A contextualized approach for segmentation of foliage in different crop species

Rico-Fernández, M. P.; Rios‐Cabrera, Reyes; Castelán, Mario; Guerrero-Reyes, H.-I.; Juárez‐Maldonado, Antonio

doi:10.1016/j.compag.2018.11.033

Cited by 45 publications

(20 citation statements)

References 20 publications

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“…In this section, we introduced the advantages of visual perception in agricultural intelligence, and introduced agricultural robots, crop pest and disease monitoring in detail which are more representative in the application of visual perception technology. In addition to these two parts, the application of machine vision in agricultural intelligence also includes agricultural product quality inspection [62], crop healthy growth monitoring [63], agricultural vehicle visual navigation [64], and UAV farmland information monitoring [65], which are shown in Fig. 3.…”

Section: B Intelligent Agricultural Productionmentioning

confidence: 99%

Visual Perception Enabled Industry Intelligence: State of the Art, Challenges and Prospects

Yang

Wang

Jiang

et al. 2021

IEEE Trans. Ind. Inf.

184

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Visual perception refers to the process of organizing, identifying and interpreting visual information in environmental awareness and understanding. With the rapid progress of multimedia acquisition technology, research on visual perception has been a hot topic in the academical field and industrial applications. Especially after the introduction of artificial intelligence theory, intelligent visual perception has been widely used to promote the development of industrial production towards intelligence. In this paper, we review the previous research and application of visual perception in different industrial fields such as product surface defect detection, intelligent agricultural production, intelligent driving, image synthesis and event reconstruction. The applications basically cover most of the intelligent visual perception processing technologies. Through this survey, it will provide a comprehensive reference for research on this direction. Finally, this paper also summarizes the current challenges of visual perception and predicts its future development trends.

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Section: B Intelligent Agricultural Productionmentioning

confidence: 99%

Visual Perception Enabled Industry Intelligence: State of the Art, Challenges and Prospects

Yang

Wang

Jiang

et al. 2021

IEEE Trans. Ind. Inf.

184

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“…• CIE LUV color space and Support Vector Machines (LUV_SVM) [37]: They propose a method which uses central pixel neighborhood information to classify a pixel through support vector machines (SVM). The underlying idea is to include contextualized information on the learning system.…”

Section: Crop Segmentation Algorithms a Crop Segmentation Algorimentioning

confidence: 99%

“…DA [33] belongs to the neural network model algorithm. The LUV_SVM [37] algorithm belongs to the SVM model algorithm. In addition to the common color indice and learning algorithms, some people use the wavelet method for crop image segmentation, such as the DWT [34] algorithm.…”

Section: ) Pixel-based Algorithmsmentioning

confidence: 99%

Performance Evaluation of Crop Segmentation Algorithms

Huang

Chen

et al. 2020

IEEE Access

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This paper presents a thorough evaluation of twenty-one state-of-the-art widely-used crop segmentation algorithms, motived by their significance in vision tasks for further analysis. An ideal crop segmentation algorithm can effectively extract crop information, thus providing an important precondition for the application of intelligent agriculture analytics. In order to enable researchers in this field to fully understand various crop segmentation methods, this paper proposes a new classification strategy of object segmentation by dividing the algorithms into pixel-based and region-based approaches at first, and then systematically evaluating various crop segmentation methods with a unified data benchmark and four common metrics. A new dataset which incorporates crop variety, environment condition and observation distance into consideration is constructed for demonstrating the experiments and comparisons. The effectiveness and robustness of these algorithms were evaluated by three sets of comparative experiments. Based on the quantitative results, we summarize the advantages and disadvantages of the evaluated algorithms from the segmentation performances with four metric indicators. Furthermore, the discussion and evaluation results will provide great support for precision agriculture analysis.

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“…PSO is an evolutionary computation technique proposed by Kennedy and Eberhart in 1995 to simulate the foraging behaviour of birds [12, 13]. The classical PSO algorithm uses one particle to represent each solution in the solution space.…”

Section: Improved Particle Swarm Optimisationmentioning

confidence: 99%