Physical attributes of Cambisol in apple (Malus domestica Borkh. cv. Gala) orchard under different weed management systems in Urubici, Plan alto Serrano SC, Brasil

Schmitz, Daniela; Loss, Arcângelo; Oliveira, Bruno L.; Lovato, Paulo Emílio; Veiga, Milton da; Brunetto, Gustavo; Curmi, Pierre; Comin, Jucinei José

doi:10.15446/acag.v66n3.56379

Cited by 1 publication

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“…Arithmetic and geometric means are two commonly used mathematical terms differing in the method of calculation. Arithmetic mean (or simply the mean) is calculated by adding up all the numbers in the dataset and dividing the result by the total number of data points, while the geometric mean is calculated by multiplying the numbers in the dataset and taking the nth root of the result, where n is the total number of data points [57]. In this study, after extracting the features of the left and right channels and finding the corresponding points, the arithmetic and geometric mean of the points were calculated according to Equations (2) and (3).…”

Section: Arithmetic and Geometric Meansmentioning

confidence: 99%

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

Dadashzadeh

Abbaspour‐Gilandeh

Mesri-Gundoshmian

et al. 2020

Plants

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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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Section: Arithmetic and Geometric Meansmentioning

confidence: 99%