Assessment of BRRI Whole Feed Combine Harvester (Model BRRI WCH2021) for Mechanized Rice Harvesting in Bangladesh

Agricultural mechanization plays a pivotal role in the transition from subsistence to commercial agriculture, with a particular focus on labour-intensive activities like harvesting. This study assesses the operational characteristics of the BRRI Whole Feed Combine Harvester (Model BRRI WCH2021) at the field level. Developed under the SFMRA project, the harvester’s technical performance and loss assessment were conducted during the Boro 2022 and Aman 2022 seasons in farmer fields in Bangladesh’s Rangpur region. The field efficiency of the harvester was determined to be 62.5% and 57.9% in the Boro and Aman seasons, respectively. Fuel consumption rates were recorded at 2.77 l/ha and 2.31 l/ha for the Boro and Aman seasons. The total harvesting losses, encompassing cutter bar, shatter, cylinder, and separation loss, averaged 0.56% and 0.48% in the Boro and Aman seasons, respectively. Mechanized harvesting with the BRRI Whole Feed Combine Harvester significantly reduced paddy losses by 5.81% compared to manual methods. The field evaluation results indicate the combine harvester’s satisfactory performance, highlighting its potential to alleviate labour demands during peak harvesting. The development of the BRRI WCH offers a sustainable solution for rice harvesting mechanization among progressive farmers. It paves the way for the broader adoption of advanced agricultural technology in Bangladesh.

Optimization of Conventional Combine Harvester to Reduce Combine Losses for Basmati Rice (<i>Oryza Sativa</i>)

A study was conducted at PAU Regional Research Station, Gurdaspur (Punjab) to investigate the design and operational parameters of threshing mechanism of conventional combine harvester for basmati crop. This study was aimed to investigate the suitable changes required in the self-propelled conventional combine harvester for harvesting the basmati crop with minimum grain losses. Field evaluation of experiment was carried out to assess the influence of independent design variable i.e., arrangement of spikes (AS) and independent operational parameters such as concave clearance (CC) and cylinder speed (CS). The study was aimed to enumerate various combining losses viz., extent of visible and invisible grain damage and threshing efficiency at different AS, CC and CS levels. The first year data recorded during 2017 were processed for the optimization during 2018. The results of the present study revealed that during 2017, maximum visible and invisible losses was 5.49% ± 0.33% and 28.07% ± 3.21%, respectively whereas after modification, these losses remained only 4.00% ± 0.80% and 24.07% ± 2.86%, respectively. The threshing efficiency remained above 99.31% ± 0.47%, for both years. Thus, optimization of combine harvester was able to save the visible grain damage by 60% to 83% and invisible grain damage by 6% to 16%, respectively during 2018 than the year 2017.

Combine Harvester: Small Machine Solves Big Rice Harvesting Problem of Bangladesh

Bangladesh, a country with 87% of rural households that depend on agriculture and rice dominated diets (70%), engages the country’s food security. Therefore, there is strong advocacy for a mechanized paddy harvesting system for food security in South Asia, mainly Bangladesh. Some harvesting machinery is available;however, the precise roles of a combine harvester during this process are little. This study attempts to understand the consequences of combining harvesters for paddy harvesting and compare them with hand harvesting. For comparison of field performance of combine harvester with traditional harvesting, the experiment was conducted at Gazipur District, Bangladesh, in 2016. Results showed that the labor required for harvesting unit area 283, 15 and 9.87 man-h∙ha−1 for T1 (Sickle + Head carry + Beating + Kula), T2 (Reaper + Trolley carry + Close drum thresher + winnower), T3 (Combine harvester) system, respectively. Likewise, the harvesting costs were 1.84 (T2) and 2.5 (T1) times higher than the combine harvester (T3). Also, the postharvest loss can be saved 1.84%, to use combine harvester over manual harvesting of paddy. The above result revealed that the semi-mechanized and mechanized harvesting system is a time, labor and cost-saving system;however, the manual reaping system is concerned with more postharvest losses (3.09%). This study highlights the importance of combine harvester;however, further research is required in other places with large and mini-combine harvesters under diverse cropping areas and to test a combine harvester’s detailed potentiality.

Parameter matching and experiment of the combined cyclone separation and cylinder sieve cleaning system for rape combine harvester

Existing rape combine harvester with a cyclone separation cleaning device has the challenge that the loss rate and the cleaning rate increase and decrease simultaneously.A cleaning process route was proposed,which involves the cyclone separation cleaning device removing light and tiny impurities,and the cylinder sieve device removing coarse and long impurities such as pod shells and short stems.A novel cleaning system combining the cyclone separation cleaning device and cylinder sieve cleaning devices was designed.The ranges of the structure and operation parameters for each component were analyzed based on kinematics and dynamic analysis.A four-factor five-level quadratic orthogonal test was carried out,in which the loss rate and cleaning rate were taken as the evaluation indexes.The velocity at the suction port,the rotation speed of the cylinder sieve,the screw pitch of the spiral blade and the diameter of the sieve hole were taken as the influencing factors.The orthogonal test results showed that the cleaning system performed best at a rotation speed of the winnower is 600 r/min,an airflow velocity at the suction port is 18.25 m/s,a rotation speed of the cylinder sieve is 87 r/min,a screw pitch of the spiral blade is 440 mm and a diameter of the sieve hole is 4.48 mm.At this time,the loss rate of the cleaning system is 3.22%,and the cleaning rate is 95.67%.Compared to the conventional cyclone separation cleaning device,the loss rate is reduced by 2.17%and the cleaning rate is increased by 1.05%.This study can provide a reference for the optimal cleaning system design for rape combine harvesters.

Experiment and parameter optimization of an automatic row following system for the traction beet combine harvester

To improve the automation level and operation quality of China's beet harvester and reduce the loss due to damaged and missed excavation,this study used a self-developed sugar beet combine harvester and field simulation experiment platform,based on the single-factor bench test of the automatic row following system in the early stage,taking hydraulic flow A,spring preload B,and forward speed C which have significant influence on performance indices as test factors,and taking the missed excavation rate,breakage rate and reaction time as performance indices,the orthogonal experimental study on the parameter optimization of the three-factor and three-level automatic row following system with the first-order interaction of various factors was carried out.The results of the orthogonal experiments were analyzed using range analysis and variance analysis.The results showed that there were differences in the influence degree,factor priority order and first-order interaction,and the optimal parameter combination on each performance index.A weighted comprehensive scoring method was used to optimize and analyze each index.The optimal parameter combination of the overall operating performance of the automatic row following system was A 2B 2C 1,that is,the hydraulic flow was 25 L/min,the forward speed was 0.8 m/s,and the spring preload was 198 N.Under this combination,the response time was 0.496 s,the missed excavation rate was 2.35%,the breakage rate was 3.65%,and the operation quality was relatively good,which can meet the harvest requirements.The comprehensive optimization results were verified by field experiments with different ridge shapes and different planting patterns.The results showed that the mean values of the missed excavation rate of different planting patterns of conventional straight ridges and extremely large"S"ridges were 2.23%and 2.69%,respectively,and the maximum values were 2.39%and 2.98%,respectively;the average damage rates were 3.38%and 4.14%,and the maximum values were 3.58%and 4.48%,which meet the industry standards of sugar beet harvester operation quality.The overall adaptability of the automatic row following system is good.This study can provide a reference for research on automatic row following harvesting systems of sugar beets and other subsoil crop harvesters.

Effects of working parameters on the performance of cyclone separator for rapeseed combine harvester based on CFD

Existing development for cyclone separation cleaning components of the rapeseed combine harvester,which employs the suspending airflow to separate the rapeseeds from the materials other than grain(MOG),has the challenge to figure out the optimal working parameters,highlighting a need for exploration of the invisible airflow based on Computational Fluid Dynamics(CFD).The airflow status was mainly affected by the air velocities of the inlet,and the outlet for the MOG.The single factor and response surface experiments were carried out.It could be found that the inlet and MOG outlet velocities affected the air velocities through the change in the air quantity.Furthermore,the mathematical model of the relationship between the air velocities inside the cyclone and the working parameters was built,and the optimal combination of working parameters was obtained by multi-objective optimization.The inlet and outlet velocities of the optimal combination were 4.25 m/s and 29.87 m/s,respectively.Under this condition,the cleaning ratio and loss ratio of the cleaning device was 94.62%and 5.39%,respectively,as validated by the field experiment.The findings provide references for the improvement of cleaning systems for rapeseed combine harvesters.

Research and optimization of the hand-over lifting mechanism of a sweet potato combine harvester based on EDEM

In order to meet the challenge of high loss rate and high tuber injury rate during the operation of the 4UZL-1 sweet potato combine harvester,the sweet potato tuber hand-over lifting mechanism was designed based on a comprehensive analysis of the entire structure of the harvester.Taking the average normal force on conveying sweet potato tubers as evaluation indexes,the EDEM simulation experiment was carried out considering the factors of the angle of the excavating and conveying mechanism,the conveying angle of the scraper chain,the speed of the excavating and conveying mechanism,and the speed of the scraper chain conveying.The experimental findings indicated that the optimal operational performance of the hand-over lifting mechanism was achieved at a speed of 1.15 m/s for the excavating and conveying mechanism,an angle of 24°for the excavating and conveying mechanism,a conveying speed of 0.66 m/s for the scraper chain,and a conveying angle of 60°for the scraper chain.Field experiments were conducted based on the EDEM simulation experiment,using the loss rate and the injury rate of conveying sweet potato tubers as evaluation indexes.The results showed that the hand-over lifting mechanism demonstrated optimal operational effectiveness with an excavating and conveying mechanism angle of 20°,a scraper chain angle of 68°,an excavating and conveying mechanism speed of 1.2 m/s,and a scraper chain speed of 0.66 m/s,while the machine running speed was maintained at 1 m/s.At the moment,the hand-over lifting structure exhibited a loss rate of 1.12%and a tuber injury rate of 0.94%.The conclusions obtained serve as a valuable reference for future research and optimization of sweet potato combines.

Artificial neural network-based repair and maintenance cost estimation model for rice combine harvesters

This research proposes an artificial neural network(ANN)-based repair and maintenance(R&M)cost estimation model for agricultural machinery.The proposed ANN model can achieve high estimation accuracy with small data requirement.In the study,the proposed ANN model is implemented to estimate the R&M costs using a sample of locally-made rice combine harvesters.The model inputs are geographical regions,harvest area,and curve fitting coefficients related to historical cost data;and the ANN output is the estimated R&M cost.Multilayer feed-forward is adopted as the processing algorithm and Levenberg-Marquardt backpropagation learning as the training algorithm.The R&M costs are estimated using the ANN-based model,and results are compared with those of conventional mathematical estimation model.The results reveal that the percentage error between the conventional and ANN-based estimation models is below 1%,indicating the proposed ANN model’s high predictive accuracy.The proposed ANN-based model is useful for setting the service rates of agricultural machinery,given the significance of R&M cost in profitability.The novelty of this research lies in the use of curve-fitting coefficients in the ANN-based estimation model to improve estimation accuracy.Besides,the proposed ANN model could be further developed into web-based applications using a programming language to enable ease of use and greater user accessibility.Moreover,with minor modifications,the ANN estimation model is also applicable to other geographical areas and tractors or combine harvesters of different countries of origin.

Selection of proper combine harvesters to field conditions by an effective field capacity prediction model

Farmers have to finish their harvesting with high efficiency,because of time and cost.However,farmers are lacking knowledge and information required for selecting suitable combine harvesters and giving the conditions of their rice fields,because both information factors(combine harvester and field condition)impact the field capacity.The field capacity model was generated from combine harvesters with the Thai Hom Mali rice variety(KDML-105).Therefore,this study aimed to determine the prediction model for effective field capacity to combine harvesters when harvesting the Thai Hom Mali rice variety(KDML-105).The methods began by collecting data of 15 combine harvesters,such as field,crop,and machine conditions and operating times;to generate the prediction model for the KDML-105 variety.The prediction model was then validated using 12 combine harvesters that were collected similarly to the model creation.The results showed a root mean square error(RMSE)of 0.24 m^(2)/s for the model.The prediction model can be applied for farmers to select the proper combine harvesters and give their field conditions.

Development of uncut crop edge detection system based on laser rangefinder for combine harvesters

The objective of this research was to develop an uncut crop edge detection system for a combine harvester.A laser rangefinder(LF)was selected as a primary sensor,combined with a pan-tilt unit(PTU)and an inertial measurement unit(IMU).Three-dimensional field information can be obtained when the PTU rotates the laser rangefinder in the vertical plane.A field profile was modeled by analyzing range data.Otsu’s method was used to detect the crop edge position on each scanning profile,and the least squares method was applied to fit the uncut crop edge.Fundamental performance of the system was first evaluated under laboratory conditions.Then,validation experiments were conducted under both static and dynamic conditions in a wheat field during harvesting season.To verify the error of the detection system,the real position of the edge was measured by GPS for accuracy evaluation.The results showed an average lateral error of±12 cm,with a Root-Mean-Square Error(RMSE)of 3.01 cm for the static test,and an average lateral error of±25 cm,with an RMSE of 10.15 cm for the dynamic test.The proposed laser rangefinder-based uncut crop edge detection system exhibited a satisfactory performance for edge detection under different conditions in the field,and can provide reliable information for further study.

Real-time grain breakage sensing for rice combine harvesters using machine vision technology

Breakage rate is one of the most important indicators to evaluate the harvesting performance of a combine harvester.It is affected by operating parameters of a combine such as feeding rate,the peripheral speed of the threshing cylinder and concave clearance,and shows complex non-linear law.Real-time acquisition of the breakage rate is an effective way to find the correlation of them.In addition,real-time monitoring of the breakage rate can help the driver optimize and adjust the operating parameters of a combine harvester to avoid the breakage rate exceeding the standard.In this study,a real-time monitoring method for the grain breakage rate of the rice combine harvester based on machine vision was proposed.The structure of the sampling device was designed to obtain rice kernel images of high quality in the harvesting process.According to the working characteristics of the combine,the illumination and installation of the light source were optimized,and the lateral lighting system was constructed.A two-step method of“color training-verification”was applied to identify the whole and broken kernels.In the first step,the local threshold algorithm was used to get the edge of kernel particles in a few training images with binary transformation,extract the color spectrum of each particle in color-space HSL and output the recognition model file.The second step was to verify the recognition accuracy and the breakage rate monitoring accuracy through grabbing and processing images in the laboratory.The experiments of about 2300 particles showed that the recognition accuracy of 96%was attained,and the monitoring values of breakage rate and the true artificial monitoring values had good trend consistency.The monitoring device of grain breakage rate based on machine vision can provide technical supports for the intellectualization of combine harvester.

Proportional distribution method for estimating actual grain flow under combine harvester dynamics

The yield monitors use a constant delay time to match the grain flow with location.Therefore,mixing and smoothing effects on the grain flow are neglected.Although constant time delay compensates for time mismatch,actual grain flow at a combine harvester head is not equal to the grain flow measured by a sensor due to the dynamics effects.In order to eliminate the dynamics effects,a new method for estimating actual grain flow,called proportional distribution(PD),is proposed.This method assumes that actual grain flow is directly proportional to the feedrate.Based on this assumption,the actual grain flow results from redistributing accumulated grain mass over a certain time according to the profile of the feedrate.The PD can avoid the dynamics effects because the feedrate is measured at a combine harvester’s head.Compared with constant time delay,the proposed method can effectively estimate actual grain flow and be applied to improve the accuracy of yield maps.

Inertial force balance and ADAMS simulation of the oscillating sieve and return pan of a rice combine harvester

To reduce the inertial force of the oscillating sieve and return pan of a rice combine harvester,partial equilibrium was adopted.Firstly,based on the kinematic analysis of a slider-crank mechanism,the appropriate mass of counterweight intervals was achieved.Then,an ADAMS dynamic simulation was used to determine the optimum balance mass of the oscillating sieve and return pan individually.Considering the relative motion between the return pan and the oscillating sieve,the overall inertial force of the two parts would be reduced.The simulation results indicated that the optimum counterweight of the oscillating sieve was 15.5 kg based on an analysis of the movement tracks of the mass center and overall inertial force.The results also showed that the overall balance of inertial force not only reduced the counterweight but also decreased the overall inertial force of the oscillating sieve and return pan.Finally,a search for the most suitable crank initial angle of the return pan to reduce the overall inertial force and optimize the overall balance revealed three groups of initial angles:φ=0°,φ=45°,andφ=90°.The results indicated that arranging the initial angle of the crank of the oscillating sieve and return pan in the same position(φ=90°)was the most favorable for reducing the overall inertial force.In this situation,the optimum counterweight of the oscillating sieve was only 14.0 kg.The results can provide references for the design of the overall balance of the inertial force in the cleaning components of a combine harvester.

Evaluation of operator visibility in three different cabins type Far-East combine harvesters

The purpose of this study was to develop and evaluate a visibility evaluation system for the cabin type Far-East combine harvester.The human field of view has been classified into five levels(perceptive,effective,stable gaze,induced,and auxiliary)depending on the rotation of the human head and eye.The divider,reaper lever,gearshift,dashboard,and conveying component were considered as major viewpoints of the cabin type Far-East combine harvester.The visibility of the cabin type Far-East combine harvester was evaluated quantitatively using viewpoints and human field of view levels.The visibility evaluation system for the cabin type Far-East combine harvester consisted of a laser pointer,stepping motors to control the directions of the view,gyro sensors to measure horizontal and vertical angles,and I/O interface to acquire the signals.The visibility evaluation tests were conducted at different postures(‘sitting straight’,‘sitting with a 15°tilt’,‘standing straight’,and‘standing with a 15°tilt’)for three cabin type Far-East combine harvesters.The LSD(least significant difference)multiple comparison tests showed that the visibilities of viewpoints differed significantly as the operator's posture changed.The results showed that the posture while standing with a 15°tilt provided the best visibility.The average visibility scores at sitting postures were 22.3(straight)and 24.4(15°tilt),and the scores at standing postures were 18.7(straight)and 29.5(15°tilt).Also,the average visibility scores were observed in order from highest to lowest as reaper lever(44.6),divider(28.7),dashboard(23.1),conveying part(12.2),and gearshift lever(10.1).Most viewpoints of the cabin type Far-East combine harvester were out of the stable gaze field of the view level.Modifications of the cabin type Far-East combine harvester design will be required to enhance the visibility during harvesting operation and to improve safety and convenience of farmers.

Experimental study on driver seat vibration characteristics of crawler-type combine harvester

To improve the driving comfort of combine harvesters,driver seat low-frequency vibration and related driver ride-comfort problems were investigated on a Chinese CFFL-850 crawler-type full-feed combine harvester based on ISO2631.Driver vibration and driving seat transmission characteristics were measured under the following conditions:no-load idling,driving on the road,driving in the field,and simulated harvesting.The root mean square values composite vibration under four conditions were 3.63 m/s^(2),2.35 m/s^(2),3.34 m/s^(2),and 2.67 m/s^(2),respectively.For the same condition,the maximum root mean square scores of vibration component on driver whole-body occurred in the seat support surface(test point 1)and vertical direction(Z direction),which were 3.56 m/s^(2),2.05 m/s^(2),3.15 m/s^(2),and 2.43 m/s^(2),respectively.The test point 2 to test point 1 vertical-transfer function curve trends were nearly identical.Nearly all of the transfer coefficients were greater than 1 in the range of 1-50 Hz,therefore,the seat vibration attenuation performance was poor.Based on the analysis results,the driver seat structure was altered and a verification test was performed.The test results indicated that after an X-damping mechanism was installed,vibration acceleration,on the surface of the seat support under the road-driving conditions,decreased from 2.35 m/s^(2) to 1.68 m/s^(2).Under the simulated harvesting condition,the vibration acceleration decreased from 2.56 m/s^(2) to 1.46 m/s^(2).Nearly all of the seat vertical transfer coefficients were less than 1 within the frequency range of 1-80 Hz,therefore the dynamic comfort of the seat was ameliorated after structural improvement.

Development of a measurement system for noise and vibration of combine harvester

Combine harvesters are important in that grain and legumes directly affect the production economy.On the other hand,it forms an important branch of the agricultural machinery sector.There are many unknown constraints on machines when harvesting with a combine harvester,including the noise and vibration that occur in the combine harvester thresher.This study aims to analyze noise and vibration in conventional combine harvesters using sensors.Two accelerometers were installed on each side of the concave and two microphones were used to register the noise of the threshing process.The result showed that effects of machine speeds(3.5 km/h,4.5 km/h and 5.5 km/h)and rotor speeds(950 r/min,850 r/min and 950 r/min)were significantly important for the mean range values of the left and right accelerometer in the X,Y and Z direction.The machine’s speed and rotor speed were not significantly important for the mean range values of the left and right microphones.

Modeling of working environment and coverage path planning method of combine harvesters

This paper mainly studied the working environment modeling and coverage path planning of combine harvesters.The boundaries of the farmland to be harvested were extracted through the farmland satellite imagery and canny algorithm.The polygon approximation method was used to fit the extracted boundaries as polygons.The edge offset of the farmland and obstacles was realized based on the principle of straight skeleton.According to the structure data of the split points which were obtained through the improved scan line algorithm,the coverage path planning of the combine harvester was realized.Moreover,the circular arc transition algorithm was used to optimize the harvesting paths to achieve the smooth turning of the combine harvester at the edge of farmland and when encountering obstacles.The simulation results show that the proposed the proposed polygon approximation method can accurately depict the boundaries of the farmland to be harvested,and reduce the amount of data to be stored.Additionally,the designed path planning method can realize the coverage path planning of the combine harvester in irregular and internal obstacle farmland.

Development of the automatic navigation system for combine harvester based on GNSS

An automatic navigation system was developed to realize automatic driving for combine harvester,including the mechanical design,control method and software design.First of all,for the harvester modified with the automatic navigation system,a dynamic calibration method of the rear wheel center position was proposed.The control part included the navigation controller and the steering controller.A variable universe fuzzy controller was designed to the navigation controller,which used fuzzy control to change the fuzzy universe of input and output dynamically,that means,under the condition that the fuzzy rules remain unchanged,the fuzzy universe changes with the change of input,which is an adaptive fuzzy control method and can modify the control strategy in time.To realize the automatic navigation of the harvester,the decision result of the navigation controller based on the variable universe fuzzy control was input into the steering controller,and then the electric steering wheel was controlled to rotate.To test the performance of the designed automatic navigation system,the field experiment was carried out.When the combine harvester was navigating linearly at a speed of 0.8 m/s,the overall root mean square error(RMSE)of the lateral deviation was 5.87 cm.The test results showed that the system was designed could make the combine track the preset path smoothly and stably,and the tracking accuracy was at the centimeter level.

Simulation and analysis of the pneumatic recovery for side-cutting loss of combine harvesters with CFD-DEM coupling approach

A novel pneumatic recovery method was proposed to curb the problem of high losses caused by side-cutting in a rapeseed combine harvester header.The influence of recovery method and material status changes on the recovery effect was studied via the CFD-DEM(computational fluid dynamic-discrete element method)coupling simulation.The effect of airflow action on the recovery effect was compared and analyzed,and the composite pneumatic recovery method was determined.In addition,the influence of material status changes and material feeding rate on the recovery effect was explored,and the critical condition of material blockage in the recovery device was configured.As such,the relationship model between air velocity and recovery rate was constructed and the air distribution ratio of the flow field in the device under this condition was optimized,had verified the rationality of this pneumatic recovery method was verified by a series of field tests.The average rapeseed recovery rate of 92.95%was achieved with the application of the recovery device,and the total loss rate of the header reduced by 52.26%,which is of great significance in reducing the total loss rate of the combine harvesters and improving the operation performance of machinery.The research results can provide a reference for the design of the header structure of a rape combine harvester.

Fault prediction of combine harvesters based on stacked denoising autoencoders

Accurate fault prediction is essential to ensure the safety and reliability of combine harvester operation.In this study,a combine harvester fault prediction method based on a combination of stacked denoising autoencoders(SDAE)and multi-classification support vector machines(SVM)is proposed to predict combine harvester faults by extracting operational features of key combine components.In general,SDAE contains autoencoders and uses a deep network architecture to learn complex non-linear input-output relationships in a hierarchical manner.Selected features are fed into the SDAE network,deep-level features of the input parameters are extracted by SDAE,and an SVM classifier is then added to its top layer to achieve combine harvester fault prediction.The experimental results show that the method can achieve accurate and efficient combine harvester fault prediction.In particular,the experiments used Gaussian noise with a distribution center of 0.05 to corrupt the test data samples obtained by random sampling of the whole population,and the results showed that the prediction accuracy of the method was 95.31%,which has better robustness and generalization ability compared to SVM(77.03%),BP(74.61%),and SAE(90.86%).