Fruit Picking Research Articles

Design and Analysis of a Robotic Gripper Mechanism for Fruit Picking

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

Phenological growth stages of highbush blueberries (Vaccinium spp.): codification and description according to the BBCH scale

Highbush blueberries ( Vaccinium spp.) are cultivated crop plants worldwide and they are still gaining importance. However, a common uniform description of the phenological development was missing so far. In this work, a specific code is presented, based on the decimal BBCH scale. The BBCH scale comprises the macrostages 0–9, with macrostage 4 being omitted here, because it has no relevance for blueberry crops grown in the field. Each macrostage is subdivided in several microstages, each describing a notable developmental stage. The BBCH code takes special reference to the fruit development, fruit ripening, and several fruit pickings, which are typical for blueberries. For each macrostage the main development as well as cultivation characteristics are explained in more detail. The new BBCH code is compared with other descriptions of phenotypical development of highbush blueberries and transformations are suggested. The BBCH code is developed for cultivated highbush blueberries and is therefore applicable to northern, southern, and rabbiteye blueberries as well.

Fruit Picking Robot Arm Training Solution Based on Reinforcement Learning in Digital Twin

In the era of Industry 4.0, digital agriculture is developing very rapidly and has achieved considerable results. Nowadays, digital agriculture-based research is more focused on the use of robotic fruit picking technology, and the main research direction of such topics is algorithms for computer vision. However, when computer vision algorithms successfully locate the target object, it is still necessary to use robotic arm movement to reach the object at the physical level, but such path planning has received minimal attention. Based on this research deficiency, we propose to use Unity software as a digital twin platform to plan the robotic arm path and use ML-Agent plug-in as a reinforcement learning means to train the robotic arm path, to improve the accuracy of the robotic arm to reach the fruit, and happily the effect of this method is much improved than the traditional method.

Fruit Picking and Farmwork as Racialised Stigma: The Children of Pacific Migrant Workers in Rural Australia

ABSTRACT Pacific youth growing up in rural horticultural towns in Victoria, Australia, frequently experience the stigmatising labels of ‘fruit picker’ and ‘farmworker’. Drawing on extensive fieldwork in the Sunraysia region, we focus on schools as early sites of racialisation and racism, and compare the perspectives of students and teachers. While teachers often blame ‘culture’ for poor academic performance, students complain of teachers’ racism while also experiencing low confidence and poor self-esteem that hinder their academic progress. Underpinning these issues is the racial hierarchy in the towns, shaped by their particular histories of colonisation, migration and intercultural relationships. Current theories of rural multiculturalism tend to focus on everyday interactions and strategies to encourage social cohesion, with the inherent danger of neglecting the deeper structural issues. We argue that Pacific youth seeking to move beyond the casual and precarious farmwork undertaken by their migrant parents encounter structural barriers that begin in schools and profoundly shape their future opportunities.

A Distance Measurement Approach for Large Fruit Picking with Single Camera

Target ranging is the premise for manipulators to complete agronomic operations such as picking and field management; however, complex environmental backgrounds and changing crop shapes increase the difficulty of obtaining target distance information based on binocular vision or depth cameras. In this work, a method for ranging large-sized fruit based on monocular vision was proposed to provide a low-cost and low-computation alternative solution for the fruit thinning or picking robot. The regression relationships between the changes in the number of pixels occupied by the target area and the changes in the imaging distance were calculated based on the images of square-shaped checkerboards and circular-shaped checkerboards with 100 cm2, 121 cm2, 144 cm2, 169 cm2, 196 cm2, 225 cm2, 256 cm2, 289 cm2, and 324 cm2 as the area, respectively. The 918 checkerboard images were collected by the camera within the range from 0.25 m to 1.5 m, with 0.025 m as the length of each moving step, and analyzed in MATLAB to establish the ranging models. A total of 2448 images of four oval watermelons, four pyriform pomelos, and four oblate pomelos, as the representatives of large fruit with different shapes, were used to evaluate and optimize the performance of the models. The images of the front were the input, while the imaging distances were the output. The results showed that the absolute error would be less than 0.06 m for both models and would linearly increase with a decrease in the distance. The relative error could be controlled at 5%. The results proved the proposed monocular method could be a solution for the ranging of large fruit targets.

A Dragon Fruit Picking Detection Method Based on YOLOv7 and PSP-Ellipse.

Dragon fruit is one of the most popular fruits in China and Southeast Asia. It, however, is mainly picked manually, imposing high labor intensity on farmers. The hard branches and complex postures of dragon fruit make it difficult to achieve automated picking. For picking dragon fruits with diverse postures, this paper proposes a new dragon fruit detection method, not only to identify and locate the dragon fruit, but also to detect the endpoints that are at the head and root of the dragon fruit, which can provide more visual information for the dragon fruit picking robot. First, YOLOv7 is used to locate and classify the dragon fruit. Then, we propose a PSP-Ellipse method to further detect the endpoints of the dragon fruit, including dragon fruit segmentation via PSPNet, endpoints positioning via an ellipse fitting algorithm and endpoints classification via ResNet. To test the proposed method, some experiments are conducted. In dragon fruit detection, the precision, recall and average precision of YOLOv7 are 0.844, 0.924 and 0.932, respectively. YOLOv7 also performs better compared with some other models. In dragon fruit segmentation, the segmentation performance of PSPNet on dragon fruit is better than some other commonly used semantic segmentation models, with the segmentation precision, recall and mean intersection over union being 0.959, 0.943 and 0.906, respectively. In endpoints detection, the distance error and angle error of endpoints positioning based on ellipse fitting are 39.8 pixels and 4.3°, and the classification accuracy of endpoints based on ResNet is 0.92. The proposed PSP-Ellipse method makes a great improvement compared with two kinds of keypoint regression method based on ResNet and UNet. Orchard picking experiments verified that the method proposed in this paper is effective. The detection method proposed in this paper not only promotes the progress of the automatic picking of dragon fruit, but it also provides a reference for other fruit detection.

A Review of Target Recognition Technology for Fruit Picking Robots: From Digital Image Processing to Deep Learning

Machine vision technology has dramatically improved the efficiency, speed, and quality of fruit-picking robots in complex environments. Target recognition technology for fruit is an integral part of the recognition systems of picking robots. The traditional digital image processing technology is a recognition method based on hand-designed features, which makes it difficult to achieve better recognition as it results in dealing with the complex and changing orchard environment. Numerous pieces of literature have shown that extracting special features by training data with deep learning has significant advantages for fruit recognition in complex environments. In addition, to realize fully automated picking, reconstructing fruits in three dimensions is a necessary measure. In this paper, we systematically summarize the research work on target recognition techniques for picking robots in recent years, analyze the technical characteristics of different approaches, and conclude their development history. Finally, the challenges and future development trends of target recognition technology for picking robots are pointed out.

Ergonomic Analysis of Working Postures in Hawthorn Fruit (Crataegus Spp.) Picking

İnsanların beslenmesinde kullanılan tarım ürünlerinin birçoğunun kültürü yapılmaktadır. Ancak bazı ürünler ise doğada kendiliğinden yetişmektedir. Bu ürünler suya ihtiyaç duymadığı gibi çoğu zaman tarımsal mücadele, gübreleme gibi diğer tarımsal işlemler ve uygulamalara da ihtiyaç duymazlar. Kendiliğinden büyür, gelişir ve meyve verirler. Bu tip bitkiler ya da ağaçlar doğada kendileri için en uygun yerlerde bulunurlar. Kendini korumak için çoğu zaman ya sarp kayalık yerlerde yetişirler ya da meyveleri dikenli ve ulaşılması zor yerlerdedir. Çoğu zaman eğimli, sarp ve dağlık yerlerde kendiliğinden yetişen bitkilerin, meyvelerinin ya da diğer kısımların toplanmasında tarımsal mekanizasyon araçları kullanılamamaktadır. Bu işlemlerin tümünün insan eli ile yapılması gerekmektedir. Bu bitkilerin meyveleri olgunlaştığında toplanmadığında kendiliğinden yere düşmektedir. Bu meyveleri toplayan kişiler bazen dalların arasından bazen de yere dökülen meyveleri toplamak zorunda kalabilirler. Özellikle küçük boyutlu meyvelerin toplanması çok zaman alabilmektedir. Bu durumda uzun bir süre zor koşullarda çalışan toplayıcıların çeşitli fiziksel zorlamalara maruz kaldıkları söylenebilir. Bunların çalışma durumlarının incelenerek, ergonomik analizinin yapılması ve elde edilecek sonuçlara göre önerilerde bulunulması insan sağlığı için büyük önem taşımaktadır.
 Bu çalışmada, eğimli bir zeminde kendiliğinden yetişmiş olan bir grup alıç ağacında geleneksel yollarla yapılan meyve toplama işleminde, toplayıcıların duruşları kamera ile kayıt altına alınmıştır. Elde edilen veriler, OWAS (Hızlı Tüm Vücut Değerlendirmesi) kriterlerine göre belirlenmiş ve EgroFellow 3.0. yazılımı kullanılarak, sınıflandırılmıştır. Böylece alıç toplama esnasında çalışanların duruşlarından elde edilen veriler incelenip, uygun olmayan duruşlar belirlenmiştir. Bunların yol açtığı kas ve iskelet sistemi rahatsızlıklarına neden olabilecek risklerin önceden tahmin edilip, en aza indirmesi ve duruşlarda iyileştirmeler sağlanması mümkün olacaktır.

Research on Flexible End-Effectors with Humanoid Grasp Function for Small Spherical Fruit Picking

The rapid, stable, and undamaged picking of small-sized spherical fruits are one of the key technologies to improve the level of intelligent picking robots and reduce grading operations. Cherry tomatoes were selected as the research object in this work. Picking strategies of two-stage “Holding-Rotating” and finger-end grasping were determined. The end-effector was designed to separate the fruit from the stalk based on the linear motion of the constraint part and the rotating gripper. This work first studied the human hand-grasping of cherry tomatoes and designed the fingers with sinusoidal characteristics. The mathematical model of a single finger of the gripper was established. The structural parameters of the gripper were determined to meet the requirements of the grabbing range from 0 to 61.6 mm. Based on the simulation model, the constraint part was set to 6 speeds, and the fruit sizes were set to 20 mm, 30 mm, and 40 mm, respectively. When the speed was 0.08m/s, the results showed that the grabbing time was 0.5381 s, 0.387 s, and 0.2761 s, respectively, and the maximum grabbing force was 0.9717 N, 3.5077 N, and 4.0003 N now of clamping, respectively. It met the picking requirements of high speed and low loss. The criterions of two-index stability and undamaged were proposed, including the grasping index of the fixed value and the slip detection of variance to mean ratio. Therefore, the control strategy and algorithm based on two-stage and two-index for rapid, stable, and non-destructive harvesting of small fruit were proposed. The results of the picking experiment for seventy-two cherry tomatoes showed that the picking success rate was 95.82%, the average picking time was 4.86 s, the picking damage rate was 2.90%, the browning rate was 2.90% in 72 h, and the wrinkling rate was 1.49% in 72 h, which can meet the actual small spherical fruit picking requirements. The research will provide an idea for the flexible end-effectors with humanoid grasp function and provides a theoretical reference for small spherical fruit picking.

Production capacity of seeds affected by green fruit pickings in six varieties of Okra (Abelmoschus esculentus L.)

Production capacity of seeds affected by green fruit pickings in six varieties of Okra (Abelmoschus esculentus L.)

Seed Quality Parameters as Influenced by Plant Growth Regulators and Fresh Fruit Harvesting in Okra

The research work was conducted to find out the most suitable level of plant growth regulator (PGR) and stage of plant for spray as well as fresh fruit pickings to increase the seed yield of okra. The experiment was laid out in randomized block design (factorial) with three replications to at the Vegetable seed production area BAU, Sabour, Bhagalpur. Present investigation was framed with Sixty (60) treatments consisting of 10 PGR’s level, P0 (distilled water), GA3 (100 ppm, 150 ppm and 200 ppm), NAA (100 ppm, 150 ppm and 200 ppm), IAA (30 ppm, 60 ppm and 90 ppm), 2 Stage of plant for spraying of PGRs, S1 (4 leaf stage), S2 (4 leaf stage + Flower initiation) and 3 pickings of fruits, H1 (3 Pickings of fresh fruits + rest fruits left for seed production), H2 (6 Pickings of fresh fruits + rest fruits left for seed production), H3 (No pickings of fresh fruits + all fruits left for seed production. It was observed that the different levels of treatments affected the seed quality attributes of the crops. Among the treatments, P2S1H1 treatment was recorded maximum Germination % (86.36), Seedling length (31.04 cm), Vigour index-I (2787.73), Vigour index-II (5.27) and seed viability test (88.00). Thus, P2S1H1 was superior among all treatments.

Pear Recognition in an Orchard from 3D Stereo Camera Datasets to Develop a Fruit Picking Mechanism Using Mask R-CNN.

In orchard fruit picking systems for pears, the challenge is to identify the full shape of the soft fruit to avoid injuries while using robotic or automatic picking systems. Advancements in computer vision have brought the potential to train for different shapes and sizes of fruit using deep learning algorithms. In this research, a fruit recognition method for robotic systems was developed to identify pears in a complex orchard environment using a 3D stereo camera combined with Mask Region-Convolutional Neural Networks (Mask R-CNN) deep learning technology to obtain targets. This experiment used 9054 RGBA original images (3018 original images and 6036 augmented images) to create a dataset divided into a training, validation, and testing sets. Furthermore, we collected the dataset under different lighting conditions at different times which were high-light (9–10 am) and low-light (6–7 pm) conditions at JST, Tokyo Time, August 2021 (summertime) to prepare training, validation, and test datasets at a ratio of 6:3:1. All the images were taken by a 3D stereo camera which included PERFORMANCE, QUALITY, and ULTRA models. We used the PERFORMANCE model to capture images to make the datasets; the camera on the left generated depth images and the camera on the right generated the original images. In this research, we also compared the performance of different types with the R-CNN model (Mask R-CNN and Faster R-CNN); the mean Average Precisions (mAP) of Mask R-CNN and Faster R-CNN were compared in the same datasets with the same ratio. Each epoch in Mask R-CNN was set at 500 steps with total 80 epochs. And Faster R-CNN was set at 40,000 steps for training. For the recognition of pears, the Mask R-CNN, had the mAPs of 95.22% for validation set and 99.45% was observed for the testing set. On the other hand, mAPs were observed 87.9% in the validation set and 87.52% in the testing set using Faster R-CNN. The different models using the same dataset had differences in performance in gathering clustered pears and individual pear situations. Mask R-CNN outperformed Faster R-CNN when the pears are densely clustered at the complex orchard. Therefore, the 3D stereo camera-based dataset combined with the Mask R-CNN vision algorithm had high accuracy in detecting the individual pears from gathered pears in a complex orchard environment.

A Multi-Objective Particle Swarm Optimization for Trajectory Planning of Fruit Picking Manipulator

Stable, efficient and lossless fruit picking has always been a difficult problem, perplexing the development of fruit automatic picking technology. In order to effectively solve this technical problem, this paper establishes a multi-objective trajectory model of the manipulator and proposes an improved multi-objective particle swarm optimization algorithm (represented as GMOPSO). The algorithm combines the methods of mutation operator, annealing factor and feedback mechanism to improve the diversity of the population on the basis of meeting the stable motion, avoiding the local optimal solution and accelerating the convergence speed. By adopting the average optimal evaluation method, the robot arm motion trajectory has been testified to constructively fulfill the picking standards of stability, efficiency and lossless. The performance of the algorithm is verified by ZDT1~ZDT3 benchmark functions, and its competitive advantages and disadvantages with other multi-objective evolutionary algorithms are further elaborated. In this paper, the algorithm is simulated and verified by practical experiments with the optimization objectives of time, energy consumption and pulsation. The simulation results show that the solution set of the algorithm is close to the real Pareto frontier. The optimal solution obtained by the average optimal evaluation method is as follows: the time is 34.20 s, the energy consumption is 61.89 °/S2 and the pulsation is 72.18 °/S3. The actual test results show that the trajectory can effectively complete fruit picking, the average picking time is 25.5 s, and the success rate is 96.67%. The experimental results show that the trajectory of the manipulator obtained by GMOPSO algorithm can make the manipulator run smoothly and facilitates efficient, stable and nondestructive picking.

Fuzzy Neural Network PID Control of Valve-controlled Hydraulic Motor for Camellia Fruit Picking Machine

Fuzzy Neural Network PID Control of Valve-controlled Hydraulic Motor for Camellia Fruit Picking Machine

Research on the Application of Rigid-flexible Compound Driven Fruit Picking Robot Design in Realizing Fruit Picking

This article mainly designs an efficient fruit picking robot that combines rigidity and flexibility. The design of the robot adopts a mechanical arm with 5 degrees of freedom for the mecanum wheel. The mechanical arm is equipped with a gripper made using artificial muscle technology. The ZED2 binocular vision system is used to obtain spatial coordinates to realize the positioning and positioning of the mechanical arm. Video image acquisition. In order to realize the automatic recognition and picking of strawberries, this paper creates a strawberry training set, and uses the yolov3 algorithm to train the data and obtains a strawberry recognition model, which has a recall rate of larger strawberries and immature strawberries and the precision rate reached 90%, and the detection precision and precision rate of small target strawberries reached more than 80%. It can Meet the requirements of picking in real life.

水果采摘机器人采摘装置机研究现状

水果采摘机器人采摘装置机研究现状

Virtual Simulation of Fruit Picking Robot Based on Unity3D

With the development of the fruit farming industry, there have been breakthroughs in both scale and harvesting requirements, and the resulting problem is that the demand of picking robot is more and more high, the function, cost and quality of the harvest and picking efficiency compared with the traditional manual operation mode with strong competitiveness for market, that is to say, in terms of cost cheaper, picking at a faster rate, and can avoid damage on the fruit in the process of picking. Therefore, in terms of mechanical structure design, the transmission accuracy and efficiency of the picking robot should be improved. At the same time, the structural design of the picking robot should simplify the structure as much as possible, reduce the manufacturing cost, and ensure the feasibility of the picking robot’s functions. Picking robot combined with automatic walking system, automatic detection system and control system-centered intelligent system, can achieve accurate and efficient work in various conditions. Realize picking robot toward automation, intelligence, scientific development. The design of this topic is mainly aimed at the development of the picking robot and the use of analysis, determine the feasibility of the picking robot design and virtual simulation.

Design of Agricultural Robot for Fruit Picking and Tuning

There are mainly five steps in agriculture that are plowing, seeding, watering, tuning, harvesting. In this project, we are going to mainly focus on Boring, where a robot with the help of the drilling tool attached at its rear side will be used to remove the soil for the plantation. Secondly comes plantation, here the person will carry out plantation at desired places. Then comes the maintenance of the crop in which the amount of watering, sunlight, pesticide, fertilizer required for the healthy growth of the plant is controlled by the robot with help of electronic circuits present inside a robot. Then we focus on tuning operation, this module will be taken care of by cutters attached to the robot. Finally, fruit picking operation is done by a three-hand jaw attached to a robot which will be used to pick the fruits. Another additional module is a sample of soil that will be taken and processed with different chemicals and give us output as deficient nutrients in the soil and fertilizers to be added to make soil fertile land and suitable atmospheric conditions for the crop

English

English

Assessment of avoidable losses due to seed borer, Trymalitis margarias Meyrick in different varieties of sapota under high density plantation

Studies were conducted to assess avoidable losses due to seed borer, Trymaliti margarias Meyrick in eight varieties/hybrids of sapota under high density plantation (5 x5 m). The fruit infestation was found significantly maximum in Kalipatti (3.91%) and DHS 2 (3.13%) under protected condition in overall six fruit pickings. Whereas under unprotected plot, the significantly higher fruit damage of 7.81% was reported in Kalipatti, after that in DHS 2 (5.88%) and Cricket ball (5.00%). The maximum reduction of fruit damage was found in Kalipatti with 49.93%, after that in Cricket ball (48.00%) and DHS 2 (46.76%). However, the fruit damage reduction was less in PKM 4 (29.67) and PKM 3 (27.94%), which already had less fruit infestation under unprotected plot. In terms of avoidable losses on the basis of marketable fruits yield, the losses were anticipated more in Cricket ball (53.99%), Kalipatti (51.90%), DHS 2 (49.35%) and CO 3 (45.16%). However, the moderate to low avoidable losses were noted in DHS 1 (38.51%), PKM 3 (36.00%), PKM 1 (31.33%) and PKM 4 (28.70%).