Harvesting Success Rate Research Articles

Tomato harvesting robotic system based on Deep-ToMaToS: Deep learning network using transformation loss for 6D pose estimation of maturity classified tomatoes with side-stem

This paper presents the development of an autonomous harvesting robot system for tomato, a representative crop cultivated in the facility horticulture smart farm. The automated harvesting work using a robotic system is very challenging because of the appearance, environmental features, such as the atypical directions of the peduncles or their growing form in a bunch. Also, the robot system should enable the harvesting of the target fruit only without damaging other fruits, stems, and branches. Hence, this paper presents a deep learning network pipeline, Deep-ToMaToS, capable of three-level maturity classification and 6D pose (3D translation + 3D rotation) estimation of the target fruit simultaneously. Due to the difficulties encountered in building a large-scale dataset to train and test the deep learning model for the 6D pose estimation in the real world, we presented an automatic data collection scheme based on a photo-realistic 3D simulator environment. The robotic harvesting system includes a harvesting motion control algorithm based on the result of the 6D pose estimation. The overall process of the motion control phase is described along with the decision way of the appropriate final posture of the harvesting module mounted at the end-effector of the robot manipulator via removal of invalid motions getting out of the valid workspace or redundant motions. We conducted experiments on the 6D pose estimation based on the Deep-ToMaToS and the harvesting motion control in virtual and real smart farm environments. The experimental results showed a 6D pose estimation accuracy of 96 % based on the ADD_S metric, and the proposed harvesting motion control algorithm achieves the harvesting success rate of 84.5 % on average. The experimental results reveal that the harvesting robot system has significant potential to extend to harvesting works for other fruits and crops.

Ekonomi Kreatif Dengan Program Warung Keluarga Melalui Budidaya Ikan Dalam Ember

Budikdamber or known as Fish Cultivation in a Bucket is a form of creative economy business that can be done in a family in order to fulfill some basic needs. This research is a Research and Development, which is to continue and modify various findings and problems in the implementation of Warung Keluarga through Budikdamber. The discussion and report models use a qualitative approach. This research was conducted in several places in Boyolali in 2020, 2021, and early 2022. The results show that the Warung Keluarga program through Budikdamber can be done easily, does not require a large space, large capital, and is side-by-side. However, it will result in a significant economic increase in meeting the basic needs of the family, and can be used as a business opportunity if taken seriously. The Budikdamber program can be developed through the media of drums, Pengaron, Plastic Sheeting, and Permanent Pools. Catfish harvest success rate 75% - 95%. The success for the types of catfish and gourami can reach 90% - 98%.

Learning-Based Slip Detection for Robotic Fruit Grasping and Manipulation under Leaf Interference.

Robotic harvesting research has seen significant achievements in the past decade, with breakthroughs being made in machine vision, robot manipulation, autonomous navigation and mapping. However, the missing capability of obstacle handling during the grasping process has severely reduced harvest success rate and limited the overall performance of robotic harvesting. This work focuses on leaf interference caused slip detection and handling, where solutions to robotic grasping in an unstructured environment are proposed. Through analysis of the motion and force of fruit grasping under leaf interference, the connection between object slip caused by leaf interference and inadequate harvest performance is identified for the first time in the literature. A learning-based perception and manipulation method is proposed to detect slip that causes problematic grasps of objects, allowing the robot to implement timely reaction. Our results indicate that the proposed algorithm detects grasp slip with an accuracy of 94%. The proposed sensing-based manipulation demonstrated great potential in robotic fruit harvesting, and could be extended to other pick-place applications.

Research on Multiobjective Optimization Algorithm for Cooperative Harvesting Trajectory Optimization of an Intelligent Multiarm Straw-Rotting Fungus Harvesting Robot

In view of the difficulties of fruit cluster identification, the specific harvesting sequence constraints of aggregated fruits, and the balanced harvesting task assignment for the multiple arms with a series-increasing symmetric shared (SISS) region, this paper proposes a multi-objective optimization algorithm, which combines genetic algorithm (GA) and ant colony optimization (ACO) stepwise, to optimize the multiarm cooperative harvesting trajectory of straw-rotting fungus to effectively improve the harvesting efficiency and the success rate of non-destructive harvesting. In this approach, firstly, the multiarm trajectory optimization problem is abstracted as a multiple travelling salesman problem (MTSP). Secondly, an improved local density clustering algorithm is designed to identify the cluster fruits to prepare data for harvesting aggregated fruits in a specific order later. Thirdly, the MTSP has been decomposed into M independent TSP (traveling salesman problem) problems by using GA, in which a new DNA (deoxyribonucleic acid) assignment rule is designed to resolve the problem of the average distribution of multiarm harvesting tasks with the SISS region. Then, the improved ant colony algorithm, combined with the auction mechanism, is adopted to achieve the shortest trajectory of each arm, which settles the difficulty that the clustered mature fruits should be harvested in a specified order. The experiments show that it can search for a relatively stable optimal solution in a relatively short time. The average harvesting efficiency is up to 1183 pcs/h and the average harvesting success rate is about 97%. Therefore, the proposed algorithm can better plan the harvesting trajectory for multiarm intelligent harvesting, especially for areas with many aggregated fruits.

Autonomous Robotic System for Pumpkin Harvesting

The present study focused on the development, optimization, and performance evaluation of a harvesting robot for heavyweight agricultural products. The main objective of developing this system is to improve the harvesting process of the mentioned crops. The pumpkin was selected as a heavyweight target crop for this study. The main components of the robot consist of mobile platforms (the main robot tractor and a parallel robot tractor), a manipulation system and its end-effector, and an integrated control unit. The development procedure was divided into four stages: stage I (designed system using Solidworks), stage II (installation of the developed system on a temporary platform), stage III (developed system on an RT-1 (Yanmar EG453)), and stage IV (developed system on an RT-2 (Yanmar YT5113)). Various indicators related to the performance of the robot were evaluated. The accuracy of 5.8 and 4.78 mm in x and y directions and repeatability of 5.11 mm were observed. The harvesting success rate of 87~92%, and damage rate of 5% resulted in the evaluation of the final version. The average cycle time was 35.1 s, 42.6 s, and 43.2 s for stages II, III, and IV, respectively. The performance evaluations showed that the system’s indicators are good enough to harvest big-sized and heavy-weighted crops. Development of the unique and unified system, including a mobile platform, a manipulation system, an end-effector, and an integrated algorithm, completed the targeted harvesting process appropriately. The system can increase the speed and improve the harvesting process because it can work all day long, has a precise robotic manipulation and end-effector, and a programmable controlling system that can work autonomously.

Division-merge based inverse kinematics for multi-DOFs humanoid robots in unstructured environments

A Two-step Division-Merge (TDM) based inverse kinematics (IK) mechanism is proposed for robots with multiple degrees of freedom (multi-DOFs) in unstructured environments. For any robot with n-DOFs (n>6), a division point is selected first to divide it into a lower part subsystem from its base to the division point, together with an upper part one from the point to its end-effector. Once a collision-free configuration is found for the upper part subsystem to match with the target, the lower part subsystem is then planned to merge with the upper part subsystem at the division point. Specifically, the division point is selected such that the lower part subsystem has 6-DOFs, while the upper part subsystem has (n-6)-DOFs. Furthermore, a Reverse & Random Rotation (RRR) algorithm is also proposed to solve the IK of the upper part subsystem in unstructured environments, while the IK of the lower part subsystem is solved analytically. The proposed TDM based mechanism is verified via experiments using a lab-customized humanoid apple harvesting robot with 11- and 13-DOFs, respectively. Under the various scenarios with different target and obstacle configurations, errors in position coordinates and orientation angles between the target pose and those of the end-effector obtained with forward kinematics (FK) are calculated. Results show that the errors in position and orientation are less than 0.005 mm and 0.007°, respectively, and the average computational time is less than 2 s for the IK solution of the entire robot. Such results indicate that the proposed mechanism is reliable and capable of reducing the complexity of IK for multi-DOFs humanoid robots. With the proposed algorithm, the apple harvesting success rate of a lab-customized 11-DOFs robot reaches 89.2%, which can be further increased up to 93.3% with error compensation, while the average time starting from the robot action to the end-effector reaching the target pose of the apple is around 12.3 s.

Recognition of sweet peppers and planning the robotic picking sequence in high-density orchards

• A YOLO-V4-CBAM method for the accurate recognition of green pepper in a dense orchard environment was developed. • A DPC algorithm for automatic classification of picking clusters is proposed. • A principle of anti-collision picking within picking clusters is proposed based on the "empirical expert method", Gaussian distance weighting, and "winner-take-all" optical neural filter competition method. To improve the operational efficiency of and to prevent possible collision damage in the near-neighbor multi-target picking of sweet peppers by robots in densely planted complex orchards, this study proposes an algorithm for recognizing sweet peppers and planning a picking sequence called AYDY. First, the convolutional block attention module is embedded into the you only look once model (YOLO-V4), and this combined model is used to recognize and localize sweet peppers. Then, the clustering algorithm for the fast search-and-find of density peaks is improved based on the inflection points and gaps of a decision graph. Sweet peppers with multiple near-neighbor targets are automatically partitioned into picking clusters. An anti-collision picking sequence for a picking cluster is determined based on the experience of experts. The algorithm combines Gaussian distance weights with the winner-takes-all approach as an optic neural filter. In tests, the F 1 -score of this method for sweet peppers in a densely planted environment was 91.84%, which is a 9.14% improvement compared to YOLO-V4. The average localization accuracy and collision-free harvesting success rate were 89.55% and 90.04%, respectively. The recognition and localization time for a single image was 0.3033 s. The time to plan a picking sequence for a single image was 0.283 s. When the robotic arm harvested 22 and 24 sweet peppers, compared to sequential and stochastic planning, the proposed method had higher collision-free picking rates by 18.18, 18.18, 16.67, and 25 percentage points, respectively. This method can accurately detect sweet peppers, reduce collision damage, and improve picking efficiency in high-density orchard environments. This study may provide technical support for anti-collision picking of sweet peppers by robots.

Geometry-aware fruit grasping estimation for robotic harvesting in apple orchards

Field robotic harvesting is a promising technique in recent development of agricultural industry. It is vital for robots to recognise and localise fruits before the harvesting in natural orchards. However, the workspace of harvesting robots in orchards is complex: many fruits are occluded by branches and leaves. It is important to estimate a proper grasping pose for each fruit before performing the manipulation. In this study, a geometry-aware network, A3N, is proposed to perform end-to-end instance segmentation and grasping estimation using both color and geometry sensory data from a RGB-D camera. Besides, workspace geometry modelling is applied to assist the robotic manipulation. Moreover, we implement a global-to-local scanning strategy, which enables robots to accurately recognise and retrieve fruits in field environments with two consumer-level RGB-D cameras. We also evaluate the accuracy and robustness of proposed network comprehensively in experiments. The experimental results show that A3N achieves 0.873 on instance segmentation accuracy, with an average computation time of 35 ms. The average accuracy of grasping estimation is 0.61 cm and 4.8$^{\circ}$ in centre and orientation, respectively. Overall, the robotic system that utilizes the global-to-local scanning and A3N, achieves success rate of harvesting ranging from 70\% - 85\% in field harvesting experiments.

A Study on the Feasibility of Robotic Harvesting for Chile Pepper

This paper presents a study on the robotic harvesting of New Mexico type chile pepper, in a laboratory setting, using a five degrees of freedom (DoF) serial manipulator. The end-effector of the manipulator, a scissor-type cutting mechanism, was devised and experimentally tested in a lab setup which cuts the chile stem to detach the fruit. Through a MATLAB™-based program, the location of the chile pepper is estimated in the robot’s reference frame, using Intel RealSense Depth Camera. The accuracy of the 3D location estimation system matches the maximum accuracy claimed by the manufacturer of the hardware, with a maximum error to be in Y-axis, which is 5.7 mm. The forward and inverse kinematics are developed, and the reachable and dexterous workspaces of the robot are studied. An application-based path planning algorithm is developed to minimize the travel for a specified harvesting task. The robotic harvesting system was able to cut the chile pepper from the plant based on 3D location estimated by MATLAB™ program. On the basis of harvesting operation, on 77 chile peppers, the following harvesting indicators were achieved: localization success rate of 37.7%, detachment success rate of 65.5%, harvest success rate of 24.7%, damage rate of 6.9%, and cycle time of 7 s.

A Field-Tested Harvesting Robot for Oyster Mushroom in Greenhouse

The fully autonomous harvesting of oyster mushrooms in the greenhouse requires the development of a reliable and robust harvesting robot. In this paper, we propose an oyster-mushroom-harvesting robot, which can realize harvesting operations in the entire greenhouse. The two crucial components of the harvesting robot are the perception module and the end-effector. Intel RealSense D435i is adopted to collect RGB images and point cloud images in real time; an improved SSD algorithm is proposed to detect mushrooms, and finally, the existing soft gripper is manipulated to grasp oyster mushrooms. Field experiments exhibit the feasibility and robustness of the proposed robot system, in which the success rate of the mushroom recognition success rate reaches 95%, the harvesting success rate reaches 86.8% (without considering mushroom damage), and the harvesting time for a single mushroom is 8.85 s.

Three-dimensional reconstruction of guava fruits and branches using instance segmentation and geometry analysis

In unstructured environments, harvesting robots may collide with disorderly growing branches, thus reducing the success rate of harvesting. This study introduces a fruit and branch detection and three-dimensional (3D) reconstruction method for obstacle avoidance path planning of robots. A new architecture for instance segmentation was developed by replacing the backbone of Mask R-CNN with a tiny network, referred to as “tiny Mask R-CNN”. The tiny Mask R-CNN was trained with a small number of images and used to detect guava fruits and branches. Each detected fruit and branch were converted into a 3D point cloud. It was then hypothesized that guava fruits could be represented by 3D spheres and irregular branches can be approximated by a finite number of 3D cylindrical segments. Based on the proposed hypothesis, a random sample consensus-based sphere fitting method and a principal component analysis-based cylindrical segment fitting method were investigated to reconstruct the fruits and branches from the point clouds. A guava dataset with 304 RGB-D images was collected from the fields and used to validate the developed method. The results showed that the detection F1 score of the tiny Mask R-CNN was 0.518; the F1 score for fruit reconstruction was approximately 0.851 and 0.833 under the 2D- and 3D-fruit metrics, respectively; and the F1 score for branch reconstruction was approximately 0.394 and 0.415 under the 2D- and 3D-branch metrics, respectively. These results confirm that the proposed method can effectively reconstruct the fruits and branches and can, therefore, be used to plan an obstacle avoidance path for harvesting robots.

Canopy segmentation and wire reconstruction for kiwifruit robotic harvesting

Kiwifruits are commercially grown on sturdy support structures such as T-bars and pergolas. Wires are widely used in modern agriculture as an important material for supporting T-bars. It may lead to damage to kiwifruit harvesting end-effector or robot when accessing fruits occluded by branches or wires. Additional development to segment calyxes, branches, and wires will help to achieve higher-level picking strategies. DeepLabV3+ was adopted to segment the fruit calyx, branch, and wire in this work. A method of discrete wire pixels reconstruction was then developed on Progressive probabilistic Hough transform (PPHT) to help sense distribution of the wire. Lines that didn’t meet the constraints, i.e., angle or distance between the lines, were regarded as noise and eliminated. There were 327 images divided into training (261) and testing (66) sets, where the training set was augmented to 1566 images. The dataset was heavily imbalanced where the pixels of calyx, branch, and wire were much fewer than background pixels. For the imbalanced kiwifruit canopy image segmentation, it was proven that the uniform weights assignation method outperformed the median frequency weights. In terms of backbone, ResNet-101 achieved IoUs (intersection over union) of 0.686, 0.709, and 0.424 for calyx, branch, and wire, respectively, and the highest mIoU (mean IoU) of 0.694. It took about 210.0 ms to process a resolution of 512 × 341 pixels image, which could be acceptable for the kiwifruit harvesting robot. The PPHT achieved an correct detection rate of 92.4%, and was competitive in processing time of 6.4 ms/image. Canopy image segmentation can provide a basis for guiding the harvesting end-effector to pick kiwifruits safely, thus improving the harvesting success rate and reducing on-orchard costs.

Real-Time Fruit Recognition and Grasping Estimation for Robotic Apple Harvesting.

Robotic harvesting shows a promising aspect in future development of agricultural industry. However, there are many challenges which are still presented in the development of a fully functional robotic harvesting system. Vision is one of the most important keys among these challenges. Traditional vision methods always suffer from defects in accuracy, robustness, and efficiency in real implementation environments. In this work, a fully deep learning-based vision method for autonomous apple harvesting is developed and evaluated. The developed method includes a light-weight one-stage detection and segmentation network for fruit recognition and a PointNet to process the point clouds and estimate a proper approach pose for each fruit before grasping. Fruit recognition network takes raw inputs from RGB-D camera and performs fruit detection and instance segmentation on RGB images. The PointNet grasping network combines depth information and results from the fruit recognition as input and outputs the approach pose of each fruit for robotic arm execution. The developed vision method is evaluated on RGB-D image data which are collected from both laboratory and orchard environments. Robotic harvesting experiments in both indoor and outdoor conditions are also included to validate the performance of the developed harvesting system. Experimental results show that the developed vision method can perform highly efficient and accurate to guide robotic harvesting. Overall, the developed robotic harvesting system achieves 0.8 on harvesting success rate and cycle time is 6.5 s.

Design and evaluation of a modular robotic plum harvesting system utilizing soft components

AbstractThe human labor required for tree crop harvesting is a major cost component in fruit production and is increasing. To address this, many existing research works have sought to demonstrate commercially viable robotic harvesting for tree crops, though successful commercial products resulting from these have been few and far between. Systems developed for specific crops such as sweet peppers or apples have shown promise, but the vast majority of cultivar types remain unaddressed, and developing a specific system for each one is inefficient. In this study, an easily modifiable development platform for robotic fruit harvesting is presented, this can be used to test specific design choices on different fruit and growing conditions. The system is evaluated in a commercial plum orchard, with no crop modifications. Both a hard and soft gripper are trialed, along with three object detector approaches and two picking motions. Some existing techniques are found to be counterproductive for plums, while soft robotics and persistent target tracking significantly improve performance. The best harvest success rate of 42%, was observed when using the soft gripper with complex motion. This is lower than expected based on prior testing with apples and indicates the difficulty in moving to new fruit types. Unique challenges specific to the plum type and growing style are examined in the context of system module design choices.

Pumpkin harvesting robotic end-effector

The development of an end-effector with a unique harvesting methodology based on the properties of pumpkin was presented in this paper. The components of the end-effector designed using Solidworks software; it dynamically simulated using SAM software; and after several modifications, the final components manufactured and assembled. This end-effector is a five-fingered anthropomorphic hand with electric drive and internal impactive gripping mode, designed to grasp and harvest heavyweight crops with a radius range of 76.2 to 265 mm by consideration of sustainable force distribution. The controlling unit of the system was a PLC system consists of a computer, a position board, an amplifier, a servo motor, switching units, and emergency switches. The coordinate of pumpkins enter the PC manually and after analysis using a controlling algorithm, the PC sends the related commands to the servo motor and switch unit via position board and optical cables. The controlling algorithm developed using the Denavit-Hartenberg (D-H) method. The maximum stress values of the final design were 6.2×107 and 7.8×107N/m2 in the opened and closed mode, respectively. The accuracy, repeatability, harvest possibility, and harvesting success rate were 8.78 mm, 8.32 mm, 92%, and 79%, respectively. The results proved that the fingers components have enough capability under the maximum payload of the system, and the designed end-effector can harvest the different varieties of pumpkin because the range of radius, volume and mass can cover the extracted physical parameter of pumpkins. The results indicated that the designed controlling unit and developed algorithm could control the system properly and do the desired function appropriately.

Constraints to hunting and harvesting elk in a landscape dominated by private land

While the harvest success rate of bull elk in Nebraska has been relatively high in recent years, antlerless harvest success is substantially lower inhibiting the ability of wildlife managers to effectively manage population numbers. We sought to explore differences in hunter behaviour based on the type of elk being hunted as well as how specific constraints affected each type of hunter's ability to hunt and harvest elk. Decreased antlerless-elk harvest rates were likely a product of the reduced frequency in which an antlerless-elk hunter would pay for the opportunity to hunt on property containing a comparatively higher probability of harvest. Antlerless-elk hunters reported being more constrained by time, and felt more strongly that the quantity of elk on the land that they hunted affected their ability to harvest. Our results reinforce the need for wildlife managers to work closely with private landowners and focus hunting pressure on land currently restricted to elk hunting.

Real-Time Visual Localization of the Picking Points for a Ridge-Planting Strawberry Harvesting Robot

At present, the primary technical deterrent to the use of strawberry harvesting robots is the low harvest rate, and there is a need to improve the accuracy and real-time performance of the localization algorithms to detect the picking point on the strawberry stem. The pose estimation of the fruit target (the direction of the fruit axis) can improve the accuracy of the localization algorithm. This study proposes a novel harvesting robot for the ridge-planted strawberries as well as a fruit pose estimator called rotated YOLO (R-YOLO), which significantly improves the localization precision of the picking points. First, the lightweight network Mobilenet-V1 was used to replace the convolution neural network as the backbone network for feature extraction. The simplified network structure substantially increased the operating speed. Second, the rotation angle parameter $\alpha $ was used to label the training set and set the anchors; the rotation of the bounding boxes of the target fruits was predicted using logistic regression with the rotated anchors. The test results of a set of 100 strawberry images showed that the proposed model's average recognition rate to be 94.43% and the recall rate to be 93.46%. Eighteen frames per second (FPS) were processed on the embedded controller of the robot, demonstrating good real-time performance. Compared with several other target detection methods used for the fruit harvesting robots, the proposed model exhibited better performance in terms of real-time detection and localization accuracy of the picking points. Field test results showed that the harvesting success rate reached 84.35% in modified situations. The results of this study provide technical support for improving the target detection of the embedded controller of harvesting robots.

Performance evaluation of harvesting robot for heavy-weight crops

A harvesting robot for heavy-weight crops (HRHC) was developed and evaluated in this study. This robot consists of a robot tractor as a mobile platform, a specifically developed robotic arm for this application, a developed end-effector for pumpkin, and a controlling system. The final evaluations were focused on eight parameters including working space, system resolution, harvesting possibility zone, accuracy, repeatability, harvesting success rate, cycle time, damage rate. The results showed that this robot has harvesting success rate and damage rate of 92% and 0%, respectively. The average cycle time in scenarios 1, 2, and 3 were 58.7s, 41.9s, and 35.1s, respectively. The workspace parameters of a final system including the workspace volume, harvesting surface, and harvesting length were 5.662×109 mm3, 2.86×106 mm2, and 800 mm, respectively, which was 70.6, 81.3, and 99%, of required parameters in the designed system, respectively. The accuracy and repeatability were 4.5, and 5.23 mm, respectively. The system resolution in x, y and z directions were 1 mm, which had a tolerance of 75, 50, and 25 μm, respectively. These results indicate that the system has enough motion resolution, accuracy, and repeatability for harvesting pumpkins. The HCRH system harvest success (100%) was higher than overall average harvest success of previous studies between 1984 and 2012. In during of experimentations no fruit damage was detected. The experiments output meets the determined requirements and the HCRH system found applicable to harvest pumpkin in the field.

Recombinant human thrombopoietin improves the efficacy of intermediate-dose cyclophosphamide plus granulocyte colony-stimulating factor in mobilizing peripheral blood stem cells in patients with multiple myeloma: A cohort study.

The combination of intermediate-dose cyclophosphamide (ID-CTX) and granulocyte colony-stimulating factor (G-CSF) fails to mobilize peripheral blood stem cells (PBSCs) in approximately 20% of treated patients with multiple myeloma (MM).In this cohort study, patients with MM underwent PBSC mobilization with either an ID-CTX plus G-CSF plus recombinant human thrombopoietin (rhTPO) regimen (72 patients; TPO group), or an ID-CTX plus G-CSF regimen (70 patients; non-TPO group).In the TPO group, the median CD34+ harvest was 5.36 × 106 per kg of body weight (0.50–22.39 × 106 per kg of body weight), with a harvest success rate of 91.7% (66/72), and an excellence rate of 55.6% (40/72). In the non-TPO group, the median CD34+ harvest was 3.30 × 106 per kg of body weight (0.20–21.14 × 106 per kg of body weight), with a harvest success rate of 75.7% (53/70), and an excellence rate of 25.7% (18/70). The median count of the CD34+ cells collected, success rate of collection, and excellence rate of collection were significantly higher in the TPO group than in the non-TPO group (P=.0001, P=.01, and P = .0001, respectively). Time to granulocyte and platelet engraftment was faster among patients in the TPO group than that in those from the non-TPO group. No platelet engraftment delay (>21 days) was observed among patients in the TPO group, while 3 patients in the non-TPO group displayed delayed platelet engraftment.Adding rhTPO to the ID-CTX chemotherapy plus G-CSF regimen improved treatment efficacy in mobilizing PBSCs for autologous hematopoietic stem cell transplantation.

METODE PERHITUNGAN TINGKAT KEBERHASILAN PANEN BROILER BERDASARKAN PERFORMANCE INDEX (PI) PADA GRUP TERNAK DI KOTA PRABUMULIH

In this study, we tried to propose a method to assess the success of the harvest broiler using internal factors found on farms in Prabumulih. To be able to measure the success rate of harvest broiler, we use the Performance Index (PI) as a parameter. By looking at the results of measuring the degree of success of the crop expected broiler breeder who owns the farm, especially in Prabumulih, can better utilize and maximize the potential that can increase crop yields. After going through a testing phase, the percentage of harvest success rate reached 90% when the results of calculation of the value of PI reached the high category. This proves that by looking at the value of PI, farmers, especially in the farm group in Prabumulih can predict the success rate of harvest in a period. By calculating the value of PI and learn all variables breeders can develop appropriate measures to avoid the occurrence of crop failure. With reference to the value of PI, farmers can also make better planning in preparing the development of broiler chickens for the next period.Key words :success rate, Performance Index, Broiler