especially evident in tomatoes that had been illuminated for 8 hpd. The light treatment influenced the maintenance of higher levels of ascorbic acid and antioxidant activity in tomatoes. However, irradiation did not increase the polyphenol content of tomatoes or reduce the lycopene levels in the fruit. Overall, the results showed that LED irradiation during storage improves storability and affects the health-promoting components of tomato fruit. It is a promising tool for reducing losses of horticultural produce. [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 phenological characteristics of the sweet cherry depend on both the agroecological growing conditions and the cultivar/rootstock combination. Knowing them is an important condition for the correct selection of cultivars when creating new orchards. Four commercial sweet cherry cultivars - Ferrovia, Regina, Kordia, and Skeena grafted on two rootstocks - Gisela 6 and Maxma 14 were the object of research. The phenological stages of development were monitored, including buds swelling, full flowering and fruit harvest maturity, duration of the flowering, and vegetation period. For the Plovdiv region, at the cultivar/rootstock combinations studied, the earliest bud swelling phase (BBCH 51) for Maxma 14 rootstock cultivars occurred with Kordia on February 19, and the latest with Regina on February 24. In the sweet cherries cultivars grafted on Gisela 6, a delay in vegetation was observed, occurring 3 to 8 days later. In trees grafted on Gisela 6, a delay in flowering by 2 (Regina and Ferrovia) to 5 days (Kordia) was also observed, compared to the same grafted on Maxma 14 rootstock. The period from bud swelling (BBCH 51) to full flowering (BBCH 65) was in the order of 43 - 53 days for the different cultivar/rootstock combinations. From full flowering (BBCH 65) to harvest maturity (BBCH 87) in cultivars grafted on Gisela 6 rootstock, a 3 to 11 days shorter period was reported than the same grafted on Maxma 14 rootstock. The longest vegetation period in both rootstocks was found in the Skeena cultivar, 289 days in trees grafted on Maxma 14 and 277 days in Gisela 6 trees, respectively, and the shortest in the Kordia cultivar, 276 and 270 days, respectively. [ABSTRACT FROM AUTHOR]

This study aims to address the challenges of achieving a high harvesting rate and low flower bud damage rate during the harvesting of camellia fruits. To this end, a dynamic model of the camellia osmantha tree and a self-developed shaker-type harvesting machine were used as research subjects. The first 24 natural frequencies and mode shapes of the camellia tree were solved using the finite element method, and the effects of vibration frequency, excitation position, and vibration duration on the harvesting rate and flower bud damage rate were quantitatively analyzed through an orthogonal experiment. The numerical analysis results indicate that the camellia tree exhibits good response characteristics at vibration frequencies of 10–15.5 Hz and 38.5 Hz. The three-factors orthogonal experiment figured out that the optimal operational parameters for shaker-type harvesting were determined to be a vibration duration of 30 s, a motor output frequency of 12.5 Hz, and a gripping position height of 50 to 60 cm above the ground. Meanwhile, under these operational parameters, the harvesting efficiency reached 96.97%, while the flower bud damage rate was only 6%. [ABSTRACT FROM AUTHOR]

This study addresses the problem of detecting occluded apples in complex unstructured environments in orchards and proposes an apple detection and segmentation model based on improved YOLOv8n-SGW-YOLOv8n. The model improves apple detection and segmentation by combining the SPD-Conv convolution module, the GAM global attention mechanism, and the Wise-IoU loss function, which enhances the accuracy and robustness. The SPD-Conv module preserves fine-grained features in the image by converting spatial information into channel information, which is particularly suitable for small target detection. The GAM global attention mechanism enhances the recognition of occluded targets by strengthening the feature representation of channel and spatial dimensions. The Wise-IoU loss function further optimises the regression accuracy of the target frame. Finally, the pre-prepared dataset is used for model training and validation. The results show that the SGW-YOLOv8n model significantly improves relative to the original YOLOv8n in target detection and instance segmentation tasks, especially in occlusion scenes. The model improves the detection mAP to 75.9% and the segmentation mAP to 75.7% and maintains a processing speed of 44.37 FPS, which can meet the real-time requirements, providing effective technical support for the detection and segmentation of fruits in complex unstructured environments for fruit harvesting robots. [ABSTRACT FROM AUTHOR]

Raspberry production is limited to cold temperate areas of high latitude due to the requirement of low temperatures for flowering and fruiting from most cultivars. However, primocane cultivars, as they are less demanding in cold conditions, represent a possible alternative that suits regions with a subtropical climate. The cultivar Heritage primocane raspberry was investigated in the Cwa climate, in three production systems (PS), during two crop cycles. In PS1, canes were hard pruned at ground level after primocane fruiting. In PS2, canes were tipped to promote subapical bud break for a second harvest. In PS3, canes were tipped again after the second harvest to induce a third harvest. PS1 had the lowest yield, however, after two cycles; in plants of this system it was observed the highest root weight, and starch content. Raspberries subjected to subapical pruning show lower carbohydrate storage in the root system. The production systems had little influence on fruit qualities, in both cycles. The cultivation of cv. Heritage raspberry primocane, in the subtropical Cwa climate can be carried out with sequential pruning, allowing for the production of commercial fruits with harvests distributed over the months, without any reduction in the postharvest quality of the fruits produced. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the morphological characteristics and performance of Formosa and Sunrise Solo papaya cultivars under protected cultivation in subtropical climate conditions as well as the relationships between the yield and factors affecting the yield. The Formosa cultivars had higher values in terms of plant height (519.4 cm), stem diameter (238.4 mm), first flowering height (138.2 cm), and duration from flowering to harvest (141 days) compared to the Sunrise Solo cultivars. The yield per plant was higher in the Formosa cultivars (52.5 kg/plant/year) than in the Sunrise Solo cultivars (27.4 kg/plant/year). The values of fruit peel color parameters were highest in the spring, the fruit flesh firmness was highest in the autumn, and the soluble solid content was higher in fruits harvested in the summer. Medium to high positive phenotypic correlations were found between the first flowering height and fruit set, fruit weight, yield, fruit width, fruit length, and fruit flesh firmness (0.371–0.595) and between the fruit set (number/plant) and fruit weight, yield, fruit width, fruit length, and fruit flesh firmness (0.388–0.819) (p ≤ 0.01). The papaya can be commercially cultivated under protected cultivation in extreme subtropical conditions, and the Formosa cultivars generally performed better than the Sunrise Solo cultivars across many parameters. [ABSTRACT FROM AUTHOR]

Ethylene is an essential indicator of fruit ripening and climacteric or non-climacteric nature. This study investigated the postharvest behavior of five Chinese bayberry cultivars 'Biqi', 'Dongkui', 'Fenhong', 'Xiazhihong', and 'Shuijing'. The fruits were harvested mature and stored at room temperature (25 °C) and under cold storage conditions (4 °C) to investigate the dynamics of ethylene production, firmness, anthocyanin content, and cell wall polysaccharide composition, as well as basic fruit physicochemical characteristics. The results show that Chinese bayberry is a climacteric fruit with ethylene production peaking shortly after harvest, especially at room temperature. Fruit color intensified over time due to anthocyanin accumulation, particularly in the flesh core. Darker cultivars produced more ethylene, which correlated with higher anthocyanin levels. At room temperature, 'Biqi' (black) had the highest ethylene production (4.03 µL·kg−1·h−1) and anthocyanin content (0.91 mg/g FW), while 'Shuijing', the white cultivar, had the lowest ethylene levels (1.9 µL·kg−1·h−1) and anthocyanin content (0.03 mg/g FW). Firmness significantly decreased at room temperature due to the degradation of hemicellulose and insoluble pectin, whereas cold storage mitigates this effect. After 3 days at room temperature, the average of firmness decreased by 23.7% in the five cultivars, compared to 12.7% under cold storage. Total soluble solids increase during storage, enhancing sweetness, especially at room temperature, with 'Biqi' increasing from 9.2 to 10.9% at 4 °C. Titratable acidity slightly decreased over time: the value for 'Biqi' decreased from 1.2% to 0.95% at room temperature and 1.1% at 4 °C. Citric, malic, and tartaric acid generally declined at room temperature but stabilized under cold storage. Sucrose, fructose, and glucose increased or remained stable, with significant varietal differences. Our results indicate that storing Chinese bayberry at 4 °C effectively preserves its quality and extends postharvest life. These findings underscore ethylene's key role in regulating ripening, postharvest quality, and shelf life by influencing fruit color, firmness, and overall consumer appeal. [ABSTRACT FROM AUTHOR]

We investigated the behavior of stable Cs+ ions contained in droplets applied directly on the leaf surfaces of plumleaf crab apple trees (2–3 years old Malus domestica 'Alps Otome') at three different fruit growing stages: before bearing fruit, early fruit development and late fruit development stages. Most of the Cs was rapidly transferred from the leaf surfaces into the applied leaves after application, and then gradually transferred to the fruit through the branches. The mean proportion of Cs transferred to fruit by harvest time ranged from 11 to 30% not directly depending on the fruit growing stages. Cs absorption from leaf surfaces was faster at early and late fruit development stages than before bearing fruit stage, and Cs transfer from leaf surfaces to the fruit was faster as the fruit growing stage progressed. To describe the transfer of Cs, we constructed a compartment model using the datasets of obtained for each fruit growing stage. However, it did not well reproduce the measured values, showing that further studies are necessary. [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]