Estimation of Soil Surface Roughness Using Stereo Vision Approach.

Soil roughness is one of the most challenging issues in the agricultural domain and plays a crucial role in soil quality. The objective of this research was to develop a computerized method based on stereo vision technique to estimate the roughness formed on the agricultural soils. Additionally, soil till quality was investigated by analyzing the height of plow layers. An image dataset was provided in the real conditions of the field. For determining the soil surface roughness, the elevation of clods obtained from tillage operations was computed using a depth map. This map was obtained by extracting and matching corresponding keypoints as super pixels of images. Regression equations and coefficients of determination between the measured and estimated values indicate that the proposed method has a strong potential for the estimation of soil shallow roughness as an important physical parameter in tillage operations. In addition, peak fitting of tilled layers was applied to the height profile to evaluate the till quality. The results of this suggest that the peak fitting is an effective method of judging tillage quality in the fields.

Novel environmentally friendly fuel: The effect of adding graphene quantum dot (GQD) nanoparticles with ethanol-biodiesel blends on the performance and emission characteristics of a diesel engine.

Biodiesel fuel has some disadvantages including increase in NOx, poor atomization and incomplete combustion. Additives and catalysts can be used to reduce the negative effects of biodiesel fuel. In addition, the use of metal oxide and metal nanoparticles causes environmental hazards. However, using biodegradable nanoparticles can significantly reduce such concerns. The present study investigated the effect of adding GQD + E to B10 fuel on the emission and performance characteristics of a diesel engine. B10 was blended with GQD (90 ppm) and bioethanol (E2, E4, E6 and E8% vol). Performance and emission characteristics, including power, torque, SFC, CO, CO2, UHC and NOx emissions were measured at the speeds of 1800, 2100 and 2400 rpm and full load mode. According to the results, the addition of GQD + E to B10 improved torque and power and decreased SFC, CO, UHC and NOx. Finally, the B10 + E6 + GQD90 fuel was the best fuel regarding improved engine performance and reduced exhaust emission. The average of changes in power and torque, SFC, CO, UHC and NOx compared to D100 for B10 + E6 + GQD90 were + 15.69%, +15.39%, -17.58%, -30.30%, -38.91% and -1.54%, respectively.

Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields.

Site-specific weed management and selective application of herbicides as eco-friendly techniques are still challenging tasks to perform, especially for densely cultivated crops, such as rice. This study is aimed at developing a stereo vision system for distinguishing between rice plants and weeds and further discriminating two types of weeds in a rice field by using artificial neural networks (ANNs) and two metaheuristic algorithms. For this purpose, stereo videos were recorded across the rice field and different channels were extracted and decomposed into the constituent frames. Next, upon pre-processing and segmentation of the frames, green plants were extracted out of the background. For accurate discrimination of the rice and weeds, a total of 302 color, shape, and texture features were identified. Two metaheuristic algorithms, namely particle swarm optimization (PSO) and the bee algorithm (BA), were used to optimize the neural network for selecting the most effective features and classifying different types of weeds, respectively. Comparing the proposed classification method with the K-nearest neighbors (KNN) classifier, it was found that the proposed ANN-BA classifier reached accuracies of 88.74% and 87.96% for right and left channels, respectively, over the test set. Taking into account either the arithmetic or the geometric means as the basis, the accuracies were increased up to 92.02% and 90.7%, respectively, over the test set. On the other hand, the KNN suffered from more cases of misclassification, as compared to the proposed ANN-BA classifier, generating an overall accuracy of 76.62% and 85.59% for the classification of the right and left channel data, respectively, and 85.84% and 84.07% for the arithmetic and geometric mean values, respectively.