Simulation and evaluation of tomato growth by AquaCrop model under different agricultural waste materials

This study aimed to enhance the utilization of agricultural waste and identify the most suitable agricultural waste materials for tomato cultivation. It utilized a locally modified substrate labeled as CK, along with five different groups of agricultural waste materials, designated as T1 (organic fertilizer: loessial soil: straw in a ratio of 4:5:1), T2 (organic fertilizer: loessial soil: straw: grains in a ratio of 3:5:1:1), T3 (organic fertilizer: loessial soil: straw: grains in a ratio of 2:5:1:2), T4 (organic fertilizer:loessial soil:straw:grains in a ratio of 1:5:1:3), and T5 ( loessial soil:straw:grains in a ratio of 5:1:4), the AquaCrop model was employed to validate soil water content and tomato growth and yield under these treatments. Furthermore, a multi-objective genetic algorithm was employed to determine the optimal agricultural waste materials that would ensure maximum tomato yield, water use efficiency (WUE), partial factor productivity of fertilizer (PFP) and sugar-acid ratio. The results indicated that the AquaCrop model reasonably simulated volumetric soil water content, tomato canopy cover, and biomass, with root mean square error (RMSE) ranges of 20.0-69.4 mm, 15.2%-25.1%, and 1.093-3.469 t/hm2, respectively. The CK group exhibited an R-squared (R2) value of 0.63 for volumetric soil water contents, while the ratio scenarios showed R2 values exceeding 0.80. The multi-objective genetic optimization algorithm identified T5 as the optimal ratio scenario, resulting in maximum tomato yield, WUE, PFP, and quality. This study offers a theoretical foundation for the efficient utilization of agricultural wastes and the production of high-quality fruits and vegetables.

Image moments-based visual servoing control of bagged agricultural materials handling robot

Manual handling is less efficient and sometimes even hazardous to humans in many areas,for example,agriculture.Using robots in those areas not only avoids human contact with such dangerous agricultural materials but also improves working efficiency.The motion of a robot is controlled using a technique called visual servoing that uses feedback information extracted from a vision sensor.In this study,a visual servoing method was proposed based on learning features and image moments for 3D targets to solve the problem of image moment-based visual servoing.A Gaussian process regression model was used to map the relationship between the image moment invariants and the rotational angles around the X-and Y-axes of the camera frame(denoted asγandβ).To obtain maximal decoupled structure and minimal nonlinearities of the image Jacobian matrix,it was assumed two image moment features,which are linearly proportional toγandβ.In addition to the other four standardized image moment features,a 6-DOF image moment-based visual servoing controller for the agricultural material handling robot was designed.Using this method,the problem of visual servoing task failure due to the singularity of the Jacobian matrix was solved,and it also had a better convergence effect for the part of the target image beyond the field of view and large displacement visual servoing system.The proposed algorithm was validated by carrying out experiments tracking bagged flour in a six-degree-of-freedom robotic system.The final displacement positioning accuracy reached the millimeter level and the direction angle positioning accuracy reached the level of 0.1°.The method still has a certain convergence effect when the target image is beyond the field of view.The experimental results have been presented to show the adequate behavior of the presented approach in robot handling operations.It provides reference for the application of visual servoing technology in the field of agricultural robots and has important theoretical significance and practical value.

Hexavalent Chromium Adsorption from Aqueous Solution by Agricultural Waste Materials

Abilities of agricultural waste materials (walnut shell-WS, rice husk-RH, and peanut hull-PH) were tested as adsorbents for the adsorption of Cr(Ⅵ) from aqueous solution. Batch adsorption experiments were carried out to study the adsorption kinetics mechanism of Cr(Ⅵ) effect of adsorbent dosage, pH, contact time, and temperature. The best results are obtained at 15g/L adsorbent concentration, 60min contact time, 298K temperature, and 50mg/L adsorbate initial concentration at pH 2. The adsorption isotherms, using initial concentrations of Cr(Ⅵ) between 10 and 500mg/L for the Cr(Ⅵ) removal, show the maximum metal uptake capacities of adsorbent were 10.48, 6.71, and 8.54mg/g for WS, RH, and PH, respectively. And the adsorption data fitted well to the Langmuir adsorption isotherm for WS, RH, and PH with correlation coefficients of 0.9862, 0.9723, and 0.9714, respectively. Moreover, the FTIR analysis of WS, RH, and PH before and after adsorption of Cr(Ⅵ) suggested that Cr ions were combined to some functional groups of compounds contained in these materials.

Research on the Logistics Model of Agricultural Material

The paper states something about the characteristic of agricultural material, product and agricultural material market, and searches for the new agricultural material logistics model. In .addition, some proper strategies are provided for logistic companies.