Vegetable Harvesting Research Articles

Geoeconomic modelling: southwestern trend of vegetable growing in Russia

Relevance. Modern globalization, internationalization, regional competitive differentiation actualize geoeconomic modeling methods for geopolitical planning, management and control. The novelty of the proposed solutions is the proposed concept, based on the translation of the ideas of theoretical mechanics into economics.Methodology. Considering the annual values of statistical indicators of territorial concentrates as a system of geographically distributed objects (subjects of the Russian Federation), it is possible to determine the geographic barycenter. Knowing the values of the geocoordinates of the barycenter, it is possible to determine the tendencies and trend of the barycenter movement as an indicator of the dynamics of the system under consideration through the analysis of the dynamics of these coordinates. Based on Russtat data on the annual gross harvest of agricultural crops (vegetables), population by constituent entities of the Russian Federation for the period 1991-2021, data on the coordinates of the administrative centers of the constituent entities of the Russian Federation, the values of the geographic coordinates of the barycenter of vegetable growing in Russia were calculated.Results. For 2021, the geographic coordinates of the barycenter of vegetable growing in Russia are 50.30° N and 49.49° E with an average regional value with an average regional annual vegetable har-vest of 1722.52 million centners/year and fluctuations in the coordinates of the barycenter in the zone of 3-4 geographic degrees or three hundred territorial kilometers with a pronounced south-west trend Analysis of the dynamics of the barycenter for the period 1991-2021. will allow to reveal a slight shift of the vegetable growing barycenter to the south by 3.5 degrees with an increase in the average regional vegetable harvest by 124.11 million centners per year for each degree (or the all-Russian by more than 9,500 million centners per year for each degree to the south), a slight shift of the vegetable growing barycenter to the west by 4.5 degrees with an increase in the average regional vegetable har-vest by 86.5 million centners per year per degree (or the All-Russian by more than 6,660 million cent-ners per year for each degree to the west). For the period 1991-2021, two opposite trends in the devel-opment of vegetable growing are distinguished. In the period from 1991 to 2000, the movement of the coordinate of the vegetable growing barycenter went in the direction of the northeast of Russia, in the period 2000 to 2021 -- on the contrary, to the southwest.The conclusion is made about the existence of a relationship between the development of vegetable growing and the geopolitics of Russia in the period under review. The obtained method and results can be used in strategic state management of agricultural production in Russia.

The Response of Inorganic and Organic Fertilizers on Growth, Yield and Quality of Cluster Bean [Cyamopsis tetragonoloba (L.) Taub.] in the Village Chikkenahalli of Challakere Taluk, Chitraduraga District

Cluster bean is one of the significant yet underutilized leguminous vegetables belonging to family fabaceae. It is known by various names across the country such as gaur, chavli kaayi, gori kaayi, khutt, govar, and kothavare. A field experiment on cluster bean was carried out during the summer season of the year 2024 at the farm of P. L. Dodda Rangegowda in Chikkenahalli of Challakere taluk, Chitradurga district, Karnataka. The primary objectives of the study are to examine the effects of organic and inorganic fertilizers on the productivity and growth characteristics of cluster bean; to identify the advantages and disadvantages of these fertilizers in relation to productivity, soil fertility, and environment and further to review the effects of mixed organic and inorganic fertilizers on yield and soil fertility. The experiment was laid out in a randomized block design with four replications and five treatments which comprised of 100 percent organic (FYM and chicken manure), 100 percent inorganic (NPK), 50 percent organic + 50 percent inorganic, 100 percent organic + jeevamruta and control. The results revealed that, treatment T3 outperformed the others, showing the highest values for plant height, number of branches per plant, days to first flowering, days to first vegetable harvest, pod length, pod width, yield per plant, yield per plot, and yield per hectare, while the control recorded the lowest values. Soil properties such as pH, EC, SOC and NPK levels improved when combination of organic and inorganic fertilizers were applied together. Based on these results, it can be concluded that, the application of organic manures, along with chemical fertilizers, positively enhanced the vegetative growth, reproductive growth, yield, quality of cluster bean and soil parameters.

Scheduling of Collaborative Vegetable Harvesters and Harvest-Aid Vehicles on Farms

Transporting harvested vegetables in the field or greenhouse is labor-intensive. The utilization of small harvest-aid vehicles can reduce non-productive time for farmers and improve harvest efficiency. This paper models the process of harvesting vegetables in response to non-productive waiting delays caused by the scheduling of harvest-aid vehicles. Taking into consideration harvesting speed, harvest-aid vehicle capacity, and scheduling conflicts, a harvest-aid vehicle scheduling model is constructed to minimize non-production waiting time and coordination costs. Subsequently, to meet the collaborative needs of harvesters, this paper develops a discrete multi-objective Jaya optimization algorithm (DMO-Jaya), which combines an opposition-based learning mechanism and a long-term memory library to obtain scheduling schemes suitable for agricultural environments. Experiments show that the studied model can schedule harvest-aid vehicles without conflicts. Compared to the NSGA-II algorithm and the MMOPSO, the DMO-Jaya algorithm demonstrates a better diversity of solutions, resulting in a shorter non-productive waiting time for harvesters. This research provides a reference model for improving the efficiency of vegetable harvesting and transportation.

How to Prepare Agriculture Operations under the Impact of LLM Technology (Focusing on the Perilla Vegetable Harvest Mobile Robot and Gripper)

How to Prepare Agriculture Operations under the Impact of LLM Technology (Focusing on the Perilla Vegetable Harvest Mobile Robot and Gripper)

Chitosan-based functional coatings for postharvest processing of tomatoes

The review article includes information on the use of coatings, both from chitosan itself and in combination with other polysaccharides and functional compounds, for postharvest processing, mainly of tomatoes, to extend the shelf life of harvested produce. During harvesting of fruits and vegetables, losses due to fungal infection can be as high as 50%. To prevent yield losses and preserve fruit quality, various methods of postharvest treatment are used. Currently, the most effective means used to control postharvest diseases are synthetic fungicides, but their uncontrolled use negatively affects human health and the environment. Recently, there has been a continuing trend to increase the use of natural antimicrobial agents. Such natural compounds can be chitin and chitosan, they are renewable, biodegradable, low toxicity and safe for consumers and the environment. The film-forming ability of chitosan and its antimicrobial, antioxidant properties are important for obtaining functional coatings for postharvest processing of fruits.

Survival of Twelve Pathogenic and Generic Escherichia coli Strains in Agricultural Soils as Influenced by Strain, Soil Type, Irrigation Regimen, and Soil Amendment

Biological soil amendments of animal origin (BSAAO) play an important role in agriculture but can introduce pathogens into soils. Pathogen survival in soil is widely studied, but data are needed on the impacts of strain variability and field management practices. This study monitored the population of 12 Escherichia coli strains (generic, O157, and non-O157) in soils while evaluating the interactions of soil type, irrigation regimen, and soil amendment in three independent, greenhouse-based, randomized complete block design trials. Each E. coli strain (4–5 log10 CFU/g) was homogenized in bovine manure amended or nonamended sandy-loam or clay-loam soil. E. coli was enumerated in 25 g samples on 0, 0.167 (4 h), 1, 2, 4, 7, 10, 14, 21, 28, 56, 84, 112, 168, 210, 252, and 336 days postinoculation (dpi). Regression analyses were developed to understand the impact of strain, soil type, irrigation regimen, and soil amendment on inactivation rates. E. coli survived for 112 to 336 dpi depending on the treatment combination. Pathogenic and generic E. coli survived 46 days [95% Confidence interval (CI) = 20.85, 64.72; p = 0.001] longer in soils irrigated weekly compared to daily and 146 days (CI = 114.50, 184.50; p < 0.001) longer in amended soils compared to unamended soils. Pathogenic E. coli strains were nondetectable 69 days (CI = 39.58, 98.66, p = 0.015) earlier than generic E. coli strains. E. coli inactivation rates demonstrated a tri-phasic pattern, with breakpoints at 26 dpi (CI = 22.3, 29.2) and 130 dpi (CI = 121.0, 138.1). The study findings demonstrate that using bovine manure as BSAAO in soil enhances E. coli survival, regardless of strain, and adequate food safety practices are needed to reduce the risk of crop contamination. The findings of this study contribute data on E. coli concentrations in amended soils to assist stakeholders and regulators in making risk-based decisions on time intervals between the application of BSAAO and the production and harvest of fruits and vegetables.

Multi-Feature Fusion Recognition and Localization Method for Unmanned Harvesting of Aquatic Vegetables

The vision-based recognition and localization system plays a crucial role in the unmanned harvesting of aquatic vegetables. After field investigation, factors such as illumination, shading, and computational cost have become the main difficulties restricting the identification and positioning of Brasenia schreberi. Therefore, this paper proposes a new lightweight detection method, YOLO-GS, which integrates feature information from both RGB and depth images for recognition and localization tasks. YOLO-GS employs the Ghost convolution module as a replacement for traditional convolution and innovatively introduces the C3-GS, a cross-stage module, to effectively reduce parameters and computational costs. With the redesigned detection head structure, its feature extraction capability in complex environments has been significantly enhanced. Moreover, the model utilizes Focal EIoU as the regression loss function to mitigate the adverse effects of low-quality samples on gradients. We have developed a data set of Brasenia schreberi that covers various complex scenarios, comprising a total of 1500 images. The YOLO-GS model, trained on this dataset, achieves an average accuracy of 95.7%. The model size is 7.95 MB, with 3.75 M parameters and a 9.5 GFLOPS computational cost. Compared to the original YOLOv5s model, YOLO-GS improves recognition accuracy by 2.8%, reduces the model size and parameter number by 43.6% and 46.5%, and offers a 39.9% reduction in computational requirements. Furthermore, the positioning errors of picking points are less than 5.01 mm in the X direction, 3.65 mm in the Y direction, and 1.79 mm in the Z direction. As a result, YOLO-GS not only excels with high recognition accuracy but also exhibits low computational demands, enabling precise target identification and localization in complex environments so as to meet the requirements of real-time harvesting tasks.

Effects of the Mixed Seeding Rate of Milkvetch on Fertilizer Substrate, Growth, and Carotenoid Levels of Baby Leaf Vegetables in Vertical Indoor Farming

During this study, an indoor experiment was conducted to determine the effect of mixed seeding rates of legumes used as green manure on the substrate fertilizer, growth characteristics, and bioactive compounds of baby leaf vegetables. The mixed seeding treatment was designed for milkvetch (Astragalus sinicus L.), tatsoi (Barassica rapa L.), kale (Brassica oleracea var. sabellica L.), and spinach (Spinacia oleracea L.) using five rates for each. Accordingly, a total of 15 treatments (3 baby leaf species × 5 mixed seeding rates) were constructed using a randomized complete block design with three replications for each treatment. During the baby leaf vegetable harvest, we evaluated the macronutrient levels (nitrogen, phosphorus, and potassium) in the substrate as well as the growth parameters and carotenoid contents. The substrate in the treatment mixed with milkvetch showed significantly higher levels of nitrogen, phosphorus, and potassium compared with those of tatsoi and kale sown alone (P ≤ 0.05). However, there were no significant differences in macronutrients observed in substrate-sown spinach with or without the milkvetch mixture. The growth and carotenoid levels of each baby leafy vegetable sown alone were significantly higher than those of each baby leafy vegetable sown with the mixed seeding treatment (P ≤ 0.05). Sowing the milkvetch–vegetable mixtures did not result in a significant increase in the growth and carotenoid levels of the three baby vegetables. The results showed that planting milkvetch with tatsoi and kale had a significant impact on substrate fertilization. However, regarding short-term vertical indoor farming, the growth and carotenoid responses of the three greens may be different. Nonetheless, we still believe that the combined interactions of legumes can provide long-term benefits by enhancing the biological functionality of the growing medium for balanced indoor agriculture production.

Traceability of Fruits and Vegetables Agricultural Products using Blockchain

This study explores the implementation of traceability systems utilizing blockchain technology in the agricultural sector, specifically focusing on fruits and vegetable products. Traceability is crucial for ensuring food safety, quality control, and supply chain transparency. Blockchain offers a decentralized and immutable ledger that enables the recording of every transaction or movement of agricultural products from farm to fork. By leveraging blockchain, stakeholders along the supply chain can access real-time, transparent, and tamper-proof information regarding the origin, cultivation, harvesting, processing, and distribution of fruits and vegetables. This enhances trust between consumers and producers, facilitates regulatory compliance, improves efficiency in recall management, and enables swift identification and resolution of food safety issues. Through case studies and analysis, this report evaluates the benefits, challenges, and future prospects of integrating blockchain-based traceability solutions into the agricultural industry, ultimately aiming to enhance food safety and consumer confidence.

ПРОИЗВОДСТВО ОВОЩЕЙ: СОСТОЯНИЕ ОТРАСЛИ И ПЕРСПЕКТИВЫ РАЗВИТИЯ

The scientific article conducted a theoretical study on the issues of ensuring the efficiency of vegetable production, the development of the vegetable growing industry, and providing the population with vegetable growing products. The dynamics of sown areas over the past 25 years is presented. It was revealed that the largest share in the structure of sown areas is occupied by potato plantings, while in dynamics over 25 years, plantings have decreased by almost half. The smallest share in the structure of sown areas is occupied by the cultivation of industrial crops. Their share in the structure of sown areas has almost doubled over the past 25 years. From 10 to 20 percent of the sown area is used for planting vegetables; in the period under study, their area almost doubled. An analysis of the dynamics of the yield of open-ground vegetables showed that over the past 25 years it has almost doubled (from 143 c/ha to 242 c/ha). It is shown that the gross harvest of open-ground vegetables has been trending upward over the past 25 years, with an increase of about 15 percent. It was revealed that in the period from 2007 to 2019, there was an increase in vegetable production per capita by 21.5 percent. Consumption of vegetables exceeds its production within the country; during the study period it increased by 17.4 percent. It was noted that vegetable import indicators are ahead of export indicators. In 2019, vegetable exports were almost 15 times higher than in 1996.

Smart Agri Bot for Multi-Crop Harvesting

The integration of automation and robots into modern agriculture is crucial for improving output and efficiency. A recent development in this field is the creation of a color-sensing robot specifically designed for agricultural purposes. This robot utilizes advanced color detection technology to accurately identify objects and exert control over them based on their color characteristics. The primary goal of this research is to find solutions for labor-intensive agricultural tasks, particularly fruit and vegetable harvesting. By combining robotics and computer vision technology, this robot aims to streamline these processes. Automation plays a significant role in reducing human effort in routine and frequently performed tasks. In the case of agriculture, color-sensing robots have been employed to pluck fruits and vegetables. The robot's mobile application is controlled through a Bluetooth Module, sending instructions to the microcontroller, and then to the motor. The robot's architecture is also programmed with Arduino, an open-source programming language that is a simplified version of other languages. These robots have the potential to reduce damage, increase productivity, and prove cost- effective, while also freeing up labor resources. In addition to object recognition, the robot's navigational capabilities have been enhanced with the integration of an ultrasonic sensor. This sensor emits high-frequency sound waves and analyzes the echoes to accurately estimate distances to nearby objects. This information is crucial for the robot to identify obstacles in its path, ensuring safe and effective navigation

МАТЕМАТИЧНЕ МОДЕЛЮВАННЯ ВПЛИВУ ОПРОМІНЕННЯ НА ПРОДУКТИВНІСТЬ ПРОЦЕСУ ВИРОЩУВАННЯ РОСЛИННОЇ ПРОДУКЦІЇ В ГІДРОПОННІЙ УСТАНОВЦІ

The actual problems of agriculture not only in Ukraine but also in other countries are to increase crop yields, increase nutritional value, quality and reduce production costs, reduce the required production area, the cost of agricultural machinery, etc. In this regard, hydroponic plants look quite promising, as they provide round-the-clock cultivation of a wide range of crops with minimal space, capital and operating costs, and a halved harvest period. The widespread introduction of hydroponic equipment and technology in Ukraine will allow for the fullest realization of the concept of small enterprises, which in this case can be created with little or no start-up capital. In addition, the use of hydroponics will allow existing enterprises of various profiles and small farms to receive additional income or valuable plant material. Hydroponics is a promising area of modern agriculture that ensures long-term cultivation of the main types of vegetables and greenery in small areas with minimal water and fertilizer consumption. This technology allows you to get a fairly large harvest of fresh vegetables within large cities, including office and residential premises. The main trends in improving this technology are expanding the range of crops grown, their yields, more efficient use of land, soil, nutrients, minerals, water, energy, and reducing the cost of finished products.. The article is devoted to the development and study of a mathematical model of the influence of irradiation on the productivity and quality of hydroponically grown products. In particular, dependencies were obtained to determine the influence of irradiation parameters on the development of lettuce, the use of which allows predicting the size and weight of the plant, forming the basis for optimizing the operating parameters of the hydroponic plant.

Designing and development of agricultural rovers for vegetable harvesting and soil analysis.

To address the growing demand for sustainable agriculture practices, new technologies to boost crop productivity and soil health must be developed. In this research, we propose designing and building an agricultural rover capable of autonomous vegetable harvesting and soil analysis utilizing cutting-edge deep learning algorithms (YOLOv5). The precision and recall score of the model was 0.8518% and 0.7624% respectively. The rover uses robotics, computer vision, and soil sensing technology to perform accurate and efficient agricultural tasks. We go over the rover's hardware and software, as well as the soil analysis system and the tomato ripeness detection system using deep learning models. Field experiments indicate that this agricultural rover is effective and promising for improving crop management and soil monitoring in modern agriculture, hence achieving the UN's SDG 2 Zero Hunger goals.

Impact of Environmental Factors on Indoor Air Temperature in Gas-Fired Radiant Heated Cultivated Structures

Ensuring the required microclimate parameters of a cultivation structure, including indoor air temperature, relative humidity, and soil surface temperature, plays a crucial role in obtaining a rich harvest of vegetables and fruits in the winter season. Creating favourable conditions for growing plants in the protected ground is possible only by using modern, high-tech heating systems that will compensate for heat energy losses and maintain a set temperature regime in the room. Gas-fired radiant heating is one such system. This heating method, using ceiling gas infrared emitters as heat sources, directs the required heat flux directly to the soil surface. At the same time, direct or indirect emissions of harmful substances into the environment are minimal, and due to the absence of heat losses during heat production and heat carrier transportation, this type of heating is effective from both energy-saving and economic points of view. The article investigates the influence of meteorological, aerodynamic, heat engineering, and other factors on the air temperature in a cultivation structure under gas-fired radiant heating conditions using a developed software calculation method. An analysis is performed, and explanations are given for the nature of the change in indoor air temperature depending on the changing environmental conditions—the example of the industrial greenhouse “Farmer 7.5” (Russian Federation).

Farmer’s Assistant App-Agrozenith

Abstract: Agriculture is the primary livelihood for nearly two-thirds of India's workforce, making it the country's most vital economic sector. This integrated system involves techniques to control the growth and harvesting of animals and vegetables, serving as a straightforward endeavor with technical and practical processes. Its significance lies not only in maintaining ecological balance and protecting human resources but also as a crucial food production system. Contributing significantly to India's GDP, agriculture accounts for nearly 20% and directly influences the livelihoods of 60% of the population, ranging from small-scale producers to larger agricultural enterprises. Despite its importance, small-scale farmers, constituting the majority, often encounter obstacles in accessing essential information that could improve their yields and market outcomes. Market dynamics, alongside labor, play a pivotal role in determining a farmer's success. Traditionally, obtaining reliable and up-to-date market information has proven challenging, particularly for small-scale producers lacking direct access to trading hubs.

Identification of Microbial Populations Present in Agricultural and Nonarable Soils in the Talas Valley, Northern Kyrgyzstan, in Autumn

Soil bacterial and fungal communities were investigated in relation to soil type and farm management practices after vegetation harvesting in autumn. Soils from fields cultivated with Phaseolus vulgaris (bean) and Pyrus comminus (pear) and nonarable, natural areas were studied. Microbial diversity was analysed using cultivation-dependent methods (isolation of pure cultures) and cultivation-independent methods (direct extraction of DNA from soil, followed by PCR amplification of the 16S rRNA and 18S rRNA genes). The use of cultivation-dependent methods revealed that there were no differences in the biodiversity of the soil bacterial and fungal communities between fields cultivated with bean plants and pear trees. However, the use of cultivation-independent methods showed that there were clear soil and crop type-specific effects on the composition of the soil bacterial and fungal communities. The density of the bacterial population was two times higher in northern mountain-valley serozem (NMVS) soil samples than in light chestnut (LC) soil samples. In contrast, the densities of the fungal communities were almost equal in the studied soil types. The density of the actinomycetes community was almost two times higher in LC soil than in NMVS soil under bean plants. The Shannon index values showed that the bacterial biodiversity in the NMVS soil samples was greater than that in the LC soil samples. Soils under fallow appeared to have diverse bacterial communities that mainly consisted of local autochthonous microflora and a small amount of zymogenic microflora (since fresh plant residue does not enter the soil). The Shannon index results revealed two interesting facts: (1) the soil bacterial community was highly diverse in soils that supported bean plants and (2) the soil fungal biodiversity was high under pear trees in both soil types.

Development Challenges of Fruit-Harvesting Robotic Arms: A Critical Review

Promotion of research and development in advanced technology must be implemented in agriculture to increase production in the current challenging environment where the demand for manual farming is decreasing due to the unavailability of skilled labor, high cost, and shortage of labor. In the last two decades, the demand for fruit harvester technologies, i.e., mechanized harvesting, manned and unmanned aerial systems, and robotics, has increased. However, several industries are working on the development of industrial-scale production of advanced harvesting technologies at low cost, but to date, no commercial robotic arm has been developed for selective harvesting of valuable fruits and vegetables, especially within controlled strictures, i.e., greenhouse and hydroponic contexts. This research article focused on all the parameters that are responsible for the development of automated robotic arms. A broad review of the related research works from the past two decades (2000 to 2022) is discussed, including their limitations and performance. In this study, data are obtained from various sources depending on the topic and scope of the review. Some common sources of data for writing this review paper are peer-reviewed journals, book chapters, and conference proceedings from Google Scholar. The entire requirement for a fruit harvester contains a manipulator for mechanical movement, a vision system for localizing and recognizing fruit, and an end-effector for detachment purposes. Performance, in terms of harvesting time, harvesting accuracy, and detection efficiency of several developments, has been summarized in this work. It is observed that improvement in harvesting efficiency and custom design of end-effectors is the main area of interest for researchers. The harvesting efficiency of the system is increased by the implementation of optimal techniques in its vision system that can acquire low recognition error rates.

Review of current mechanical design in agricultural end effector

Agricultural robots have become increasingly crucial in precise agriculture. This paper reviews recent developments in end-effectors applied in the robotic harvesting of fruits and vegetables. The control and harvest function of the end-effector is the focus. Different structures are categorized based on their properties. Advantages and limitations in each category are introduced. For the collection method, the hard-grab method is very popularly for low difficulties and the soft one can protect the fruits adequately and can harvest different fruits of similar size; double-finger type is used widely with the advantages of low cost, while the multi-finger type has the much higher stability. The collection method is divided into the cutting and twisting methods. The former is applied for low difficulties but may spread diseases among plants; therefore, thermal cutting, namely using high temperature to separate stem and crop, and the twisting method is used. Overall, different types of end-effectors show various advantages. With consideration of the target crop physical properties, a proper decision for the harvester can be made.

Accurate detection and depth estimation of table grapes and peduncles for robot harvesting, combining monocular depth estimation and CNN methods

Precision agriculture is a growing field in the agricultural industry and it holds great potential in fruit and vegetable harvesting. In this work, we present a robust accurate method for the detection and localization of the peduncle of table grapes, with direct implementation in automatic grape harvesting with robots. The bunch and peduncle detection methods presented in this work rely on a combination of instance segmentation and monocular depth estimation using Convolutional Neural Networks (CNN). Regarding depth estimation, we propose a combination of different depth techniques that allow precise localization of the peduncle using traditional stereo cameras, even with the particular complexity of grape peduncles. The methods proposed in this work have been tested on the WGISD (Embrapa Wine Grape Instance Segmentation) dataset, improving the results of state-of-the-art techniques. Furthermore, within the context of the EU project CANOPIES, the methods have also been tested on a dataset of 1,326 RGB-D images of table grapes, recorded at the Corsira Agricultural Cooperative Society (Aprilia, Italy), using a Realsense D435i camera located at the arm of a CANOPIES two-manipulator robot developed in the project. The detection results on the WGISD dataset show that the use of RGB-D information (mAP=0.949) leads to superior performance compared to the use of RGB data alone (mAP=0.891). This trend is also evident in the CANOPIES Grape Bunch and Peduncle dataset, where the mAP for RGB-D images (mAP=0.767) outperforms that of RGB data (mAP=0.725). Regarding depth estimation, our method achieves a mean squared error of 2.66 cm within a distance of 1 m in the CANOPIES dataset.

Optimization and validation of root-cutting device for Chinese cabbage harvester based on discrete element method

Optimization of the structural and working parameters for a cutting system are essential to improve the working performance and reduce the cutting energy consumption and mechanical damage of the vegetable harvester machinery. An asymmetric root-cutting system was developed to efficiently solve the problems of high cutting loss and energy consumption of mechanized harvesting of Chinese cabbage. Discrete element method (DEM) and microscopic observation were utilized to establish the simulation model of Chinese cabbage, and a delamination particle model was proposed in terms of contact interactions. The experimental methods of Plackett-Burman design (PBD), steepest ascent (SA), and Box Behnken design (BBD) were applied to determine significant factors from eight major influential factors and to optimize the structural and working parameters of the cutting system, and further validated experiments were performed according to the practical feasibility of the optimal combinations of parameters. Finally, the cutting mechanisms were revealed both at a macro and micro level by comparative analyses of the cutting process using EDEM simulation. On this basis, the designed root-cutting device was configured on the self-propelled Chinese cabbage harvester for experimental verification. Results showed that the four most impactful factors on maximum reaction force (MRF) of root-cutting were determined in the order of cutting pitch angle (D), cutter speed (A), walking speed (E), and cutter overlap amount (B), and most interactions between interactive factors had consequences for the cutting force change, except A and cutting position (C). The minimum value of MRF was 88.62 N when the cutter speed, cutter overlap amount, cutting pitch angle, and walking speed were 186.91 r min−1, 17.87 mm, 12.26°, and 0.30 m s−1, respectively. Results of the validated experiments indicated that the measured MRF was 93.41 N under the specified parameters of 187 r min−1, 18 mm, 12°, and 0.3 m s−1. This value was found to be comparable to the original value, within an acceptable percentage change of 5.41 %. These findings demonstrate the feasibility of optimizing the cutting system based on DEM. In comparison to the three most prevalent combinations of cutting disks, the cutting system utilizing one concave disk and one sawtooth disk could efficiently sever roots over shorter periods during the cutting process. The combined disks were arranged at an angle of 12° in the longitudinal direction. The forces of each layer of the particle structure inside the root were inconsistent during the root-cutting process. The internal bonding forces of the two-layer particle model played a role as a structural support of the parenchyma in the root epidermis, and the connecting epidermis between the inner and outer epidermis stand greater forces. The result of the field experiment showed that the average productivity of the Chinese cabbage harvester was 0.11 hm2 h−1, and the average qualified rate of root-cutting was 93.40 %, which demonstrated that the optimized root-cutting device efficiently fulfills the harvest requirements of high efficiency, and low cutting energy consumption and damage.