Biosystems Engineering最新文献

To accurately predict and evaluate the performance of the crushing device of no-till planter, it is strongly necessary to establish an accurate interaction model of the straw-crushing device coupling system. The traditional model has low prediction accuracy and cannot simulate the fracture characteristics of the straw during the operation of the crushing device due to the straw being usually regarded as a rigid body or a combination of rigid ball joints. In this paper, a breakable and flexible hollow straw model is first proposed and a CFD-DEM coupling model of the straw group-crushing device considering the effect of gas-solid two phase is also established. Afterwards, the corresponding verification experiments are conducted and the effect of working and structural parameters on the straw-throwing weight and crushing length is also analysed. It is demonstrated that the relative error between the simulation results and the experimental results for the straw crushing length is 7.7%, and the relative error for the straw-throwing weight is 7.1%, thereby verifying the correctness of the CFD-DEM model. Finally, the response surface method is also used to determine the optimal working and structural parameters of the crushing device and corresponding field tests verification is also conducted. Results indicate that the optimal parameter combination is as: the rotational speed of the crushing spindle is 2400 rpm, the inlet clearance is 30 mm and the starting point angle is 45°. Moreover, the crushing length after optimisation is 6.6% smaller than before optimisation, and the straw-throwing weight is increased by 11.5%.

In this study, the Discrete Element Method (DEM) was employed as a computational approach to analyse the bulk behaviour of wheat (Triticum aestivum L., cv. ‘Pionier’) under compressive loading. A laboratory compression apparatus was utilised as a basis for the acquisition of data from compression tests. To ensure accurate DEM predictions, the physical, mechanical, and interaction properties of wheat kernels were experimentally determined for two different moisture contents (12.4% and 25.4% w.b.). Five particle models were constructed to represent the geometric shape of the kernel, with the model composed of five sub-spheres identified as the most suitable, ensuring an optimal balance between computational complexity and simulation accuracy. The bulk compression results from DEM simulations were compared to experimental data, revealing a good agreement for volumetric strain, bulk density increase, and bulk compressibility within the specified range of applicability. Simulations provided valuable insights into temporal and spatial variations of forces and deformations acting on individual particles, thereby enhancing the understanding of bulk behaviour at different compression levels. The moisture content was found to significantly affect the particle compressive forces, deformation capabilities, and bulk compression characteristics. In conclusion, this study demonstrated the great potential of DEM in predicting the bulk behaviour of wheat under compression, providing valuable information for practical storage and handling processes.

Cucumber downy mildew, caused by Pseudoperonospora cubensis, typically remains hidden from view during its initial infection stage. To address this issue, this study proposed an early diagnosis model for greenhouse cucumber downy mildew. This study was conducted under controlled conditions, utilising a large-scale mobile chlorophyll fluorescence imaging system to monitor the samples during their seedling stage on a daily basis from the first day of inoculation. A total of 98 sets of fluorescence parameter values and corresponding fluorescence images were collected. Machine learning methods, such as recursive feature elimination (RFE) and Least Absolute Shrinkage and Selection Operator (LASSO) regression with L1 regularisation, were used to screen the chlorophyll fluorescence parameter Ft_D3, whose corresponding the chlorophyll fluorescence images were used as inputs to the proposed model, following by employing a convolutional neural network (CNN) transfer learning method to the early detection task of cucumber downy mildew in fluorescence images. The study improved the topology structure of ResNet50, the network model with a learning rate of 0.001 and 16 cycles as the optimal feature extractor. The results indicated that the enhanced network displayed improved performance in early detection of cucumber downy mildew compared with other CNNs. Infected leaves were distinguished from healthy leaves in the early stages of infection, specifically 3 days before the appearance of symptoms. The accuracy of the model in the task of early diagnosis of downy mildew was 94.76%. This study presents an efficient approach for the photosynthetic characterisation and early identification of cucumber downy mildew.

In Japan, small agricultural tractors without axle suspensions used on bumpy farm roads experience severe vibrations. Excessive vibrations often cause tyres to lose contact with the supporting ground or decrease the vertical tyre force such that the cornering force exceeds the static friction limit. The resulting bouncing and sliding motions induce steering instability, resulting in overturning accidents. This study investigated the effectiveness of axle suspension in alleviating bump disturbance-induced steering instability in small tractors. A step lane change test involving bump disturbances was newly proposed for the steering stability testing of small tractors. The numerical experiments compared four axle suspension structures: no-, front-, rear-, and full-suspensions. The results showed that bump disturbances induce steering instability in the no- and rear-suspension tractors. In contrast, the front- and full-suspension structures significantly improved the steering stability of the tractor by keeping the steering wheel in contact with the ground. The proposed testing method helped evaluate improvements in the steering stability brought about by increasing the ground contact of the steering wheel. Our results can aid in designing and testing suspension systems for small tractors.

The rational fertilisation of olive trees, based on adding exclusively the nutrients that are actually needed, is important from both the economic and environmental sustainability points of view. This paper employs UAV-obtained multispectral data collected from five different orchards located in Southern Spain to build a set of models for the prediction of the leaf nutrient content of olive trees using Support Vector Regression. The paper shows the convenience of addressing the problem as a Multiple Instance Regression, and compares two strategies of data aggregation and different choices of feature vectors derived from the raw multispectral data. The models provided good results for N, P and K (r² = 0.76, r² = 0.87 and r² = 0.91, respectively for the Hojiblanca model, and r² = 0.79, r² = 0.80 and r² = 0.80 for the Picual model). The rest of nutrients studied also offered good results for both the Picual and Hojiblanca models, ranging from r² = 0.69 for B to r² = 0.93 for Cu. The results indicate a robust performance of the models and a potential for improvement with the addition of more data, along with an advantage of considering individual models for each cultivar variety. Overall, these results are very promising for the estimation of the leaf nutrient content of olives trees and the detection of spatial variability in the fertilisation needs of orchards.

In this study, a method is proposed to deal with the variable light conditions in a greenhouse to non-destructively predict the soluble solid content (SSC) of tomatoes on the plant. It was investigated how well the SSC – measured as °Brix – of tomatoes could be predicted based on spectral data in the range of 470–900 nm, where data acquired in situ (in the greenhouse) was compared to post-harvest data captured under controlled laboratory conditions. To deal with the variation in illumination in the greenhouse, a dynamic-calibration method is proposed, using a grey reference in the image. Ground-truth SSC data of the tomatoes was acquired using a refractometer. Data was collected of three different types of truss tomatoes with a wide range of SSC. Different PLS regression models were then trained on the spectral data in relation to the refractometer values. Trained and tested on all types, the in situ measurements showed a predicted coefficient of determination on the test set, Q², of 0.95 with a Root Mean Squared Error of Prediction (RMSEP) of 0.29 °Brix using the dynamic calibration, and a Q² of 0.93 with RMSEP of 0.35 °Brix without using the dynamic-calibration method. The post-harvest measurements resulted in a Q² of 0.95 with RMSEP of 0.31 °Brix. The results show that spectral imaging using dynamic calibration is applicable for in situ non-destructive prediction of SSC. This method enables high-throughput and non-destructive quality estimation of fruits on the plant in commercial greenhouse conditions.

Modified atmosphere storage containers with gas-permeable membranes are effective in reducing post-harvest losses, mainly at low temperatures but become ineffective in controlling O₂ and CO₂ at temperatures above 3 °C. This study aimed to develop a method to control O₂ in a storage box. This method uses a diffusion tube, that blocks air from entering the box but allows for air exchange when an air blower is activated. The blower ON frequency (BOF) was modelled as a function of temperature, considering the type and amount of produce, blower properties, tube dimension, and the O₂ setpoint. The effects of temperature and gas composition on respiration rate kinetics were analysed with Michaelis-Menten and Arrhenius equations. The model was used to program the microcontroller to control the blower. Validation was performed using a 190 L box containing 25 kg of broccoli. The developed model maintained the desired O₂ concentration under fluctuating temperatures. The BOF values ranged from 25.6 to 549.2 s h⁻¹, for temperature fluctuations between 4 and 23 °C. The time required to reach the 3% O₂ in the box differed depending on the blower's state. With the blower turned ON from the beginning, it took 24.9 h. However, when the blower was initially turned OFF, it took 11.1 h. Despite temperature changes, the system maintained O₂ at 3.8 ± 0.29% and CO₂ at 14.4 ± 0.66%. The system is promising for commercial use and best suited for CO₂ tolerant produce because it requires a separate mathematical model and control mechanism.

The service life and irrigation efficiency of drip irrigation systems are largely determined by the anti-clogging performance of the emitters. The degree and speed of clogging of the emitters are governed by the motion state of the particles in the flow channel. Computational fluid dynamics coupled discrete element method (CFD-DEM) and experiments were used to analyse anti-clogging performance and particles transport characteristics of a stellate irrigation emitter. With the angle of repose of sediment particles as the optimization objective, the key parameters of physical properties of sediment particles and container were obtained in the discrete phase model using a Plackett-Burman test, steepest climb test and Box-Behnken test. A novel evaluation method for assessing anti-clogging performance based on particles mass passing rate is proposed and its accuracy experimentally verified. Using three particles groups with different particle size distributions, the anti-clogging performance of stellate emitter was found to better than the widely used toothed emitter. In particular, the anti-clogging performance of the stellate emitter was found to be superior with the sensitive particle size of 0.03 mm. It was shown that sediment particles demonstrated retention, bonding and deposition in the inlet far away from the flow channel, the backwater area at the vertical vertex of the stellate structure and the backwater area at the joint of the stellate emitter flow channel structure units.

An efficient tractor path-tracking method can increase the operational accuracy, improve land utilisation, and more efficiently provide services for autonomous tractors and precision agriculture. In this study, a model-free adaptive predictive control-proportional-integral-derivative (MFAPC-PID) method was devised. Based on preview theory, a tracking system based on course deviation angle was designed. After the tracking system was linearised, an MFAPC tracking system was developed, and its inherent defects were analysed. Referring to the control structure of an incremental PID algorithm, the MFAPC was considered as an adaptive integral item, and the MFAPC-PID controller was obtained by adding adaptive proportional and differential terms. To verify the proposed controller, collaborative simulation and hardware-in-the-loop tests were performed. The controller was simulated and tested under different paths, road surfaces, and tractor models. Compared with other methods, the MFAPC-PID path-tracking method exhibits superior comprehensive performance, adaptability, universality, and robustness. Moreover, the MFAPC-PID method is insensitive to external interference and model changes of controlled objects.

Coverage path planning is a central task in agricultural field operations such as tillage, planting, cultivation, and harvesting. In future visions, manual operation will be replaced by fleets of autonomous agricultural vehicles that perform the tasks autonomously. A step towards this transition is to enable simultaneous and safe cooperation of autonomous vehicles on the field. In this article a novel approach is presented for coverage path planning (CPP) for two autonomous tractors that perform sequentially dependent tasks simultaneously on the same area. The approach is based on the idea of computing the coverage solutions for each task by dividing them into short paths that consist of a swath and a turn. The approach ensures collision avoidance by examining that the simultaneous short paths, operated by different tractors, do not collide geometrically, and then schedules them to be operated simultaneously in real-time. The approach was demonstrated successfully in a real-world test environment with two autonomous tractors. The tractor that performed the first task was equipped with a disc cultivator and the second tractor was equipped with a seed drill. A test area of 0.8 ha was used for the demonstration drive, during which the tractors drove 22 swaths simultaneously. Both tractors completed their respective tasks.