Dadashzadeh Mojtaba, Abbaspour-Gilandeh Yousef, Mesri-Gundoshmian Tarahom, Sabzi Sajad, Hernández-Hernández Jose Luis, Hernández-Hernández Mario, Arribas Juan Ignacio
Department of Biosystems Engineering, College of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran.
Division of Research and Graduate Studies, TecNM/Technological Institute of Chilpancingo, Chilpancingo 39070, Mexico.
Plants (Basel). 2020 Apr 27;9(5):559. doi: 10.3390/plants9050559.
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.
作为生态友好型技术，特定地点的杂草管理和除草剂的选择性施用仍然是具有挑战性的任务，特别是对于像水稻这样的密集种植作物。本研究旨在开发一种立体视觉系统，通过使用人工神经网络（ANN）和两种元启发式算法来区分水稻植株和杂草，并进一步区分稻田中的两种杂草类型。为此，在稻田中录制了立体视频，并提取了不同通道并将其分解为组成帧。接下来，在对帧进行预处理和分割后，从背景中提取出绿色植物。为了准确区分水稻和杂草，共识别出302个颜色、形状和纹理特征。两种元启发式算法，即粒子群优化（PSO）和蜜蜂算法（BA），分别用于优化神经网络以选择最有效的特征并对不同类型的杂草进行分类。将所提出的分类方法与K近邻（KNN）分类器进行比较，发现在测试集上，所提出的ANN-BA分类器在右通道和左通道上的准确率分别达到88.74%和87.96%。以算术平均值或几何平均值为基础，在测试集上准确率分别提高到92.02%和90.7%。另一方面，与所提出的ANN-BA分类器相比，KNN存在更多误分类情况，对右通道和左通道数据分类的总体准确率分别为76.62%和85.59%，对算术平均值和几何平均值分类的总体准确率分别为85.84%和84.07%。
