Image Semantic Segmentation Based on Convolutional Neural Networks for Monitoring Agricultural Vegetation

Ganchenko, Valentin; Doudkin, A. A.

doi:10.1007/978-3-030-35430-5_5

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…where a computer system imitates the visual cortex and brain to identify patterns from visual elements [ 27 ]. CNNs have been used in multiple fields, such as medical [ 28 , 29 , 30 , 31 ], autonomous vehicles [ 32 , 33 , 34 ], and agricultural [ 35 , 36 ], just to name a few. A CNN differs from a conventional neural network (NN) because it contains at least a convolutional layer.…”

Section: Methodsmentioning

confidence: 99%

Fuzzy Edge-Detection as a Preprocessing Layer in Deep Neural Networks for Guitar Classification

Torres

González

Martínez

2022

Sensors

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Deep neural networks have demonstrated the capability of solving classification problems using hierarchical models, and fuzzy image preprocessing has proven to be efficient in handling uncertainty found in images. This paper presents the combination of fuzzy image edge-detection and the usage of a convolutional neural network for a computer vision system to classify guitar types according to their body model. The focus of this investigation is to compare the effects of performing image-preprocessing techniques on raw data (non-normalized images) with different fuzzy edge-detection methods, specifically fuzzy Sobel, fuzzy Prewitt, and fuzzy morphological gradient, before feeding the images into a convolutional neural network to perform a classification task. We propose and compare two convolutional neural network architectures to solve the task. Fuzzy edge-detection techniques are compared against their classical counterparts (Sobel, Prewitt, and morphological gradient edge-detection) and with grayscale and color images in the RGB color space. The fuzzy preprocessing methodologies highlight the most essential features of each image, achieving favorable results when compared to the classical preprocessing methodologies and against a pre-trained model with both proposed models, as well as achieving a reduction in training times of more than 20% compared to RGB images.

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Section: Methodsmentioning

confidence: 99%

Fuzzy Edge-Detection as a Preprocessing Layer in Deep Neural Networks for Guitar Classification

Torres

González

Martínez

2022

Sensors

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“…Guang Wang, Fan Yu, and others used the segmentation models of farmland and woodland with the help of remote sensing images to segment the farmland and woodland [ 2 ]. Ganchenko et al used convolutional neural networks based on agricultural vegetation monitoring image semantic segmentation [ 3 ]. Drone-based remote sensing images are mainly used in specific scene analysis of a field, such as Yang’s [ 4 ] deep semantic segmentation techniques, Fully Convolutional Networks (FCN), SegNet, etc., which segment the farmland covered by plastic mulch to study environmental and soil contamination.…”

Section: Introductionmentioning

confidence: 99%

An Improved Encoder-Decoder Network Based on Strip Pool Method Applied to Segmentation of Farmland Vacancy Field

Zhang

Yang

et al. 2021

Entropy

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In the research of green vegetation coverage in the field of remote sensing image segmentation, crop planting area is often obtained by semantic segmentation of images taken from high altitude. This method can be used to obtain the rate of cultivated land in a region (such as a country), but it does not reflect the real situation of a particular farmland. Therefore, this paper takes low-altitude images of farmland to build a dataset. After comparing several mainstream semantic segmentation algorithms, a new method that is more suitable for farmland vacancy segmentation is proposed. Additionally, the Strip Pooling module (SPM) and the Mixed Pooling module (MPM), with strip pooling as their core, are designed and fused into the semantic segmentation network structure to better extract the vacancy features. Considering the high cost of manual data annotation, this paper uses an improved ResNet network as the backbone of signal transmission, and meanwhile uses data augmentation to improve the performance and robustness of the model. As a result, the accuracy of the proposed method in the test set is 95.6%, mIoU is 77.6%, and the error rate is 7%. Compared to the existing model, the mIoU value is improved by nearly 4%, reaching the level of practical application.

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“…The results presented in the paper is a part of our study of the usege of the HRNet architecture for semantic segmentation of aerial imagery based on SegNet and U-Net architectures [19]. An algorithm for digital color image processing of various spatial resolutions was obtained.…”

Section: Introductionmentioning

confidence: 99%

Image Semantic Segmentation Based on High-Resolution Networks for Monitoring Agricultural Vegetation

Ganchenko

Старовойтов

Zheng

2020

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

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In the article, recognition of state of agricultural vegetation from aerial photographs at various spatial resolutions was considered. Proposed approach is based on a semantic segmentation using convolutional neural networks. Two variants of High-Resolution network architecture (HRNet) are described and used. These neural networks were trained and applied to aerial images of agricultural fields. In our experiments, accuracy of four land classes recognition (soil, healthy vegetation, diseased vegetation and other objects) was about 93-94%.

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Image Semantic Segmentation Based on Convolutional Neural Networks for Monitoring Agricultural Vegetation

Cited by 6 publications

References 12 publications

Fuzzy Edge-Detection as a Preprocessing Layer in Deep Neural Networks for Guitar Classification

Fuzzy Edge-Detection as a Preprocessing Layer in Deep Neural Networks for Guitar Classification

An Improved Encoder-Decoder Network Based on Strip Pool Method Applied to Segmentation of Farmland Vacancy Field

Image Semantic Segmentation Based on High-Resolution Networks for Monitoring Agricultural Vegetation

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