Harvesting Apples Research Articles

New design and construction of a mechanical gripping device with a telescopic link of a fruit harvesting robot

Abstract Gripping devices for harvesting fruits have such types of work as cutting, tearing and unscrewing. For apples, it is preferable to use slicing or unscrewing, while the fruit leg should not remain, damaging the apple during storage. In this article, we are developing a grab for harvesting apples. The gripper is used both for holding the fruit and for jamming, followed by unscrewing. One of the advantages is that the proposed method of collecting apples allows you not to waste time moving the manipulator from the tree to the basket, but only to grab and tear them off. The fruit enters the gripper device; after which it enters the fruit collection container through a rigid or flexible pipe. The gripper device is built on the basis of a ball-screw transmission, which is supplemented by a gear drive along the helical surface. This allows for rotation and rectilinear movement of the held fruit. The gripping device has a ratchet mechanism that allows you to fix the fruit. A mathematical model of the gripper device has been developed, which allows determining the torque of the engine depending on the position of the fingers. The parameters of the mechanism were optimized using a genetic algorithm, and the results are presented in the form of a Pareto set. A 3D model of the gripper device has been built and a layout has been developed using 3D printing. Experimental laboratory and field tests of the gripping device were carried out.

3D Camera and Single-Point Laser Sensor Integration for Apple Localization in Spindle-Type Orchard Systems.

Accurate localization of apples is the key factor that determines a successful harvesting cycle in the automation of apple harvesting for unmanned operations. In this regard, accurate depth sensing or positional information of apples is required for harvesting apples based on robotic systems, which is challenging in outdoor environments because of uneven light variations when using 3D cameras for the localization of apples. Therefore, this research attempted to overcome the effect of light variations for the 3D cameras during outdoor apple harvesting operations. Thus, integrated single-point laser sensors for the localization of apples using a state-of-the-art model, the EfficientDet object detection algorithm with an mAP@0.5 of 0.775 were used in this study. In the experiments, a RealSense D455f RGB-D camera was integrated with a single-point laser ranging sensor utilized to obtain precise apple localization coordinates for implementation in a harvesting robot. The single-point laser range sensor was attached to two servo motors capable of moving the center position of the detected apples based on the detection ID generated by the DeepSORT (online real-time tracking) algorithm. The experiments were conducted under indoor and outdoor conditions in a spindle-type apple orchard artificial architecture by mounting the combined sensor system behind a four-wheel tractor. The localization coordinates were compared between the RGB-D camera depth values and the combined sensor system under different light conditions. The results show that the root-mean-square error (RMSE) values of the RGB-D camera depth and integrated sensor mechanism varied from 3.91 to 8.36 cm and from 1.62 to 2.13 cm under 476~600 lx to 1023~1100 × 100 lx light conditions, respectively. The integrated sensor system can be used for an apple harvesting robotic manipulator with a positional accuracy of ±2 cm, except for some apples that were occluded due to leaves and branches. Further research will be carried out using changes in the position of the integrated system for recognition of the affected apples for harvesting operations.

An Apple Detection and Localization Method for Automated Harvesting under Adverse Light Conditions

The use of automation technology in agriculture has become particularly important as global agriculture is challenged by labor shortages and efficiency gains. The automated process for harvesting apples, an important agricultural product, relies on efficient and accurate detection and localization technology to ensure the quality and quantity of production. Adverse lighting conditions can significantly reduce the accuracy of fruit detection and localization in automated apple harvesting. Based on deep-learning techniques, this study aims to develop an accurate fruit detection and localization method under adverse light conditions. This paper explores the LE-YOLO model for accurate and robust apple detection and localization. The traditional YOLOv5 network was enhanced by adding an image enhancement module and an attention mechanism. Additionally, the loss function was improved to enhance detection performance. Secondly, the enhanced network was integrated with a binocular camera to achieve precise apple localization even under adverse lighting conditions. This was accomplished by calculating the 3D coordinates of feature points using the binocular localization principle. Finally, detection and localization experiments were conducted on the established dataset of apples under adverse lighting conditions. The experimental results indicate that LE-YOLO achieves higher accuracy in detection and localization compared to other target detection models. This demonstrates that LE-YOLO is more competitive in apple detection and localization under adverse light conditions. Compared to traditional manual and general automated harvesting, our method enables automated work under various adverse light conditions, significantly improving harvesting efficiency, reducing labor costs, and providing a feasible solution for automation in the field of apple harvesting.

Вивчення залишкових кількостей флонікаміду в яблуках та обґрунтування медико-санітарних нормативів безпечного застосування інсектициду на його основі

Flonicamid is a new systemic pyridine carboxamide insecticide with selective activity against a wide range of insects on various agricultural crops. In Ukraine, field tests were conducted to justify the possibility of its safe use for the protection of apple trees. Materials and Methods. The content dynamics of flonicamid residual amounts in apples and apple juice was studied in the steppe zone of Ukraine during two growing seasons of 2019-2020 with three times of insecticide use with a consumption rate of 0.07 kg/ha. The residual amounts of flonicamid in apples and apple juice were determined by high-performance liquid chromatography (HPLC) with ultraviolet (UV) detection. The limits of quantitative determination (LQ) by HPLC in apples and apple juice are 0.01 mg/kg. Results. During the research period 2019-2020, the amount of flonicamid in the yield of apples and juice from them (as the sum of flonicamid and TFNA and TFNG metabolites in terms of flonicamid) was 0.019-0.071 mg/kg and 0.017-0.032 mg/kg, respectively. After applying the insecticide, the recommended waiting period before harvesting apples is 35 days. The decrease in the flonicamid content in apples occurs on an exponential curve in accordance with the first-order kinetic equation. The calculated half-life (T50) for apples is 23 days for flonicamid, and 27 days for flonicamid (as the sum of flonicamid and TFNA and TFNG metabolites in terms of flonicamid). The possible daily intake of flonicamid in the human body with apples and apple juice is 9.5% of the level of safe intake with the diet (6.6% of its permissible daily intake). The maximum permissible levels of flonicamid content are justified: apples – 0.3 (the sum of flonicamid and TFNA and TFNG metabolites in terms of flonicamid, (LQ by HPLC – 0.01 mg/kg for each compound); apple juice – 0.03 mg / kg (the sum of flonicamid and TFNA and TFNG metabolites in terms of flonicamid. Conclusions. Flonicamid belongs to the hazard class 2 pesticides in terms of degradation in apple trees. The MPL-approved safe content of the active substance in apples and apple juice and the waiting period before harvesting ensure the safety of using a flonicamid-based insecticide. Keywords: flonicamid, residual amounts, chromatography, field tests.

The Monash Apple Retrieving System: A review on system intelligence and apple harvesting performance

The wide-scale adoption of selective apple harvesting robots is yet to be seen, due to their strict requirement of well-manicured canopies to perform reliably. However, maintaining this standard of canopy is labour and resource-intensive, and an unsustainable practice for farmers in the long term who are already burdened with labour shortages. Without a complete planner and intelligent decision-making, a robotic harvesting platform would struggle to adapt to, and navigate in even typical canopy environments. In this work, we introduce the Monash Apple Retrieving System (MARS), a selective apple harvesting platform that is capable of navigating and harvesting apples in complex canopy environments without the need for canopy simplification. Utilising a four-tier planning process, MARS is able to generate gentle, collision-free harvesting trajectories in complex and unstructured canopies. Extensive trials were conducted over the 2021 and 2022 apple harvesting seasons in Australia, where MARS achieved a harvesting success rate of 62.8% at a cycle time of 9.18 s per apple, with negligible damage to the fruit. Performance increased to 70.8% success rate and 7.91 s of cycle time under ideal visibility conditions. Although performance can be further enhanced with canopy simplification and hardware upgrades, the reported results were achieved under realistic canopy conditions where very limited attempts were made to modify the canopy to create idealistic conditions for robotic harvesting. This baseline performance is promising for farmers who are wanting to utilise robotic harvesting, but do not have the resources to maintain well-manicured canopies. By lowering strict canopy requirements for robotic harvesting, we can improve accessibility of this technology to more farmers who are struggling with labour shortages around the world.

Assessment of the Quality of ‘Red Jonaprince’ Apples during Storage after Delayed Harvesting and 1-Methylcyclopropene (1-MCP) Preharvest and Postharvest Treatment

Changing the harvesting time of apples from the optimum harvest window to delayed harvesting may be applied if it is impossible to harvest apples at the optimal time, but it requires changing other factors, as they influence the quality of fruits and shelf life. The aim of the study was to assess the quality of ‘Red Jonaprince’ apples during storage after delayed harvesting and 1-methylcyclopropene (1-MCP) preharvest and postharvest treatment for various storage times. Apples were studied within four groups subjected to preharvest and postharvest treatments, as follows: Group 0—no 1-MCP treatment; Group 1—1-MCP preharvest treatment; Group 2—1-MCP postharvest treatment; and Group 3—1-MCP preharvest and postharvest treatment. All apples were subjected to ultra-low oxygen (ULO) storage conducted for 3, 5 or 6 months, while the analyses were conducted directly after ULO storage (simulated shelf life—0 days) and after simulated shelf life (7 days). For firmness, in the case of 1-MCP applied only preharvest (Group 1) and only postharvest (Group 2), before shelf life, the longer ULO storage resulted in obtaining lower values of firmness (p < 0.0001). If 1-MCP was not applied postharvest (Group 0 and Group 1), and short ULO storage was applied (3 and 5 months for Group 0; 3 months for Group 1), after shelf-life lower values of firmness were observed (p < 0.0001). For soluble solids content (SSC), in the case of 1-MCP not applied preharvest (Group 0 and Group 2), before shelf life, and for 1-MCP applied postharvest (Group 2) after shelf life, the longer ULO storage resulted in obtaining lower values of SCC (p < 0.0001). For titratable acidity (TA), in the case of all the studied groups after shelf life, as well as in case of 1-MCP applied only preharvest (Group 1) also before shelf life, the longer ULO storage resulted in obtaining lower values of TA (p < 0.0001). Except for the 1-MCP applied only postharvest (Group 2), in the case of short ULO storage applied (3 and 5 months for Group 0; 5 months for Group 1; 5 months for Group 3), after shelf-life lower values of TA were observed (p < 0.0001). If delayed harvesting must be conducted, applying 1-MCP not only postharvest, but also preharvest, allows obtaining the most stable firmness and SSC, which do not decrease during storage and shelf life. Taking this into account, it may be concluded, that in the case of delayed harvesting, combining 1-MCP applied preharvest and postharvest should be recommended to keep the quality parameters stable during storage and shelf life.

Recognition and Counting of Apples in a Dynamic State Using a 3D Camera and Deep Learning Algorithms for Robotic Harvesting Systems.

Recognition and 3D positional estimation of apples during harvesting from a robotic platform in a moving vehicle are still challenging. Fruit clusters, branches, foliage, low resolution, and different illuminations are unavoidable and cause errors in different environmental conditions. Therefore, this research aimed to develop a recognition system based on training datasets from an augmented, complex apple orchard. The recognition system was evaluated using deep learning algorithms established from a convolutional neural network (CNN). The dynamic accuracy of the modern artificial neural networks involving 3D coordinates for deploying robotic arms at different forward-moving speeds from an experimental vehicle was investigated to compare the recognition and tracking localization accuracy. In this study, a Realsense D455 RGB-D camera was selected to acquire 3D coordinates of each detected and counted apple attached to artificial trees placed in the field to propose a specially designed structure for ease of robotic harvesting. A 3D camera, YOLO (You Only Look Once), YOLOv4, YOLOv5, YOLOv7, and EfficienDet state-of-the-art models were utilized for object detection. The Deep SORT algorithm was employed for tracking and counting detected apples using perpendicular, 15°, and 30° orientations. The 3D coordinates were obtained for each tracked apple when the on-board camera in the vehicle passed the reference line and was set in the middle of the image frame. To optimize harvesting at three different speeds (0.052 ms-1, 0.069 ms-1, and 0.098 ms-1), the accuracy of 3D coordinates was compared for three forward-moving speeds and three camera angles (15°, 30°, and 90°). The mean average precision (mAP@0.5) values of YOLOv4, YOLOv5, YOLOv7, and EfficientDet were 0.84, 0.86, 0.905, and 0.775, respectively. The lowest root mean square error (RMSE) was 1.54 cm for the apples detected by EfficientDet at a 15° orientation and a speed of 0.098 ms-1. In terms of counting apples, YOLOv5 and YOLOv7 showed a higher number of detections in outdoor dynamic conditions, achieving a counting accuracy of 86.6%. We concluded that the EfficientDet deep learning algorithm at a 15° orientation in 3D coordinates can be employed for further robotic arm development while harvesting apples in a specially designed orchard.

Optimization of the YOLOv7 Object Detection Algorithm for Estimating the Amount of Apple Harvest

The increasing population consumed in high production and food needs for survival. Apples are one of the crop harvest products in Indonesia whose needs are increasing, because they are not only needed for human vitamins but can be used as hand fruit or a form of gratitude to those who receive the fruit. In the process of harvesting apples in agricultural land, harvesting is often found which is not feasible in the hands of consumers because it takes too long for apples to not be harvested when the condition of the fruit is feasible in maturity. Therefore, the authors approach this problem by processing the image results obtained to form a detection model, whether the apples are said to be feasible to be harvested immediately and from the image results it can also be calculated the number of fruits captured by the image model , feature enhancements Estimates on objects from this image model are expected to provide more timely harvest predictions in order to provide longer aging of apples and good fruit quality after reaching consumers

A flexible swallowing gripper for harvesting apples and its grasping force sensing model

Securely harvesting apples without damage remains a challenge owing to their softness, vulnerability, and irregular shapes. In this study, a flexible swallowing (FS) gripper was designed and tested for harvesting apples. To analyze the adaptive grasping process of this gripper, first, a closed equation of the force-to-deformation model for the finger was proposed based on the Chained-Beam-Constraint Model (CBCM). The correctness of the model was verified by finite element analysis (FEA), and the maximum error was no more than 1.7 %. Subsequently, the grasping force sensing model related to bending angle of the finger was established based on the force-to-deformation model. Finally, taking an apple with a diameter of 80 mm as the experimental object, several groups of grasping tests were conducted with the gripper. The bending angle and force sensing error for different grasping positions of the fingers were analyzed and compared. The results demonstrated that the gripper can sufficiently perceive the grasping force, and the force sensing accuracy in the middle of the finger was the best, with an average absolute error and relative error of 0.153 N and 5.65 %, respectively; A better envelope ability and greater grasping force was obtained when grasping with the bottom of the finger; the maximum bending angle and maximum contact force were 22.6° and 5.72 N respectively. Moreover, a harvesting experiment using this gripper installed at the end of a robot arm was conducted, which further verified that this gripper has a good grasping ability for apples and can be sufficiently applied in actual robot harvesting.

Evaluation of the efficiency of apple picking using a flexible manipulator

Flexible manipulators are a promising executive tool in various technological complexes. In the process of collecting fruits, they can, in addition to their main task of delivering working bodies to the object of labor, also perform the functions of flexible guides. For the use of flexible manipulators in agriculture, namely in the operations of harvesting apples, developments are needed that are relatively cheap to manufacture in comparison with other flexible manipulators. To analyze the efficiency of apple picking using a flexible manipulator, a series of tests was carried out. As part of the tests, the process of picking apples was simulated using a flexible manipulator. The number of apples harvested for branches of various geometries was estimated. Within the framework of the discussion, the prospects of introducing a flexible manipulator in super-intensive gardens in Russia were assessed.

Variation of Triterpenes in Apples Stored in a Controlled Atmosphere.

Apples are seasonal fruits, and thus after harvesting apples of optimal picking maturity, it is important to prepare them properly for storage and to ensure proper storage conditions in order to minimize changes in the chemical composition and commercial quality of the apples. We studied the quantitative composition of triterpenic compounds in the whole apple, apple peel and apple flesh samples before placing them in the controlled atmosphere (CA) chambers, and at the end of the experiment, 8 months later. HPLC analysis showed that highest total amount of triterpenic compounds (1.99 ± 0.01 mg g−1) was found in the whole apple samples of the ‘Spartan’ cultivar stored under variant VIII (O2—20%, CO2—3%, N2—77%) conditions. Meanwhile, the highest amount of triterpenic compounds (11.66 ± 0.72 mg g−1) was determined in the apple peel samples of the ‘Auksis’ cultivar stored under variant II (O2—5%, CO2—1%, N2—94%) conditions. In the apple peel samples of the ‘Auksis’ cultivar stored under variant I (O2—21%, CO2—0.03%, N2—78.97%) conditions, the amount of individual triterpenic compounds (ursolic, oleanolic, corosolic, and betulinic acids) significantly decreased compared with amount determined before the storage. Therefore, in the apple flesh samples determined triterpenic compounds are less stable during the storage under controlled atmosphere conditions compared with triterpenic compounds determined in the whole apple and apple peel samples.

Comparisons of physical exposure between workers harvesting apples on mobile orchard platforms and ladders, part 1: Back and upper arm postures

This study compared farmworkers’ exposure to non-neutral postures using a new mobile platform apple harvesting method and the traditional method using ladders. Twenty-four workers were recruited and assigned into three groups: ladder workers (n = 8) picking apples from full trees using a ladder, mobile platform workers (n = 8) picking apples from upper part of the trees while standing on a moving platform, and ground-based mobile platform workers (n = 8) picking apples from lower part of the trees which the mobile platform workers left out. Upper arm and back inclinations were continuously monitored during harvesting using tri-axial accelerometers over full work shifts (~8 h). Upper arm posture was characterized as the percentage of time that upper arm flexion and abduction exceeded 30°, 60°, and 90°. Back posture was characterized as the percentage of time that torso angles (sagittal flexion or lateral bending) exceeded 10°, 20°, and 30°. The 10th, 50th, and 90th postural percentiles were also calculated. The platform workers had lower exposures to upper arm flexion and abduction than the ground and ladder workers. There were no differences in torso angles between the ladder and mobile platform workers; however, the ground workers were exposed to more and greater percentages of time in torso flexions.

Comparisons of physical exposure between workers harvesting apples on mobile orchard platforms and ladders, part 2: Repetitive upper arm motions

Farmworkers are exposed to physical risk factors including repetitive motions. Existing ergonomic assessment methods are primarily laboratory-based and, thus, inappropriate for use in the field. This study presents an approach to characterize the repetitive motions of the upper arms based on direct measurement using accelerometers. Repetition rates were derived from upper arm inclination data and with video recordings in the field. This method was used to investigate whether harvesting with mobile platforms (teams harvesting apples from the platform and the ground) increased the farmworkers’ exposure to upper arm repetitive motions compared to traditional harvesting using ladders. The ladder workers had higher repetitive motions (13.7 cycles per minute) compared to the platform and ground workers (11.7 and 12.2 cycles per minutes). The higher repetitions in the ladder workers were likely due to their ability to work independently and the additional arm movements associated with ladder climbing and walking.

Fruit picking robots: has their time come?

PurposeThe purpose of this paper is to describe recent fruit picking robot developments with an emphasis on corporate activity rather than academic research. It also aims to provide a view on the commercial prospects for these developments.Design/methodology/approachFollowing a short introduction, this first discusses strawberry and other soft fruit picking robot developments conducted principally by commercial organisations. It then provides similar details of robots for harvesting apples and other hard fruits. This is followed by a discussion and concluding comments.FindingsThe shortage of seasonal fruit pickers has stimulated the need for automation. Accordingly, a growing community of companies, many founded in the past five years, are developing fruit picking robots. These are aimed at both soft and hard fruits, such as strawberries and apples, respectively, and exploit advanced vision systems, image processing techniques and AI. Some products are already on the market, whereas many more are due for commercial release during the next two years into what is expected to be a highly competitive market.Originality/valueThis provides details of the emerging fruit picking robot business by describing the products and manufacturing companies.

Role of Antarctic yeast in biocontrol of Penicillium expansum and patulin reduction of apples

Apple is among the main fruit crops in Uruguay. After harvest apples are stored at low temperatures. Despite this storage condition, Penicilliumexpansum, one of the most important postharvest pathogens worldwide, develops on apples. This fungus produces patulin, a secondary metabolite with toxic effects in mammals. Patulin contamination control of apples currently relies mainly on the use of fungicides. However, the use of biological control agents is a promising friendly alternative and some commercial products have been developed. In this work five psychrotrophic yeasts isolated from cold environments were evaluated as biocontrol agents of P.expansum rot on apples. Experiments were done during 3 months under cold storage conditions, followed by further storage at room temperature. Among the tested Antarctic yeasts, Candida sake 41E effectively controlled fungal growth on apples at cold and room temperature. This yeast also reduced patulin concentration in apple juice at room temperature, without adsorption to the cell walls. These characteristics suggest that this strain may be a promising biocontrol agent of blue mold.

A Comprehensive Study of the Potential Application of Flying Ethylene-Sensitive Sensors for Ripeness Detection in Apple Orchards.

The right moment to harvest apples in fruit orchards is still decided after persistent monitoring of the fruit orchards via local inspection and using manual instrumentation. However, this task is tedious, time consuming, and requires costly human effort because of the manual work that is necessary to sample large orchard parcels. The sensor miniaturization and the advances in gas detection technology have increased the usage of gas sensors and detectors in many industrial applications. This work explores the combination of small-sized sensors under Unmanned Aerial Vehicles (UAV) to understand its suitability for ethylene sensing in an apple orchard. To accomplish this goal, a simulated environment built from field data was used to understand the spatial distribution of ethylene when subject to the orchard environment and the wind of the UAV rotors. The simulation results indicate the main driving variables of the ethylene emission. Additionally, preliminary field tests are also reported. It was demonstrated that the minimum sensing wind speed cut-off is 2 ms−1 and that a small commercial UAV (like Phantom 3 Professional) can sense volatile ethylene at less than six meters from the ground with a detection probability of a maximum of . This work is a step forward in the usage of aerial remote sensing technology to detect the optimal harvest time.

Design and Field Evaluation of a Robotic Apple Harvesting System with a 3D-Printed Soft-Robotic End-Effector

Abstract. Fresh market apple harvesting is a difficult task that relies entirely on manual labor. Much research has been done on the development of mechanical harvesting techniques. Several selective harvesting robots have been developed for research studies, but there are no commercially available robotic systems. This article discusses the design and development of a novel pneumatic 3D-printed soft-robotic end-effector to facilitate apple separation. The end-effector was integrated into a robotic system with five degrees of freedom that was designed to simplify the picking sequence and reduce costs compared to previous versions. Apples were successfully harvested using the low-cost robotic system in a commercial orchard during the fall 2017 harvest. A detachment success rate on attempted apples of 67% was achieved, with an average time of 7.3 s per fruit from separation to storage bin. By conducting this study in an orchard where problematic apples were not removed to increase the detachment success rate, current pruning and thinning practices were assessed to help lay the foundation for future studies and develop strategies for successfully harvesting apples that are difficult to detach. Keywords: Apple catching, Apples, Automated harvesting, Field experimentation, Harvesting robot, Soft-robotic gripper.

The Role of a New Harvest Platform in Alleviation of Apple Workers' Occupational Injuries During Harvest.

. Migrant seasonal workers manually harvest apples throughout the U.S. using ladders and buckets due to the care required for prevention of bruises and other physical damage. The motions associated with hand harvest have potential to cause musculoskeletal disorders of the neck, shoulder, and back/trunk. A prototype harvest platform was developed, aimed at alleviating occupational injuries and increasing harvest productivity by replacing ladders for reaching high apples. This study evaluated the activities of three harvest methods, i.e., conventional harvest (using ladders and buckets), harvest platform (for high apples only), and combined method (conventional harvest for low and middle apples and harvest platform for high apples) using the Rapid Upper Limb Assessment (RULA) method performed by trained researchers. Postures/activities with RULA grand scores > 5 were categorized as awkward (causing occupational injuries); otherwise, they were considered comfortable (not leading to health issues). Experimental results demonstrated that awkward activities in conventional harvest were mainly related to the use of ladders. Activities with the harvest platform were comfortable due to the elimination of ladders and buckets. The combined method significantly decreased the workers' time spent in awkward postures (from 64% with conventional harvest to 30% with the combined method) by eliminating awkward activities and increased the overall harvest productivity by approximately 40%. Apple growers and workers are therefore suggested to use the combined method to replace conventional harvest.

Hand-Picking Dynamic Analysis for Undersensed Robotic Apple Harvesting

<abstract> This article evaluates hand-picking methods as candidate grasping techniques for implementation in a robotic system designed to harvest apples. The standard method of hand-picking apples is highly selective to the apple‘s orientation and stem location. However, sensory detection of the fruit‘s orientation and stem while the apple is on the tree is a challenging problem requiring significant computation time. In this study, four picking techniques that do not require knowledge of fruit orientation were applied to five apple varieties growing in several different cultivation systems. The sensors used during hand-picking included force sensors and an inertial measurement unit. Experimental results were obtained for normal contact forces during a three-fingered power grasp as well as the angle of rotation around the axis of the forearm. Field data and controlled laboratory experiments show that fruit separation can be clearly detected. Accelerometer measurements were also used to calculate the average distance to fruit separation, which varied from 3 to 7 cm. The optimum picking method relative to stem attachment was identified for each apple variety.

Preharvest boron application and its relation with the quality of 'Galaxy' apples after harvest and controlled atmosphere storage

ABSTRACT: The aim of this research was to evaluate the effect of preharvest boron application on the physical and chemical quality of 'Galaxy' apples after harvest and in controlled atmosphere storage during eight months, plus seven days of shelf life at 20°C. The experiment was performed with two treatments applied on the field: [1] Control (fruit without boron application) and [2] Foliar application of boron (four applications of 1.5kg ha-1). Findings showed that the boron application promoted a higher ethylene production and respiratory rate and it application also reduced the flesh firmness in relation to the fruits without it. A similar result was obtained after eight months of storage plus seven days of shelf life. The preharvest application of boron makes it possible to harvest apples earlier due to the fact that it accelerates the fruit metabolism. However, the fruits end up presenting a lower storage potential as a result of the higher ethylene production, respiration rate, decay incidence, mealiness and a reduction of the healthy fruit percentage and flesh firmness after CA storage.