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.

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Eco-efficiency has become a cornerstone in improving the environmental and economic performance of farms. The joint use of life cycle assessment (LCA) and data envelopment analysis (DEA), known as LCA + DEA methodology, is an expanding area of research in this quest. LCA estimates the environmental impacts of the products or services, while DEA evaluates their efficiency, providing targets and benchmarks for the inefficient ones. Because energy consumption and environmental quality are highly interdependent, we carried out a study to examine energy efficiency and environmental emissions associated with rain-fed barley farms in Kermanshah Province, Iran. Fifty-four rain-fed barley farms were randomly selected, and production data were collected using questionnaires and interviews. DEA and LCA were used to quantify and compare environmental indicators before and after efficiency improvements were applied to the farms. To accomplish this, efficient and inefficient farms were identified using DEA. Then environmental emissions were measured again after inefficient farms reached the efficiency limit through management improvements. The results showed that by managing resource use, both energy consumption and environmental emissions can be reduced without yield loss. The initial amount of energy consumed averaged 13,443 MJ/ha while that consumed in the optimal state was determined to be 12,509 MJ/h, resulting in a savings of 934 MJ/ha. Based on the results of DEA, reductions in nitrogen fertilizer, diesel fuel, and phosphate fertilizer offered the greatest possibilities for energy savings. Combining DEA and LCA showed that efficient resource management could reduce emissions important to abiotic depletion (fossil fuels), human toxicity, marine aquatic ecotoxicity, global warming (GWP100a), freshwater aquatic ecotoxicity, and terrestrial ecotoxicity. This study contributes toward systematically building knowledge about crop production with the joint use of LCA + DEA for eco-efficiency assessment.

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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.