Harvesting Machines Research Articles

Performance Analysis and Operation Parameter Optimization of Shaker-Type Harvesting for Camellia Fruits

This study aims to address the challenges of achieving a high harvesting rate and low flower bud damage rate during the harvesting of camellia fruits. To this end, a dynamic model of the camellia osmantha tree and a self-developed shaker-type harvesting machine were used as research subjects. The first 24 natural frequencies and mode shapes of the camellia tree were solved using the finite element method, and the effects of vibration frequency, excitation position, and vibration duration on the harvesting rate and flower bud damage rate were quantitatively analyzed through an orthogonal experiment. The numerical analysis results indicate that the camellia tree exhibits good response characteristics at vibration frequencies of 10–15.5 Hz and 38.5 Hz. The three-factors orthogonal experiment figured out that the optimal operational parameters for shaker-type harvesting were determined to be a vibration duration of 30 s, a motor output frequency of 12.5 Hz, and a gripping position height of 50 to 60 cm above the ground. Meanwhile, under these operational parameters, the harvesting efficiency reached 96.97%, while the flower bud damage rate was only 6%.

Application of Computational Intelligence to Determine the Effect of Different Shear Bar Positions on Chopping Length and Specific Cutting Energy Consumption in the Chopping of Silage Sorghum

ABSTRACTFlail forage harvesting machines are disadvantageous compared to other silage machines because of the uneven length of the chop obtained, the low distribution of the appropriate chopping length in the entire chopped material, and the high energy consumption. This issue is tackled by employing artificial neural networks (ANN), which serve as versatile mathematical instruments capable of capturing data. In this article, six different knife peripheral speeds (KPS) and three different shear bar (SB) positions were tested to determine the combination of KPS and SB positions that increased the proportion of suitable chopping length (CL) for silage and decreased the specific cutting energy consumption (SCEC) among all chopped material. The results have shown that, depending on the increase in KPS, the average CL varied between 165.28, 127.30, 100.24, 83.55, 77.06, and 65.09 mm. Depending on no shear bar (), shear bar positioned parallel to the feed unit (), and shear bar positioned at an angle of 45° to the feed unit () the average CL was determined as 114.73, 99.65, and 94.88 mm, respectively. Depending on the increase in KPS, the SCEC values vary between 0.97, 1.49, 1.89, 2.20, 3.05, and 4.12 kWh t−1. Depending on , , and the SCEC values were determined as 1.64, 3.05, and 2.17 kWh t−1, respectively. The effects of KPS and SB positions on CL and SCEC were statistically very significant (p < 0.001). An ANN model with a 3‐(5‐5)‐1 architecture, utilizing a backpropagation learning algorithm, was developed to forecast SCEC. This model outperformed traditional statistical models and was constructed using data on KPS, SB, and CL. The ANN model exhibited the highest efficiency, outperforming the polynomial models. In this particular ANN model, key metrics such as coefficient of determination (R2), root mean square error (RMSE), and mean error (ε) (0.9970%, 0.0159%, and 3.86% respectively) are significantly positive. This research convincingly demonstrated the efficacy of ANN in accurately predicting SCEC, leveraging data on KPS, SB, and average CL.

Research Progress on Mechanization Technology and Equipment for Cassava Field Production

Cassava is one of the world’s staple crops and an important economic and food crop for one billion people. It holds significant value in food, feed, and industrial raw materials. However, cassava production is faced with the following problems: agricultural machinery and agronomy are not integrated, work efficiency is not high, it is not suitable for large-scale planting plots, and there is a lack of large-scale equipment, automation, and intelligent cassava production equipment. This study reviews research on cassava planting agronomy and mechanized production technologies and equipment, including planting, stem–tuber separation, and harvesting, both globally and in China. The shortcomings are analyzed, and recommendations are provided for current mechanized cassava production to accelerate the development of intelligent equipment and strengthen the shortcomings. Meanwhile, we used typical and extensive Chinese planting and harvesting machines as case studies. The future trends in cassava production are forecasted, underscoring the importance of advancing global mechanized technology and equipment for cassava production to enhance its efficiency, competitiveness, and modernization.

Improving Dynamic Performance of a Small Rhizome Chinese Herbs Harvesting Machine via Analysis, Testing, and Experimentation

The small rhizome Chinese herbal medicine harvesting machine is used for excavating the underground rhizomes of Chinese medicinal plants. The reliability of an existing machine of this type was found to be suboptimal due to high vibration levels, as confirmed by direct measurements. To remedy this issue, the differential equation of motion was derived, solved, and visualized using MATLAB software (R2017a). The impact of various parameters on the equation of motion was analyzed through both time and frequency domain plots, as well as experimental analysis. The parameters studied included the rotational speed of the tractor’s power take-off (PTO) shaft, the machine’s overall mass and stiffness, the transmission ratio, and the excitation force generated by the machine’s reciprocating parts. To reduce vibration in the non-resonant state and avoid resonance as the natural frequency changes, several modifications were necessary: the PTO speed needed to be controlled, the stiffness of the machine had to be increased, and the mass of the reciprocating parts had to be decreased. Additionally, expanding the transmission-ratio range of the operational machinery was essential. Sandbags were added to the machine’s frame to increase its overall mass. The above measures have reduced the vibration speed of the harvester during operation. The vibration speeds Vmax and VRMS of the harvester under both working and non-working conditions have decreased by half compared to their original values, reducing the occurrence of resonance in the harvester and effectively mitigating vibration damage, thereby enhancing operational reliability.

Feasibility studies on plant architecture traits influence on seed cotton yield of Gossypium hirsutum zeromonopodial genotypes and assessment of ecological suitability to machine harvesting

Feasibility studies on plant architecture traits influence on seed cotton yield of Gossypium hirsutum zeromonopodial genotypes and assessment of ecological suitability to machine harvesting

To determine the cut height of fallen stems

Abstract A significant part of the harvested crops lies down by the beginning of harvesting, and the harvesting of fallen bread cereals is accompanied by an increase in grain losses. The productivity and quality of the cutting devices of harvesting machines, which are used for harvesting crops, largely depend on the cut height of the fallen stems. It was found out that with an increase in the cut height, grain losses increase, and with its decrease, the efficiency of agricultural machinery decreases due to clogging of mechanisms with plants and soil. The article considers the analytical dependencies between the characteristics of the height of the cut stems, the soil surface and the cutting machine in its general movement across the field. The basic formulas of such dependencies are given.

FBSM: Foveabox-based boundary-aware segmentation method for green apples in natural orchards

Image segmentation of target fruits is an essential part of machine vision systems, aiming to facilitate more accurate early fruit measurement and machine harvesting in natural orchard environments. Given that instance-level pooling and down-sampling operations in conventional segmentation models often lead to the loss of detailed information, resulting in coarse partitioning masks, we endeavor to restore the boundary information of the masks. To achieve high-quality fruit segmentation with clear boundaries and refined masks, a Foveabox-based boundary-aware segmentation model (FBSM) is constructed by adding a multi-stage mask prediction head incorporating fine-grained features to the anchor-free detection model FoveaBox to realize accurate segmentation of green apple fruits. At each stage, a bi-layer fusion structure (BFS) is employed to fully fuse the fine-grained features in a double-fusion manner to guide the subsequent instance mask prediction. Finally, the boundary recovery module (BRM) is leveraged to recover the lost boundary detail information and obtain more accurate boundaries for the continuous optimization process of the fruit instance mask. Experimental results demonstrate that the proposed FBSM model achieves a mean average precision (mAP) of 60.7 % for green apple instance segmentation in unstructured natural orchard environments, surpassing traditional instance segmentation models. Moreover, it enhances base model detection accuracy by 1.3 % while striking a better balance between detection and segmentation performance.

Ergonomic optimization in forestry harvesting: analysis of indicators and workstation layout for machine operators

The planted tree sector in Brazil plays a significant role in the national economy, driven by technological advancements in forestry machines, especially in harvesting. The interaction between operators and machines has become essential for improving performance and ensuring occupational health. This study analyzed forest harvesting machine operators, focusing on anthropometric surveys and proposing indicators for ergonomic satisfaction and cabin layout. Conducted between May 2022 and July 2023 in a northeastern Brazilian forestry company, data were collected during mechanized harvesting of a six-year-old Eucalyptus spp. stand using the cut-to-length system. A questionnaire was applied to 111 employees to assess their ergonomic satisfaction and health. Two indices were created: the Ergonomic Satisfaction Coefficient and the Occupational Health Coefficient. The anthropometric analysis evaluated operators’ percentiles and BMI, revealing that 77.77% were overweight, highlighting the need for health protection measures. The ergonomic indices proved effective in assessing operator satisfaction. The study also determined an adequate workstation layout for the sample group. From the Ergonomic Work Analysis perspective, FW2 and HV3 machines received a good rating based on the adopted parameters.

Assessment of the utilization of modern agricultural machinery in rice production and its problems: A case from San Leonardo, Philippines

This study examines the relationship between the utilization of modern agricultural machinery and the business technical aspect of rice production of paddy field farmers in San Leonardo, Nueva Ecija, Philippines. The objective of this study is to assess the effectiveness of modern machinery on the business technical aspect of the farmers and the paddy field owners. The study utilized descriptive analysis to assess the use of modern agricultural machinery in rice production and its problems. The design was quantitative, using percentage, frequencies, and weighted mean. The results revealed that the majority of respondents have been operating for 11 to 20 years, owned 1 hectare of land that cultivate 2 times a year and harvest 86 rice sacks per hectare. Rice cultivation is happened during wet and dry season. To maximizing the land, farmers can try other crops while waiting for the season of rice cultivation. In agricultural mechanization, it concluded that plowing machines helps remove clods from the soil, transplanting rice seedlings and applying chemicals using machine can saves time. Harvester machine is suitable in small and large farmland for harvesting and may help farmers to save time, effort and total cost requirements. Local Government Unit to support through grants and funding the local rice farmers in maximizing utilization of machines that shows positive effect.

Energy-saving technology for harvesting root crops and potatoes

The purpose of the work is to empirically determine the temperature of the exhaust gases when changing the load on the power plant of a beet harvester with a separating system that provides better harvesting quality in conditions of high soil moisture. Have been developed: classification of methods for increasing the separating ability of slot-hole devices for cleaning root crops; laboratory installation for determining the quality indicators of a separation system with thermal energy for exhaust gas purification with a star-shaped cleaning device; a method for assessing the process of heat transfer from the exhaust gases of the power plant of the self-propelled harvester for harvesting sugar beets Holmer Terra Dos T3 to the separating system, which involves determining the temperature using thermocouples installed at various points in the gas exhaust system of the internal combustion engine. An experimental study of the temperature of the exhaust gases of the power plant of a harvesting machine was carried out using thermocouples at maximum rotation speed with a change in the thermal load index of the external environment from 5 to 30°C. The heat of the exhaust gases from the Mercedes-Benz power plant of the Holmer Terra Dos T3 self-propelled harvester, aimed at blowing the working surface of the separating device at the outlet of the first and fourth, as well as the second and third cylinders, has minor discrepancies exceeding the error limits (65 ± 5.8; 63.2 ± 1.5 and 74.9 ± 2.4; 75.2 ± 2.0°C, respectively), which leads to a uniform distribution of heat flow on the device for cleaning sugar beet roots.

A critical study on paddy and wheat harvesting machine used in Chhattisgarh

A critical study on paddy and wheat harvesting machine used in Chhattisgarh

PERFORMANCE EVALUATION OF PRECISION GROUNDNUT DIGGER-INVERTER

Conventional groundnut digger blades are labor-intensive, time-consuming, and have high yield losses (up to 30%). The objective of this study was to adopt a precision groundnut harvesting technology in the country to reduce groundnut harvesting losses and to compare its performance with the conventional digger blade. Agricultural Engineering Institute, NARC, PARC imported a precision groundnut digger-inverter from the USA. This machine was tested at farmer’s fields and its performance was compared with the conventional digger. Data from both machines were collected for total digging losses, viz exposed pod losses, unexposed pod losses, damaged pod losses, and undug pod losses from different locations in the Pothohar region of Punjab, Pakistan. Results revealed that total pod losses using an imported precision digger shaker harvesting machine were 4.76%, whereas the total losses of a conventional digger were 28.26%. The operational cost using a conventional digger was Rs. 4,400/a, which was reduced to Rs. 2,350/a using a precision digger-inverter. The net benefit in terms of recoverable losses was Rs. 14,050 per acre. It is concluded that a significant amount of losses is reduced and farmer profit increases by adopting precision groundnut digger-inverter. There is a need to upscale this technology in the country's groundnut growing areas to reduce harvesting losses.

Optimizing radiation capture in machine-harvested cotton: A functional-structural plant modelling approach to chemical vs. manual topping strategies

Topping and planting density are key agronomic management practices to optimize cotton plant structure for machine harvesting and light capture. However, modelling the effects of these practices on canopy light utilization in the field, in order to improve cotton management, remains challenging. Functional-structural plant modelling is a computational approach to explore the effects of agronomic practices on shaping plant architecture and thus light interception. This study, for the first time, utilizes the CottonXL model to quantify the significant impact of chemical topping compared to manual topping on radiation interception on machine-harvested cotton in China at different planting densities, providing new management strategies for cotton production. A more compact plant structure is shaped by chemical topping through inhibiting leaf expansion and shortening internodes of both the main stem and fruiting branches, thereby allowing more PAR interception by middle and lower leaves. Simulation results showed that the total PAR intercepted by the canopy over entire growth season was increased by 11.3 % under chemical topping compared to manual topping. This positive effect became even more pronounced with increasing plant density. These results indicate that chemical topping could be beneficial for optimizing canopy structure, enhancing light interception and lint yield, especially at higher plant densities. The results illustrate the importance of shaping plant structure for improving radiation resource capture and exemplify the potential of optimizing topping strategy and plant density to enhance crop performance. They also demonstrate the utility of a functional-structural plant model for guiding crop management.

Prediction of blueberry sensory texture attributes by integrating multiple instrumental measurements

Blueberry (Vaccinium spp.) texture contributes to consumer satisfaction, shelf-life, and machine harvestability and is now a critical goal in breeding for new fresh market cultivars. The industry commonly uses instrumental methods to phenotype texture, assuming that instrumental measurements correlate with sensory perceptions. However, the relationship between perceived sensory textures and mechanical parameters is not well established. In this study, we characterized the fruit texture profile of 43 blueberry cultivars using nine sensory descriptors and determined the predictability of the sensory attributes using mechanical parameters. The sensory study was done by a trained descriptive sensory analysis panel, and instrumental analysis was performed using flat probe penetration and texture profile analysis (TPA) methods. Differences in the perceived firmness of blueberries were mainly due to springiness, hardness, and snap/crisp attributes. Among the mechanical parameters, maximum force (FM; a flat probe penetration parameter) and gumminess (a TPA parameter) had the strongest correlations with these three sensory attributes. To develop predictive models for the nine sensory attributes, multivariate statistical methods were used. The highest level of prediction accuracy was achieved when all the penetration or TPA parameters were used for model development. The R2 values increased by up to 0.66 compared to using a single mechanical parameter. Springiness, hardness, and snap/crisp were predictable with R2 > 0.5, regardless of the instrumental method used. TPA parameters were more suitable for predicting juiciness while residual skin was only predictable using penetration parameters. Mealiness was not predictable with any instrumental measure (R2 < 0.05). For most of the sensory attributes, the models were able to effectively discern the cultivars with the highest or lowest intensity scores. This study provides a basis for breeding programs to utilize improved estimations of the sensorial texture using diverse mechanical parameters to enhance selection for desired blueberry textures.

Comparison of Performance Characteristics of Different Types of Hazelnut Harvesting Machines

In this study, the possibilities of using the hazelnut harvesting machine with pneumatic effective harvesting unit produced by local manufacturers and the prototype manufactured hazelnut harvesting machine with mechanical effective harvesting unit in the mechanical harvesting of hazelnuts grown in flat and near flat land conditions were examined, some performance values (harvesting efficiency, labor requirement, machine+human labor requirement, field productivity, machine+human field productivity and kernel productivity) of hazelnut harvesting machines with mechanically operated harvesting units were determined under different orchard yield conditions. According to the results of the experiment, the hazelnut harvesting machine with a mechanical effective harvesting unit had a harvesting efficiency of 92.54-96.03% and; a labor requirement of 6.349-9.839 MİGh ha-1; machine+human labor requirement of 46.228-62.531 BİİGh ha-1; field productivity 0.158-0.102 ha MİGh-1; machine+human field productivity: 0.022-0.016 ha BİİGh-1; kernel productivity: 124.83-1322.08 kg h-1. The hazelnut harvesting machine with a pneumatic effective harvesting unit has a harvesting efficiency of 97.68-99.36%; labor requirement 70.349-105.647 BİİGh ha-1; machine+human labor requirement 108.509-147.481 BİİGh ha-1; field productivity 0.014-0.009 ha MİGh-1; machine+human field productivity 0.009-0.007 ha BİİGh-1; kernel productivity 18.90-67.18 kg h-1. As a result, when the characteristics of harvesting efficiency, field productivity, and kernel productivity of the machines are compared, the mass production of hazelnut harvesting machines with mechanically effective harvesting units can provide benefits for hazelnut producers in terms of reducing harvesting costs and demand for labor.

Research Patents on Potato Harvesters

Background: Potato is a major food crop that is widely grown in more than 100 countries around the world. The production of this crop is a labor-intensive process, and its field production mainly includes seed arrangement, seedling cutting, ploughing, ridging, transplanting, field management, and harvesting (vine cutting, digging, cleaning up, and collection). With the improved models, the labor can be reduced to nearly half in the production process of potato. Objective: The objective of this study was to provide a comprehensive description of the classification, characteristics, and development of existing potato harvesters. Methods: In this study, a review on the principle of potato harvesting and related patents has been conducted. We have also introduced the structural characteristics and differences in applications of potato harvesters. Results: The existing potato harvesters have been analyzed and compared in this paper, and the typical characteristics have been summarized. The main problems existing in the development of potato harvesters are analyzed, the development trend of potato harvesters is forecasted, and the research status and future development trend of potato harvesters and patents are discussed. Conclusion: Based on our research, we have found that potato harvesters can be classified into three categories based on the harvesting process, clearing the vine, and harvesting machine. The combine harvester is capable of both clearing the vine and harvesting, and it has been reported to significantly improve the efficiency. However, for special terrain or other conditions not suitable for the combine harvester, the cutting and harvesting machine can perform both the steps. Currently, potato harvesters are not popular at home and abroad, and future development of potato harvesters must focus on them being intelligent, lightweight, and unmanned.

Evaluation of readiness of the technical system using the semi-Markov model with selected sojourn time distributions

Mechanization of forestry work is crucial in forest management, and the specific nature of the tasks performed requires reliable machines with a high level of technical readiness. Therefore, models describing the exploitation process of multi-operational machines used to obtain wood raw materials, and especially assessing the level of their technical readiness, are extremely important. For multi-tasking objects performing random activities in given time intervals, calculating readiness measures is a complex issue. Forecasting subsequent operational states and their durations allows to predict the behavior of technical objects and schedule work. In addition to identifying the sequence of states, it is also important to identify the sojourn time in these states. This article presents a method for identifying the semi-Markov process and then assessing the technical readiness of a Harvester machine. This allowed for conclusions regarding the timely completion of assigned tasks, and also made it possible to adjust the activities carried out to the requirements of forest management.

Study on Factors Influencing the Utilization Level of Combine Harvester by Farmers

The aim of this research is to determine the level of use of combine harvesters and the factors that influence it. The research location was chosen deliberately, namely in Negeri Katon District which received assistance from 3 combine harvester units. The respondents in this study were 53 farmers from 3 farmer groups who received combine harvester assistance. The data collected was analyzed using descriptive analysis and the Kendall tau statistical test. The results of the research show that the level of use of combine harvesters on the indicators of plant condition requirements shows a good category, where farmers harvest rice when it reaches the optimum age. The indicator for plant condition requirements shows a fairly good category, where farmers harvest when the land is dry, but farmers never confirm whether the land conditions are dangerous and can damage machinery. The machine application indicator shows the poor category, where the intensity of farmers using combine harvester machines is 3 - 4 times in 5 harvest seasons, and farmers are still less skilled in operating combine harvesters. Factors related to the use of combine harvesters by farmers are land area, relative profit, compatibility, complexity, trialability, observability, and the role of farm extension workers. Keywords: Compatibility, Complexity, extension workers, Observability, Relative profit.

A Framework of Real-time Knowledge Capture and Formalization for Model-based Design with Spoken Annotation and Design Operations

Abstract Knowledge capture without an extra burden on engineers is one of the most challenging issues in the engineering domain. This study proposes a framework of real-time knowledge capture and systematic formalization for the model-based design (MBD), the systems design approach where a simulation is a key component. The framework records the screen of the modeling tool as video data and spoken annotation in which an engineer verbally explains the rationale of the modeling conducted with Modelica-based software commonly used for MBD. The captured modeling tool log and spoken annotation are divided into chunks, each corresponding to a design operation, e.g., defining functions, defining system components, setting model elements, setting model parameters, and running simulations. gIBIS, the argumentation model for representing design rationale, represents the logical structure of the design process. We define design operation templates at the system and subsystem levels, which act as rules to convert the modeling tool log into gIBIS format systematically. We conduct a design case of a spinach harvesting machine with MBD software to verify the proposed framework. The results show that through non-intrusive recording of the design process, the proposed framework is effective in capturing knowledge that can be used to help understand how a model was built.

Method and results of laboratory and field studies of a separating system with thermal energy of cleaning a machine for sugar beet harvesting

Method and results of laboratory and field studies of a separating system with thermal energy of cleaning a machine for sugar beet harvesting