Harvesting Machines Research Articles

Prediction of Tuber Damage from Harvesting and Processing Machine Working Units Based on the Recording of Impact Parameters

The paper presents the development of an empirical mathematical model of the potato tuber damage index that considers the relationship between impact parameters, such as peak acceleration, velocity change, and the experimental coefficient. This coefficient was developed using statistical analysis methods for four isolated surfaces and two potato varieties, Hermes and Saturna. The IRD 400 device was used to measure impacts, which recorded peak accelerations and changes in impact velocity at initial velocities in the 2.43–4.43 m·s−1 range. The study’s results indicate that the variety, type of surface, and initial impact velocity had a statistically significant effect on the tuber damage index; the type of surface and initial impact velocity had a statistically significant effect on the peak acceleration values (p < 0.05). The increase in peak acceleration with increases in the impact velocity confirms the hypothesis that the maximum force and the resulting internal stresses of the tuber are key elements causing damage due to the impact. The highest values of the tuber damage index occurred during impacts with steel surfaces and conveyor bars. The developed model allows for the faster prediction of potential damage in harvesting and post-harvest processing conditions than traditional measurement methods. To fully use the proposed model in Agriculture 4.0, further research should be conducted to improve recording devices for the measurement of impact parameters in real time.

Hygiene Status of Over-the-Row Blueberry Machine Harvesters Cleaned and Sanitized Using Various Approaches

Hygiene Status of Over-the-Row Blueberry Machine Harvesters Cleaned and Sanitized Using Various Approaches

Thidiazuron as a defoliant to facilitate mechanical harvesting in cotton: A comprehensive review

Cotton is primarily cultivated for its commercial fiber, which plays a significant role in India’s agro-industrial sector. It is one of the primary raw materials for producing feed, oil, fiber, and biofuel. Currently, farmers in India widely employ machine harvesters to harvest cotton. However, excessive leaf vegetation poses challenges in boll picking, adversely affecting fiber quality and reducing mechanical harvesting efficiency. Various chemical defoliants are applied to remove leaves before harvesting to address this issue. These defoliants promote leaf shedding, minimize debris in the cotton, and enhance boll opening and picking efficiency. Thidiazuron is a potent hormonal defoliant used in cotton to induce defoliation by increasing ethylene production while inhibiting the synthesis and transport of auxins. Notably, it interferes with the crosstalk between the phytohormones, such as cytokinin and ethylene, which regulates cotton defoliation. The method and timing of defoliant application are crucial for improving cotton harvesting efficiency. This review aims to provide a clear understanding of thidiazuron’s application in synchronizing harvests, ultimately supporting the mechanization of cotton harvesting.

Results of laboratory and field studies of the separating system of a potato harvesting machine

The article describes a method for experimental studies of a separation system with thermal energy for cleaning a machine for harvesting root crops and potatoes under conditions of increased soil moisture up to 27%, represented by a rod elevator and a cleaning star. The self-propelled combine harvester VARITRON 470 is equipped with this system. When equipping it, a developed deflector system for supplying exhaust gases from the power plant to the rod elevator and cleaning star was used. Field research was carried out in the Penza region in the fields of the IP Bodyagin V.I. The research results allow us to assert the possibility of increasing the forward speed of a rod elevator as a result of improving the quality of cleaning of commercial products, eliminating the possibility of mechanical impurities sticking to the grate bars of a rod elevator. The main patterns of changes in the quality indicators of the developed separating system are determined, indicating that the completeness of separation of commercial products is 95.8% when finding the optimal values of the factors under consideration: the distance between the rod elevator and the deflector SД = 160 mm and the forward speed of the rod elevator vД = 1.6 m/s.

ENERGY CONSUMPTION FOR FLUTTERING GRASS WITH A ROLLER FLUTTER

Hay is a common type of roughage, the quality of which largely depends on the duration of natural drying. In order to obtain high-quality fodder, it is necessary to minimize the stay of mown plants in field conditions. Nutrient losses during hay harvesting occur as a result of biochemical processes, leaching of nutrients by precipitation during adverse weather.In addition, harvesting hay is accompanied by mechanical losses due to the crushing of vegetative organs of plants (leaves, inflorescences) by the working bodies of hay harvesting machines. These losses not only reduce the collection of hay, but also significantly reduce the quality of fodder, since leaves and inflorescences contain 2-3 times more nutrients than stems, they are due to the unequal drying rate of vegetative organs of plants. Small vegetative organs of plants, due to the peculiarities of their anatomical structure, give off moisture more intensively than stems, and therefore they become fragile earlier and break off and are lost as a result of the action of working organs of machines. An effective method of accelerating and equalizing the drying rate of vegetative parts of plants is flattening of grass clippings. Thanks to this measure, uniform drying of the entire plant mass is ensured, and therefore, the loss of nutrients during hay harvesting is reduced. For flattening legumes, it is advisable to use mowers equipped with roller flatteners, because they act less harshly on the grass than dynamic flatteners, reducing the crushing of the most valuable parts of plants - leaves and inflorescences. The main operational indicator that allows you to estimate the energy consumption of the process of flattening grass is the power consumption for the drive of the flattening rollers. A method of determining the power required to drive the flattening rollers of mower-folders has been developed, which can be used for calculations in the design of roller flatteners.

高水分玉米低损伤脱粒装置的设计与试验

To address the requirements of direct corn kernel harvesting in the Huang-Huai-Hai region of China, this study aimed to rectify issues related to kernel damage and low threshing rates observed in the operation of existing corn kernel direct harvesting machines. Initially, a novel threshing device was designed, incorporating staggered rotary motions for the threshing cylinder and concave grate. Subsequently, the experimental factors such as the speed of threshing drum, the speed of concave grate and the threshing gap were considered on the test bench, and the indexes such as corn grain crushing rate and threshing rate were evaluated.Moreover, orthogonal testing indicated that optimal threshing performance was achieved with a threshing cylinder speed of 287 r/min, a concave grate speed of 106 r/min, and a threshing clearance of 49 mm, resulting in a kernel crushing rate of 4.12% and a threshing rate of 94.18%. These experimental findings confirm the feasibility of the Rotating Concave Screen Threshing Device, underscoring its ability to significantly enhance corn threshing rates while minimizing kernel fragmentation and damage.

Determining the amount of water and the total acid number index in the hydraulic oil of the Austoft 7000 sugarcane harvester with visible/near-infrared spectroscopy technology

The current era witnesses significant challenges in the realm of sugarcane harvesting, primarily stemming from the assessment and high maintenance costs associated with sugarcane harvesting machines. Consequently, there arises a crucial need for a rapid and non-destructive method of monitoring the hydraulic oil of sugarcane harvesters, facilitating the determination of pollution levels and the total acid number (TAN) index. This study delves into the efficacy of visible/ near-infrared spectroscopy as a non-destructive means of measuring and predicting the water content and TAN index in hydraulic oil samples extracted from Austoft 7000 sugarcane harvesters with varying numbers of operational hours. To achieve this objective, hydraulic oil samples are subjected to analysis within the spectral range of 400-1800 nm. Multivariate regression models are developed based on reference measurements and preprocessed spectral data. Various preprocessing methods including moving average (MA), Savitzky-Golay (SG) smoothing, standard normal distribution and first derivative are employed to enhance the accuracy of water content and TAN index predictions. The findings underscore the viability of visible/near-infrared spectrometry for rapid and non-destructive assessment of the water content and TAN index in the hydraulic oil across different operational phases of Austoft 7000 sugarcane harvesters. Notably, the partial least squares (PLS) model utilising a moving average preprocessing method exhibits the most promising results in predicting the water content in the hydraulic oil (cross-validation correlation coefficient (rcv) = 0.98, root mean square error of cross-validation (RMSECV) = 1.32, predictive correlation coefficient (rp) = 0.93 and root mean square error of prediction (RMSEP) = 2.49), boasting excellent accuracy (standard deviation ratio (SDR) = 3.99). Similarly, the PLS model incorporating a combination of moving average preprocessing and standard normal distribution demonstrates exceptional accuracy in predicting the TAN index (SDR = 3.87) (rcv = 0.98, RMSECV = 0.004, rp = 0.93 and RMSEP = 0.008). Consequently, the utilisation of visible/near-infrared spectroscopy technology is advocated within the agricultural and industrial domains to expedite the monitoring of hydraulic oil quality and pollution control processes.

Blueberry Harvest Mechanisms

Blueberry (Vacciniumcorymbosum L.) is a perennial plant that shed leaves in winter. It is from Ericaceae family and it is in the form of bushes. It grows in temperate climates and its motherland is America. It spread from American culture 110 years ago. It can grow in lightly textured and acidic soil. For this reason, the areas where agriculture can be done are limited. Because the production quantities are not enough, the selling prices are also high (Çelik H., 2008). Grape berries can be harvested by hand and machine. For the Blueberry harvest, harvesters with three different types of harvesting mechanisms were produced. The first blueberry harvester was manufactured in 1956, adapted from a mechanical cranberry picker. This machine had a mechanism consisting of a six-row scraper stick (Farooque A., 2014). These machines with high collection capacity reduce the need for labor and the cost of harvest. Blueberry manual harvesting hectare requires 1300 h of labor, while single row harvesting machine requires 25 h of labor/hectare. Thus, the cost of harvest falls from $ 2.8 per kg to $ 0.26 per kg (Takeda F. et.al., 2017). These machines are not economical for medium and small sized farms. In machine harvest, there are some problems for this plant. These problems are fruit falling on the ground during fruit harvest, the harvest of fruit that has not reached sufficient maturity, fruit and plant damage during picking. A lot of work has been done and continues to be done. However, sufficient harvesting efficiency has not yet been achieved. Blueberry should be harvested with a high quality and long shelf life that will appeal to the consumer.

Sorghum grain yield estimation based on multispectral images and neural network in tropical environments

The application of machine learning and remote sensing techniques to estimate crop yield has garnered significant attention due to their ability to analyze large volumes of data and combine various inputs for enhanced results. This study aimed to optimize sorghum grain yield prediction in tropical conditions over two seasons by integrating vegetation indices and soil elevation data to calibrate Artificial Neural Networks (ANNs). We developed and implemented ten ANNs with a Multilayer Perceptron architecture using the Keras library, incorporating vegetation indices (CIgreen, SR, VARI, WDRVI) from the PlanetScope platform and soil elevation data from a harvester machine. The general M2 model (CIgreen + SR + VARI + WDRVI + soil elevation) achieved the highest performance with an R2 of 0.89 and RMSE of 0.22 t ha⁻¹ at 30 days after sowing (DAS). In 2019, the M9 model (CIgreen + SR + WDRVI + soil elevation) performed best at the same growth stage with an R2 of 0.82 and RMSE of 0.27 t ha⁻¹. Conversely, in 2020, the M3 model exhibited the best performance at 120 DAS, with an R2 of 0.84 and RMSE of 0.15 t ha⁻¹. These results highlight the variability in model performance due to environmental factors, plant growth dynamics, and the suitability of specific indices at different growth stages and years. Despite that the general model performed similarly to the growth stage-specific models, suggesting its potential applicability across different conditions and cultivars for estimating sorghum yield in tropical environments.

Sikap Petani Padi terhadap Penggunaan Combine Harvester di Kecamatan Paron Kabupaten Ngawi

<p><em>The increasingly advanced development of agricultural technology in Indonesia leads to agricultural modernization. The technology currently developing in Indonesia, especially in terms of harvesting rice plants, is the combine harvester. Many farmers already use combines, one of which is the people of Jambangan and Ngale Villages, Paron Sub-district, Ngawi Regency. Using a combine harvester can provide benefits for farmers, especially in terms of efficiency in harvesting rice crops. This research aims to 1) find out the impact of using the combine harvester machine; 2) analyze the attitudes of rice farmers towards using the combine harvester machine; 3) analyze the factors that influence the attitudes of rice farmers towards using the combine harvester in Paron Sub-district, Ngawi Regency. The research method uses a quantitative approach with survey techniques. The sample consisted of 43 people and was determined using proportional random sampling. Data analysis uses multiple linear regression analysis. The results of the research show 1) the positive impact of using combine harvester technology in increasing harvest efficiency while the negative impact is resulting in soil density and a reduction in labor labor for harvesting up to 92%; 2) the attitude of rice farmers towards the use of combine harvesters is in the strongly agree category with a percentage of 80.12%; 3) the results of the analysis of factors that influence the attitudes of rice farmers towards the use of combine harvesters in Paron Sub-district, Ngawi Regency show that non-formal education and experience have a significant influence.</em></p>

Characterising vibration patterns of winter jujube trees to optimise automated fruit harvesting

Understanding jujube tree dynamic characteristics is crucial for the design and invention of a catch-and-shake machine for fruit harvesting. Currently, the study of vibration characteristics based on the finite element method is the mainstream method for different types of fruit trees. However, limited by the lack of an accurate 3D tree model, there are still gaps between existing simulation analysis and actual tests to explore vibration characteristics. Specifically, the vibration mechanism of winter jujube trees is still unclear in jujube orchards. To address the issue, a multi-view 3D reconstruction technique is employed to acquire precise 3D tree models for simulation analysis. The obtained results from experiments indicate that the determination coefficient R2 of the trunks and branches diameter are 0.96 and 0.91 between reconstructed and actual measurement results. Subsequently, material properties of jujube tree are measured to conduct model analysis and harmonic response analysis to find the optimal frequency range (10–20 Hz) in which a considerable vibration response can be obtained at low vibration energies. Moreover, transient analysis and test experiments are conducted to explore the energy transfer properties under different vibration frequency. Results showed that the acceleration response gradually increased from the bottom to the top of the branch on most branches at non-resonant frequencies. The proposed method can provide informative insights on the design of high-efficiency and low-energy jujube catch-and-shake harvesters.

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.

Determination of Some Physico-Mechanical Properties and Critical Velocity of Opium Poppy Plant (Papaver somniferum L.) 'Ofis 1' (Blue)

In this study, it was aimed to determine some physico-mechanical properties of the stalk and capsule of Ofis 1 (Blue) poppy plant, which has a significant production size in our country. According to the evaluated research findings, on the basis of average values, poppy capsule height was 37.99 mm, width was 35.82 mm, mass was 5.86 g, volume was 300.73 mm3, horizontal projection area was 18.66 cm², vertical projection area was 17.91 cm², critical velocity was 10.22 m s-1, natural angle of repose was 22.8o, and shell ratio was 43.93%. In the study, the poppy stalk was divided into three parts: upper, middle and lower regions. During the harvest period, the wet basis moisture value of the upper region was 7.69%, the moisture value of the middle region was 7.56%, the moisture value of the lower region was 8.03%, and the average moisture value of the poppy stalk was calculated as 7.76%. The average moisture value of poppy capsule at harvest time was determined as 7.69%. The number of plants per unit area in the poppy field was 30.8 pieces m-2, the number of capsules was 55.2 pieces m-2 and the capsule/plant ratio was 1.8. Average plant height was 119.7 cm. These values are the data that should be taken into consideration for table height and finger spacing in poppy harvesting machine designs. The majority of the plants per unit area (55.9%) are single capsule. As a result of mechanical tests, poppy stem shear force values were determined between 31.14 N-74.06 N and these values vary according to the thickness of the poppy plant stem. The average compression-crushing forces of poppy capsule were determined as 159.39 N and 110.46 N for vertical and horizontal positions, respectively. As a result of the research, Ofis 1 (Blue) poppy plant physico-mechanical properties and a resource that can be useful for the studies to be carried out in machine design studies for the cultivation and harvesting of poppy plants have been put forward.

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.