Pepper Harvesting Research Articles

Online Detection of Loading Capacity in Mechanized Pepper Harvesting Using Ultrasonic Sensors

This study addresses the challenge of scheduling coordination between harvesters and transfer vehicles during the mechanized harvesting of processing peppers. An online detection method for assessing the loading capacity during harvesting was proposed, employing non-contact distance sensors to measure the stacking height of peppers in the hopper in real time. This measurement was used to calculate the loading capacity of the transfer vehicle for peppers. This study compared and analyzed the detection accuracy of ultrasonic, infrared distance, and light detection and ranging sensors to identify the most suitable sensor for detecting the stacking height of peppers, and establish an optimal detection model for the loading capacity of peppers in transfer vehicles. The results indicated that ultrasonic sensors achieved the highest accuracy, with a maximum absolute error of 11.0 mm and a standard deviation of 8.5 mm in detecting the stacking height of peppers. When three symmetrically mounted ultrasonic sensors were used, the maximum error and standard deviation for the mean stacking height under varying lifting speeds were 37.0 mm and 15.5 mm, respectively. The developed model’s relative error in detecting the pepper loading capacity decreased to less than 2% when the fed volume exceeded 0.4 m3. These findings provide a basis for scheduling decisions in the coordination of pepper harvesters and transfer vehicles.

A Novel Method on Recognizing Drum Load of Elastic Tooth Drum Pepper Harvester Based on CEEMDAN-KPCA-SVM

The operational complexities of the elastic tooth drum pepper harvester (ETDPH), characterized by variable drum loads that are challenging to recognize due to varying pepper densities, significantly impact pepper loss rates and mechanical damage. This study proposes a novel method integrating complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), kernel principal component analysis (KPCA), and a support vector machine (SVM) to enhance drum load recognition. The method consists of three principal steps: the initial experiments with ETDPHs to identify the critical factors affecting drum load and to formulate classification criteria; the development of a CEEMDAN-KPCA-SVM model for ETDPH drum load recognition, where drum spindle torque signals are processed by CEEMDAN for decomposition and reconstruction, followed by feature extraction and dimensionality reduction via KPCA to refine the model’s accuracy and training efficiency; and evaluation of the model’s performance on real datasets, highlighting the improvements brought by CEEMDAN and KPCA, as well as comparative analysis with other machine learning models. The results describe four load conditions—no load (mass of pepper intake (MOPI) = 0 kg/s), low load (0 < MOPI ≤ 0.658 kg/s), normal load (0.658 < MOPI ≤ 1.725 kg/s), and high load (MOPI > 1.725 kg/s)—with the CEEMDAN-KPCA-SVM model achieving 100% accuracy on both training and test sets, outperforming the standalone SVM by 6% and 12.5%, respectively. Additionally, it reduced the training time to 2.88 s, a 10.9% decrease, and reduced the prediction time to 0.0001 s, a 63.6% decrease. Comparative evaluations confirmed the superiority of the CEEMDAN-KPCA-SVM model over random forest (RF) and gradient boosting machine (GBM) in classification tasks. The synergistic application of CEEMDAN and KPCA significantly improved the accuracy and operational efficiency of the SVM model, providing valuable insights for load recognition and adaptive control of ETDPH drum parameters.

Development of a Bionic Picking Device for High Harvest and Low Loss Rate Pod Pepper Harvesting and Related Working Parameter Optimization Details

Addressing the issues of low harvesting efficiency and high losses in current pod pepper harvesters, this study presents the design of a bionic comb finger pod pepper picking device and an inclined double-spiral bell pepper harvester to improve the harvest rate and reduce loss rate. Through the utilization of discrete element simulation software EDEM, a discrete element model for pod peppers is established. Additionally, a simulation platform for ground drop loss during pod pepper picking is developed, enabling exploration of the movement trajectory and velocity changes of the pod pepper elements. The study also conducts an analysis on the impact of the speed of the picking rollers X1, the feeding speed of pod peppers X2, and the spacing between the two picking rollers X3 on ground drop losses. Based on the results of the single-factor test, the Box–Behnken response surface test was used to optimize the working parameters of the picking device, which resulted in the optimal combination of the working parameters of the picking device: the speed of the picking rollers was 680.41 rpm, the feeding speed of the pod peppers was 0.5 m/s, and the spacing between the two picking rollers was 12 mm, which resulted in the loss rate of pod peppers on the floor of the ground being 3.526%.

Effects of Application of Different Organic Materials on Phosphorus Accumulation and Transformation in Vegetable Fields

Presently, the improvement of soil organic matter is the basis to ensure food security, but the accumulation and transformation characteristics of soil phosphorus (P) as affected by organic matter remain unclear. The accumulation, transformation, and migration characteristics of soil P in different soil layers of vegetable fields were researched under the application of organic materials. Six treatments were set up in the experiment:control (no fertilization), traditional fertilizer application by farmers, biochar, chicken manure, food waste, and straw application. Available phosphorus (Olsen-P), water-soluble phosphorus (CaCl2-P) content, soil phosphorus forms, soil organic matter (SOM), and pH were determined during the pepper harvest period. In the 0-5 cm and 5-10 cm soil layers, the available phosphorus content of traditional fertilization of farmers was higher, and the available phosphorus content of the four organic materials was in the order of straw > biochar > chicken manure > food waste. Compared to that with food waste, the straw and biochar treatments increased soil available phosphorus by 59.6%-67.3% and 29.1%-36.9%, respectively. The straw treatment could easily enhance the soil labile P pool, and soil labile P in the 0-5 cm soil layer increased by 47.3% and 35.1% compared with that under the chicken manure and food waste treatments, respectively. With the increase in soil depth, the proportion of available phosphorus in the chicken manure treatment decreased the least, and available phosphorus of the 20-30 cm soil layer accounted for 55.9% of the topsoil layer but only accounted for 16.0%-34.0% under treatment with the other three materials. Compared with that under the traditional fertilization of farmers, the pH significantly increased by 0.18-0.36 units after the application of organic fertilizer, and the pH of the chicken manure and food waste treatments was significantly higher than that of biochar and straw (P < 0.05). SOM content under the biochar treatment significantly increased by 7.7%-17.6% compared to that under the other three organic materials. Among the four organic materials, the straw treatment boosted the labile P pool the most, which was conducive to the rapid increase in plant-available P. Phosphorus was most likely to migrate downward under the chicken manure treatment. In the field management based on soil fertility enhancement, the application of biochar could not only improve soil pH and SOM but also avoid excessive accumulation of phosphorus in the surface layer, which decreases environmental risks.

Cayenne Pepper Seed Partnership Sustainability Strategy (Case Study of "You Seed" Company Jember East Java)

Jember is one of the Cayenne pepper-producing districts in East Java. It is among the top 10 cayenne pepper producers in East Java and has the potential to contribute raw materials for cayenne pepper seeds. One of the superior seed-producing companies in Jember Regency is PT. Benih Unggul Sejati or You Seed Company. The company partners with cayenne pepper farmers in Jember to get raw seed materials. The higher demand for seeds is a problem, namely the farmers' lack of raw seed materials in stock. Farmers often provide seeds from their cayenne pepper harvest in quantities below the target set by the company. The sampling method uses a total sampling of 28 respondents. The data collection method uses observation, interview, and literature study methods. The research methods used are descriptive and quantitative. The results of the study are the sustainability strategy of the partnership that exists between the company PT. Benih Unggul Sejati and cayenne pepper seed farmers, namely the company strengthening emotional relationships with partner farmers through increasing the frequency of visits to partner farmers' land; the company provides capital loan assistance of up to 50% of the total cost of cayenne pepper farming expenses to partner farmers; utilizing waring technology in the cayenne pepper cultivation process; expanding the sales market for cayenne pepper seeds both nationally and internationally and cooperating with fellow IPBH member companies.

Design and performance analysis of soft pneumatic manipulator‐based linear cutter and stem holder for sweet pepper harvesting

AbstractEfficient automation of stem‐cutting processes in agricultural practices is crucial but little‐studied for improving productivity and reducing labor‐intensive tasks. To address this need, we present a customized stem cutter and stem holder designed explicitly for sweet pepper harvesting. The stem cutter comprises a fixed plate and a moving plate with a blade, which moves in a single plane at an upward angle of 30° toward the fixed plate. The movement of the blade is facilitated by a soft bellows pneumatic actuator constructed using silicon rubber material. By applying air pressure from a DC air pump, the actuator extends forward, causing the blade part to make contact with the fixed plate and effectively cut the stem. The stem holder holds the stem securely before cutting. It contains two inflatable manipulators made of soft materials that expand inwards when air‐filled, ensuring that the stem is securely held and readily collected after harvest. Both simulation and experimental results are presented, in which the average cutting time was 2.356 s. We believe that these innovations represent an efficient method for automating the stem‐cutting process in sweet pepper cultivation and thus significantly contribute to the field of agricultural robotics.

CHEMICAL CONTROL OF HELICOVERPA ARMIGERA AND SPODOPTERA EXIGUA AND ITS EFFECTS ON THE QUALITATIVE INDICATORS OF PEPPERS

In the conditions of Khoronk settlement of Armavir marz, Armenia, Helicoverpa armigera (syn. Chloridea armigera Hübner) and Spodoptera exigua (syn. Caradrina exigua Hübner) are the chief pests damaging the pepper plants in their larval stage. In crop season 2022, experiments on these pests in pepper fields tested the insecticides that mainly control the insects. The insecticides tested against Helicoverpa armigera and Spodoptera exigua included Eforia, Belt, Decis F-Lux, and Spintor. The Arrivo served as standard, and the experiment also had a control. As a result of the conducted research, the insecticides Belt, Decis F-Lux, and Eforia showed the highest biological and economic efficiency in controlling Helicoverpa armigera and Spodoptera exigua. The study also ran a biochemical analysis of the pepper harvest to know the effects of the applied insecticides on the peppers’ quality indicators.

Simulation Analysis of Working Circuit Performance of Mountain Pepper Harvester Based on Improved Load-Sensitive System

China’s Guizhou is a typical karst landscape province with high production of chili pepper, but it is mostly planted in mountainous areas, while manual harvesting of chili pepper has the deficiencies of high labor intensity, low efficiency, and high labor cost; in addition, there is no harvesting machinery applicable to the dense planting pattern of the chili pepper in mountainous areas in China. The fully hydraulic mountain track-based self-propelled pepper harvester 4JZ-1.0A is designed to solve the above problems. The pepper harvester spiral comb picking head is an important part of the whole machine design, the design of the hydraulic system of the working circuit of the picking head is the key to realizing the hydraulic control part of the whole system. In this paper, the working principle diagram of the improved load-sensitive hydraulic system is designed and analyzed for the study of whether the working circuit of the pepper picking head of the pepper machine can meet the requirements of mountain operation, taking the working circuit of the mountain pepper harvester as the research object. In addition, the load-sensitive pump model and the simulation model of the whole working circuit are established by the AMESim platform 2019.2 (Siemens simcenter amesim). The operating performance of the system under variable flow conditions, variable load conditions, and an improved sensitive system is analyzed. The simulation results show that the improved load-sensitive system can effectively reduce the oscillation and cavitation during cylinder operation and improve the system efficiency and the performance and service life of the components. The performance of the hydraulic system of the working circuit of the mountain pepper harvester was verified in the test, meeting the requirements of working use. This provides a theoretical basis for the improvement and optimal design of a mountain pepper harvester hydraulic system.

Design and Testing of Bionic-Feature-Based 3D-Printed Flexible End-Effectors for Picking Horn Peppers

To solve the problems of poor adaptability and large sizes of pepper harvesting machinery in facility agriculture to enhance the efficiency and quality of pepper harvesting and ultimately boost farmers’ income, several flexible end-effectors were designed. These end-effectors were tailored to the unique morphologies of horn peppers, drawing inspiration from biomimicry. Subsequently, we conducted experimental verification to validate their performance. Four biological features, namely, the outer contours of a Vicia faba L. fruit, an Abelmoschus esculentus fruit, the upper jaw of a Lucanidae, and a Procambarus clarkii claw, were selected and designed using 3D software. In order to ascertain the structural viability and establish the initial design framework for the test end-effector, a simulation analysis to evaluate the strength and deformation of the flexible end-effector under various pepper-picking conditions was conducted. PLA material and 3D printing technology were used to create the end-effector, and, together with the mobile robotic arm platform ROSMASTER X3 PLUS, they were used to build a test prototype; a pepper tensile test was performed to pre-determine the reasonableness of the picking program, and then a prototype was created for the actual picking of the peppers to compare the picking effectiveness of several types of flexible end-effectors. In six experiments, each flexible end was harvested for 120 horn peppers. The Vicia faba L. flexible end-effector had the lowest average breakage rate. The average breakage rate was 1.7%. At the same time, it had the lowest average drop rate. The average drop rate was 3.3%. The test results indicated that the flexible end-effector that emulated the outer contour characteristics of the Vicia faba L. fruit demonstrated the most favorable outcomes. This design exhibited high working efficiency and the lowest rates of fruit breakage and fruit drops, surpassing both the artificial and traditional machine picking methods and effectively fulfilling the requirements for pepper-picking operations in facility agriculture.

Rapid detection of Yunnan Xiaomila based on lightweight YOLOv7 algorithm.

Real-time fruit detection is a prerequisite for using the Xiaomila pepper harvesting robot in the harvesting process. To reduce the computational cost of the model and improve its accuracy in detecting dense distributions and occluded Xiaomila objects, this paper adopts YOLOv7-tiny as the transfer learning model for the field detection of Xiaomila, collects images of immature and mature Xiaomila fruits under different lighting conditions, and proposes an effective model called YOLOv7-PD. Firstly, the main feature extraction network is fused with deformable convolution by replacing the traditional convolution module in the YOLOv7-tiny main network and the ELAN module with deformable convolution, which reduces network parameters while improving the detection accuracy of multi-scale Xiaomila targets. Secondly, the SE (Squeeze-and-Excitation) attention mechanism is introduced into the reconstructed main feature extraction network to improve its ability to extract key features of Xiaomila in complex environments, realizing multi-scale Xiaomila fruit detection. The effectiveness of the proposed method is verified through ablation experiments under different lighting conditions and model comparison experiments. The experimental results indicate that YOLOv7-PD achieves higher detection performance than other single-stage detection models. Through these improvements, YOLOv7-PD achieves a mAP (mean Average Precision) of 90.3%, which is 2.2%, 3.6%, and 5.5% higher than that of the original YOLOv7-tiny, YOLOv5s, and Mobilenetv3 models, respectively, the model size is reduced from 12.7 MB to 12.1 MB, and the model's unit time computation is reduced from 13.1 GFlops to 10.3 GFlops. The results shows that compared to existing models, this model is more effective in detecting Xiaomila fruits in images, and the computational complexity of the model is smaller.

Green Sweet Pepper Fruit and Peduncle Detection Using Mask R-CNN in Greenhouses

In this paper, a mask region-based convolutional neural network (Mask R-CNN) is used to improve the performance of machine vision in the challenging task of detecting peduncles and fruits of green sweet peppers (Capsicum annuum L.) in greenhouses. One of the most complicated stages of the sweet pepper harvesting process is to achieve a precise cut of the peduncle or stem because this type of specialty crop cannot be grabbed and pulled by the fruit since the integrity and value of the product are compromised. Therefore, accurate peduncle detection becomes vital for the autonomous harvesting of sweet peppers. ResNet-101 combined with the feature pyramid network (FPN) architecture (ResNet-101 + FPN) is adopted as the backbone network for feature extraction and object representation enhancement at multiple scales. Mask images of fruits and peduncles are generated, focused on green sweet pepper, which is the most complex color variety due to its resemblance to the background. In addition to bounding boxes, Mask R-CNN provides binary masks as a result of instance segmentation, which would help improve the localization process in 3D space, the next phase of the autonomous harvesting process of sweet peppers, since it isolates the pixels belonging to the object and demarcates its boundaries. The prediction results of 1148 fruits on 100 test images showed a precision rate of 84.53%. The prediction results of 265 peduncles showed a precision rate of 71.78%. The mean average precision rate with an intersection over union at 50 percent (mAP@IoU=50) for model-wide instance segmentation was 72.64%. The average detection time for sweet pepper fruit and peduncle using high-resolution images was 1.18 s. The experimental results show that the proposed implementation manages to segment the peduncle and fruit of the green sweet pepper in real-time in an unmodified production environment under occlusion, overlap, and light variation conditions with effectiveness not previously reported for simultaneous 2D detection models of peduncles and fruits of green sweet pepper.

Vibration Test and Analysis of Crawler Pepper Harvester under Multiple Working Conditions

Nowadays, many scholars at home and abroad have studied the vibration of agricultural machinery, especially harvesting machinery. However, this research has lacked the analysis of vibration characteristics of harvesters under the condition of multi-vibration excitation in field work. Therefore, by taking the chassis frame and main vibration sources of a 4JZ-1700 crawler pepper harvester as the research object, this paper aims to investigate the vibration characteristics of the pepper harvester under different working conditions, and the impact of the excitation of various working parts on the chassis frame. Firstly, a modal simulation was carried out with the modal module of ANSYS Workbench to study the natural frequency of the chassis frame. The results demonstrated that the natural frequency of the chassis frame was within 23–76 Hz. A DH5902 dynamic signal acquisition instrument was used to collect vibration signals from seven measuring points under different working conditions of the whole machine, and the collected time domain signals were extracted by Fourier transform. According to the time domain signal, the amplitude at the engine support was the largest under the static no-load condition, and the transmission of engine vibration was attenuated to a certain extent, which imposes a significant effect on the vibration isolation and vibration reduction of the harvester frame. Under the field walking condition, the amplitudes of the left front of the chassis frame and the driving shaft of the cleaning separation device were abnormal, which was mainly attributed to the unequal road surface and the high center of gravity of the cleaning separation device. Through frequency domain analysis, it can be found that the main vibration frequency of most measuring points of the harvester was close to the vibration frequency of the engine under the static no-load condition, and the excitation frequency of most measuring points approximated to the working frequency of the picking drum and the cleaning separation device under the field walking condition. In addition, there were plenty of phenomena in which the main frequency of vibration was detected in the high frequency region above 200 Hz, with messy frequency values. This is due to the poor lubrication of the bearing part of the harvester, causing intense friction between the rotating shaft and the bearing, which also drives the high frequency vibration of the chassis frame. In general, this study can provide a method reference for vibration analysis of agricultural machinery and propose effective measures to reduce vibration based on the conclusions.

Characterization of Pepper Ripeness in the Field Using Hyperspectral Imaging (HSI) with Back Propagation (BP) Neural Network and Kernel Based Extreme Learning Machine (KELM) Models

Due to the economic differences of chili peppers at different maturity levels and the absence of differentiation of maturity in existing harvesting processes, it is crucial to accurately identify the maturity during harvesting for improving economic benefits. This paper investigated pepper maturity recognition models using hyperspectral technology to realize intelligent pepper harvesting with high accuracy and monitor maturity. Hyperspectral data (400–1000 nm) for field line peppers were collected, preprocessed using normalization, Savitzky-Golay convolutional smoothing, and standard normal variable transformation and subsequently used to train back propagation neural network (BP) and kernel based extreme learning machine (KELM) models. Principal component analysis (PCA) was used to reduce data dimensionality and identify characteristic spectral wavelengths for pepper maturity at 862.2, 676.9, 578.1, and 980.7 nm; and BP, PCA-BP, KELM, and PCA-KELM models were subsequently established. The precision for the KELM and PCA-KELM models (99.5% and 97.3%, respectively) was superior to the other two models; however, the PCA-KELM model used only four of the feature wavelengths as inputs, hence it required only 1/44 the data compared with the KELM model. Thus, the PCA-KELM model achieved high recognition accuracy and training speed, offering an effective method to discriminate pepper ripeness based on hyperspectral features.

Physical Properties of Black Pepper for designing a Robotic Black Pepper Harvester

Agricultural robots were designed based on the characteristics of the target crop. In this study, two varieties of black pepper including Kaimunda and Panniyur 1 were evaluated for physical parameters to design a robotic black pepper harvester. The main objective of this study was to measure and analyze the physical properties of black pepper to design the robotic pepper harvester. In this regard, studies were conducted for pepper spike, pepper peduncle, pepper berry, and pepper vine separately. The results showed that the average length and diameter of pepper spikes of Karimunda and Panniyur 1 respectively as 10.4 cm and 1.10 cm; and 12.8 cm and 1.30 cm. In an average the tested peppers have a berry diameter of 0.42 cm and RGB value of (20, 39, 3) - (255, 224, 111) for Karimunda variety and 0.59 cm and (35, 54, 10) - (255, 240, 100) for Panniyur 1 variety. Based on the shear strength analysis, at a cutting speed of 0.1 mm s -1, the average shear strength of the peduncle was obtained as 1718.6 N mm-2 for Karimunda and 1671 N mm -2 for Panniyur 1. And at cutting speed 1 mm s-1, the average shear strength peduncle of Karimunda and Panniyur 1 respectively is 1535 N mm-2 and 1544 N mm-2. The average length of the peduncle of Karimunda and Panniyur 1 respectively as 1.2 cm and 1.3 cm, and the diameter of both varieties was 0.17 cm. The maximum leaf coverage of the pepper vine ranges from 10 to 91, and the 95th percentile of the data was 76 cm.

Research Status and Development Trend of Intelligent Mechanized Pepper Harvesting

Since the reform and opening up, the mechanization level of China's agricultural production has been significantly improved. Intelligent mechanization of agricultural production is an inevitable trend for future social development. It is based on intelligent equipment, with data and knowledge as the core elements to enable the deep integration of modern science and technology with agriculture. This enables digital perception, intelligent decision making, and precise operation of the whole agricultural production process, further improving labor productivity, land output rate and resource utilization rate. This paper introduces the development status of agricultural production in China and abroad with pepper harvesting, analyzes the necessity of changing China's agricultural production to intelligent mechanized production, summarizes the opportunities and challenges in intelligent mechanization, and accordingly puts forward the development direction and development route of China's agricultural production. To promote the transformation of China's agricultural production mode from mechanization to wisdom, this paper puts forward countermeasure suggestions such as strengthening the construction of agricultural machinery equipment R&D and innovation system, strengthening the construction of agricultural machinery promotion system, strengthening the construction of agricultural machinery socialization service system, and accelerating the construction of wisdom agriculture demonstration area.

Effect of aerial application of adjuvants on pepper defoliant droplet deposition and efficacy of defoliation sprayed by unmanned aerial vehicles.

Defoliant spraying is an important aspect of the mechanized processing of pepper harvesting. Complete and uniform spraying of defoliant could improve the quality of defoliation and reduce the impurity content in processing pepper. In this study, we assessed the effect of aerial spraying of adjuvants on physicochemical properties of defoliant solution and droplet deposition when using an unmanned aerial vehicle (UAV) for defoliation spraying. The results showed that Puliwang was a better aerial spray adjuvant suitable for spraying defoliants for processing pepper using UAVs, with a higher defoliation rate and better droplet deposition. Although the YS-20 adjuvant had a higher droplet deposition amount (0.72 μg/cm2) in the middle layer, its performance was poor in droplet size, density, and coverage. The size and density of the droplets added with the Manniu were basically the same as the Puliwang, even the distribution uniformity was better (the CV of the upper canopy layer was only 33.6%), but the coverage rate was poor. In the treatment with AS-901N, there was no marked increase in droplet size, so evaporation and drift were not improved, eventually resulting in a lower defoliation rate. Puliwang had the highest comprehensive score, followed by AS-910N, YS-20, and Manniu.

An Overview of End Effectors in Agricultural Robotic Harvesting Systems

In recent years, the agricultural sector has turned to robotic automation to deal with the growing demand for food. Harvesting fruits and vegetables is the most labor-intensive and time-consuming among the main agricultural tasks. However, seasonal labor shortage of experienced workers results in low efficiency of harvesting, food losses, and quality deterioration. Therefore, research efforts focus on the automation of manual harvesting operations. Robotic manipulation of delicate products in unstructured environments is challenging. The development of suitable end effectors that meet manipulation requirements is necessary. To that end, this work reviews the state-of-the-art robotic end effectors for harvesting applications. Detachment methods, types of end effectors, and additional sensors are discussed. Performance measures are included to evaluate technologies and determine optimal end effectors for specific crops. Challenges and potential future trends of end effectors in agricultural robotic systems are reported. Research has shown that contact-grasping grippers for fruit holding are the most common type of end effectors. Furthermore, most research is concerned with tomato, apple, and sweet pepper harvesting applications. This work can be used as a guide for up-to-date technology for the selection of suitable end effectors for harvesting robots.

Study on the Dynamic Cutting Mechanism of Green Pepper (Zanthoxylum armatum) Branches under Optimal Tool Parameters

In order to design a branch-cutting type green pepper harvesting device, we firstly study the whole process of straight knife green pepper cutting to reveal the cutting mechanism and provide theoretical guidance to the design. A finite element model was established for the cutting of pepper branches across the distance, and single-factor and multi-factor finite element simulation tests were conducted on the knife feed angle, tool edge angle, and knife feed speed of the working parts of the pepper cutting and harvesting device. The results of the experiment were analyzed by ANOVA, which showed the different degrees of importance of these factors, and the optimal parameters were obtained by response surface methodology (RSM). With the optimal parameters selected, the predicted maximum cutting force and cutting completion were 803.35 N and 98.58%, respectively, this satisfies the efficiency and economy requirements of agricultural machinery design. In addition, the cutting force of green pepper branches was analyzed and a theoretical mechanical model was developed to help us understand the variation of cutting force numerically. The stress–strain system, high-speed photography system and numerical prediction were innovatively combined to observe and measure the stress and other key state variables in the cutting process in detail, summarize their changing trend, and establish a time-based monitoring and comparison model. The above research results can provide a reference for the design of green pepper branch cutting and harvesting devices, such as direct guidance on the selection of working parameters, materials, etc., and guidance on the operation in actual work.

Rollover Safety and Workable Boundary Suggestion of an Agricultural Platform with Different Attachments

Overturning accidents in agriculture offroad vehicles have been reported annually around the world. Safety structures, such as rollover prevention, have been adopted to mitigate these accidents. Despite this, accidents remain persistent but less fatal. This study investigated the safe driving boundary for a multi-purpose platform with attachments (cargo, pepper harvester). Mathematical formulations of the roll and pitch motions for multi-purpose platforms were created. The critical overturning angle, at five load levels for each machine (0, 50, 100, 150, 200 kg), was determined using Recurdyn, a dynamic analysis software. Regression analysis of each coordinate of the center of gravity was conducted to verify the most critical coordinate against gaining loads. The critical overturning angle for the X and Y directions was found. The cargo and pepper harvester had 28.64° and 21.04° of critical overturning angles in the Y direction, respectively, with a full loading state while climbing a steep hill. The regression model of the X, Y, and Z coordinates of CoG suggested that the most critical coordinate of the CoG against the loads was in the Y coordinate for the pepper harvester and the X direction for cargo. This study could be applied to develop an awareness safety system that warns the operator of the risk of a fatal accident.

CFD Analysis and Validation of a Foreign Material Winnowing Machine for Pepper Harvester

The winnowing machine of chili pepper harvesters was developed to reduce the potential problem of low pepper stem and fruit separation. The developed winnowing machine was combined with two impellers and a center bearing to prevent a strain on the drive shaft and to ensure durability. The terminal velocity of chili pepper was measured, and an aerodynamic analysis was conducted based on this winnowing machine. A CFD (Computational Fluid Dynamics, Ansys Fluent 2020 R1) analysis was conducted for three levels of discharge port guide form (0, 3, and 5 guides) and three levels of rotating speed (1600, 1800, and 2000 RPM) of a winnowing machine designed utilizing aerodynamic analysis results. A validation test was conducted by fabricating a winnower test device. As for aerodynamic analysis conducted using measured values of terminal velocity, chili pepper fruits were collected at an outlet wind speed lower than 17.5 m/s and chili pepper branches were separated at a speed higher than 12.5 m/s. As a result of CFD analysis, the wind speed deviation at outlets of the 0-, 3-, and 5-guide depending on the rotating speed appeared to be 15.8, 1.4, and 1.0 m/s on average, respectively. The result of the CFD analysis showed values higher than wind speeds of the actual winnower test device by a minimum of 0 and a maximum of 2.4 m/s. Through the CFD analysis and the wind speed validation test of the winnower test device, optimal conditions to separate foreign materials were found to be a winnowing machine at a rotating speed of 1800 RPM with a discharge port having three guides or a winnowing machine at a rotating speed of 2000 RPM with a discharge port having five guides.