Engineering in Agriculture, Environment and Food最新文献

Fuel consumption and power take-off (PTO) power requirement were measured for a 33.8 kW two-wheel drive tractor when used for operating a 1.6 m rotavator with 36 “L” shaped blades in sandy clay loam soil at an average soil moisture content of 8.8 ± 1% (dry basis) at IIT Kharagpur, India. Field experiments were conducted for a tractor with rotavator at seven different engine speeds (between 35 and 75% of full throttle engine speed), gear settings (L2 and L3) and depths of operation (60, 80 and 100 mm). Depth of operation, engine speed and gear setting were found to affect the fuel consumption of tractor. For the same PTO power consumption, lesser fuel consumption of tractor was observed in gear up conditions. A variation from −3.60 to −19.67% was observed while comparing the observed fuel consumption values with those predicted by the American Society of Agricultural and Biological Engineers (ASABE D 497.7) model. These variations were due to non-inclusion of gear settings in the ASABE fuel consumption model. Hence, an attempt was made to modify the ASABE fuel consumption model by incorporating gear settings in terms of speed ratio (peripheral speed of the rotavator to forward speed of the tractor i.e. u/v ratio). The developed fuel consumption model comprising engine speed, PTO power consumption and u/v could predict the observed values with a variation of ±6%.

In order to study the speed ratio regulation and dynamic change of hydraulic mechanical continuously variable speed tractor, it is necessary to comprehend the dynamic characteristics of the hydraulic circuit. The identification method was adopted to study the pump-motor system of transmission. Firstly, the typical identification method of combination modeling was selected to establish the model, and then the corresponding experiments were designed to determine the transfer function parameters and models of the combined modeling. Based on these, through further simplification and indirect methods, with the help of MATLAB toolbox, a fast system identification method was established by calculating the transmission ratio of the pump motor system through the output speed of the gearbox, the engine speed and the drive ratio of the front gear of the pump, as well as the transmission ratio of the gearbox. Filter was used to remove the influence of noise during the experiment. Compared with the test data, the models established by the two identification methods have higher accuracy. The positive and negative fitting rates of the fast identification method are 91.85 and 91.13, respectively, which can meet the needs of subsequent research. This study can be used as a reference for the subsequent control design of transmission and the study on the quality of the transmission.

In the grim situation of wood shortage, efficient utilize forest resources and rational use of wood have an important significance. Different kinds of trees have different use-value, so it is very important to identify the species of trees. Different species of trees have their own leaf characteristics. In this study, we proposed a novel feature extraction method based on geometric features and Haar wavelet, which can achieve the tree leaves feature rapid extraction. Extracting the geometrical features of leaves, at the same time, make Haar wavelet triple decomposition to the leaf image, calculating the leaves statistical characteristics like energy, entropy and mean values etc. Finally realize the recognition of tree species. The experimental results show that geometric features and statistical characteristics have significantly different, these differences can effectively identify the types of tree by using the classic adaboost threshold classifier, and the method is effective and practicable.

The study evaluates the Centimeter Level Augmentation Service (CLAS) of the Quasi-Zenith Satellite System (QZSS) for controlling a robot tractor. The QZSS transmits augmentation information through an L6 signal to enhance the positioning accuracy of the Global Navigation Satellite System (GNSS). Besides accessing the augmentation information through the L6 signal using a commercial QZSS receiver, this paper also introduces a method for using CLAS with a dual frequency receiver that cannot receive the L6 signal. Stationary and dynamic positioning experiments prove that the QZSS is able to improve the accuracy and availability of the current GNSS. Compensating for the biases of the CLAS positioning results relative to the current GNSS, a robot tractor works along with GNSS-based navigation within 5 cm accuracy.

The present study concerns the thermal pyrolysis kinetics of sieved rice husk that was classified into two sizes (38–200 μm) and (200–1000 μm) by using Thermo-Gravimetric analysis (TGA) at different heating rate (HR) values under N₂. The thermal pyrolysis analysis was presented and kinetic parameters as activation energy (E), frequency factor (A), and order of reaction (n) were determined by using three different kinetic models. The effect of heating rate (HR) and particle sizes on the chemical kinetic parameters were presented and discussed. Direct method gave lower values of E and A compared to the integral method. Results showed that as particle size increases, values of the activation energy (E) and frequency factor (A) nearly increase. The combustion characteristic parameters such as ignition, burnout and peak temperatures and their corresponding times were determined. It found that larger sizes (200–1000 μm) have a relatively lower ignition temperature, higher activation energy and noticeably lower ignition times as compared to the smaller sizes (38–200 μm).

Increase in global prices of grains further adds to difficulties in feeding livestock. Total Mixed Ration (TMR) formulated with food and agricultural by-products is considered as alternative animal feeds. However, it has associated problems particularly but not limited to decomposition due to high moisture content in most of them. To solve this problem, fermentation technology was brought up on TMRs. However, the fermentation condition may vary depending on the composition of the TMRs. This study set out to identify and determine a fermentation condition which can be applied regardless of the TMR composition. The Taguchi method L₉ (3⁴) orthogonal array was adopted in this research. The study considered 3 levels of 4 controllable factors (temperature, moisture content, bulk density, and fermentation period) and 2 uncontrollable factors (compositions and ratio of TMR samples). Quality score was calculated using the silage quality assessment method by analyzing pH and organic acid content (lactic acid, acetic acid, and butyric acid). Fermentation 40 L volume chamber (ϕ 300 × 400 H) was built and three TMR samples were fermented for the validation test. Results indicated that animal feed formulated with by-products had the highest quality score at a fermentation temperature of 20 °C, moisture content of 50%, a bulk density of 0.6 kg/m³, at 96-h fermentation period. This fermentation condition delivers the silage quality score of over 82 regardless of the composition of the materials used in formulating the feed.

Corn and soy have wide-ranging uses in food and biofuel industries due to its nutritional and energetic properties. In the present work, artificial drying experiments with hot air convection in different temperatures (30, 40 and 50 °C) were carried out with the addition of ozone (5, 10 and 15 min) applying a central composite design (CCD). The effective diffusion coefficient D_eff as thermodynamic properties was evaluated with and without the incorporation of ozone into drying air on corn and soy. The CCD showed different D_eff values and a numeric model was fitted to moisture diffusion during the drying-ozonation process (DOP) on corn-soy. Activation energy decreased from 43.90 to 35.20 kJ mol⁻¹ for corn and 38.23 to 34.29 kJ mol⁻¹ for soy when ozone was added into the drying air; similar observation occurred to enthalpy and entropy. Thus, the drying-ozonation process can be useful for technological purposes for energy improvement during postharvest stages, as well as maintaining the quality of cereal products and design of new dryers.

Grapevine yield prediction during phenostage and particularly, before harvest is highly significant as advanced forecasting could be a great value for superior grapevine management. The main contribution of the current study is to develop predictive model for each phenology that predicts yield during growing stages of grapevine and to identify highly relevant predictive variables. Current study uses climatic conditions, grapevine yield, phenological dates, fertilizer information, soil analysis and maturation index data to construct the relational dataset. After words, we use several approaches to pre-process the data to put it into tabular format. For instance, generalization of climatic variables using phenological dates. Random Forest, LASSO and Elasticnet in generalized linear models, and Spikeslab are feature selection embedded methods which are used to overcome dataset dimensionality issue. We used 10-fold cross validation to evaluate predictive model by partitioning the dataset into training set to train the model and test set to evaluate it by calculating Root Mean Squared Error (RMSE) and Relative Root Mean Squared Error (RRMSE). Results of the study show that rf_PF, rf_PC and rf_MH are optimal models for flowering (PF), colouring (PC) and harvest (MH) phenology respectively which estimate 1484.5, 1504.2 and 1459.4 (Kg/ha) low RMSE and 24.6%, 24.9% and 24.2% RRMSE, respectively as compared to other models. These models also identify some derived climatic variables as major variables for grapevine yield prediction. The reliability and early-indication ability of these forecast models justify their use by institutions and economists in decision making, adoption of technical improvements, and fraud detection.

A pneumatic harvesting, separating, and cleaning machine was designed and constructed to collect Jojoba seeds from the soil surface using cyclonic separation process. Jojoba seeds do not mature at once, whenever part of seeds reach maturation they fall naturally to the ground. So, more than one harvesting in the season may be necessary, depending upon weather conditions and grower preferences. To design the machine successfully, some of the physical and aerodynamic characteristics of Jojoba seeds and other undesired materials mix with the seeds were measured. The pneumatic harvesting machine was constructed at the workshop of Agricultural and Biosystems Engineering Department, Alexandria University, Egypt. The performance of the harvesting machine was investigated under different operation conditions such as suction air velocity, machine forward speed, length of the suction hose, clearance of the suction hose inlet above the soil surface, and the different ratios of materials other than Jojoba seeds (MOS). It was found that the best-operating conditions are when the suction air velocity is 30 m. s⁻¹, hose length is 2.5 m, suction hose clearance from the soil surface is 5 cm, machine forward speed is 1.2 km .h⁻¹, and the ratio of seeds to material other than seeds is 0.1. The research is atrial to produce a harvesting machine and then evaluated its performance under simulated field conditions. Although the performance of the harvester was considered satisfactory, it requires additional modifications and parts to make it commercial applicable.