A DNN-based semantic segmentation for detecting weed and crop

Jie, You; Liu, Wei; Lee, Joonwhoan

doi:10.1016/j.compag.2020.105750

Cited by 81 publications

(38 citation statements)

References 20 publications

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“…The proposed method enabled the weeds to be completely separated from the original image to obtain complete nutrients and increase yield. You et al [ 130 ] proposed a semantic segmentation method for weed crop detection based on deep neural networks (DNNs). Four additional components were integrated to improve the segmentation accuracy, which provided enhanced performance for weeds of arbitrary shape in a complex environment.…”

Section: Weed Detection and Identification Methods Based On Deep Learningmentioning

confidence: 99%

Review of Weed Detection Methods Based on Computer Vision

Chen

Zhao

et al. 2021

Sensors

171

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Weeds are one of the most important factors affecting agricultural production. The waste and pollution of farmland ecological environment caused by full-coverage chemical herbicide spraying are becoming increasingly evident. With the continuous improvement in the agricultural production level, accurately distinguishing crops from weeds and achieving precise spraying only for weeds are important. However, precise spraying depends on accurately identifying and locating weeds and crops. In recent years, some scholars have used various computer vision methods to achieve this purpose. This review elaborates the two aspects of using traditional image-processing methods and deep learning-based methods to solve weed detection problems. It provides an overview of various methods for weed detection in recent years, analyzes the advantages and disadvantages of existing methods, and introduces several related plant leaves, weed datasets, and weeding machinery. Lastly, the problems and difficulties of the existing weed detection methods are analyzed, and the development trend of future research is prospected.

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Section: Weed Detection and Identification Methods Based On Deep Learningmentioning

confidence: 99%

Review of Weed Detection Methods Based on Computer Vision

Chen

Zhao

et al. 2021

Sensors

171

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“…On the other hand, DenseNet alleviates the vanishing-gradient problem, strengthens feature propagation, encourages feature reuse, and when combined with a deep convolutional network better refines key features [30]. This has many manifestations in segmentation and detection tasks [14].…”

Section: B Deep Learning Techniquesmentioning

confidence: 99%

“…According to the Universal Approximation Theorem, neural networks can approximate various non-linear functions; accordingly, a dense network structure with five input bands called UFAB was placed on the front-end of the ResNet18 classifier. The role of UFAB implemented by dense network blocks is to non-linearly combine 5 bands in adaptive way by training [14].…”

Section: Network Structure Of Patch Classifiermentioning

confidence: 99%

“…ResNet18 is used for a backbone of the classifier. In addition, the front-end DenseNet-based universal function approximation block (UFAB) is integrated [14] with ResNet18 to enhance pool of multispectral bands. The patch-based classifier is retrained with the hard negative examples to get a more elaborated decision boundary, which is finally used to build the heat map [15] to visualize the approximate distribution of diseased trees.…”

Section: Introductionmentioning

confidence: 99%

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Detecting Pine Trees Damaged by Wilt Disease Using Deep Learning Techniques Applied to Multi-Spectral Images

2022

Self Cite

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Pine wilt disease (PWD) is responsible for significant damage to East Asia's pine forests, including those in Korea, Japan, and China. Preventing the spread of wilt disease requires early detection and removal of damaged trees. This paper proposes a method of detecting disease-damaged pines using ortho-images corrected from 5-band multi-spectral images captured by unmanned aviation vehicles. The proposed method relies on a ResNet18 backbone network connected to a modified DenseNet module, classifies the 5-band multispectral (RGB, NIR, Red_Edge) ortho-image patches, and visualizes the results as a heat map. The patch-based classifier was retrained with hard negative examples, after which it achieved 98.66% accuracy, an improvement over the 96.0% accuracy associated with the same method applied to RGB images. The resulting heat map reflects the approximate distribution, and movement of the disease. Disease locations are also predicted by local maximums in the heat map. When the distance between a ground truth and the predicted location is less than visible distance, e.g. about 5m, it is counted as a correct detection. The proposed detection which consists of heat map generation followed by localization achieves Recall of 93.39%, Precision of 88.26%, and F1-score of 90.75%.

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“…The presented method allowed the weeds to be fully identified from the exact input in order to obtain nutrients from the crops. In paper [9], authors presented advanced methodology for segmentation of weed and leaves based on DNNs. It has been observed that the identification accuracy is better as compared to manually extraction of features under traditional ML algorithms.…”

Section: Literature Surveymentioning

confidence: 99%

Development of a Novel Classification technique for detection of Cowpea Leaves using VGG16 Deep Convolutional Network

Choudhary

Guha

Tripathi

et al. 2021

Preprint

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This paper is based on CNN-VGG16 network architecture in order to identify the cowpea plants from weed in the cowpea fields. It is done to increase the productivity of the crops. The VGG16 is a basic network of Convolutional Neural Network family and it has quite good classification performance. Implement and modification is quite easy with this model. Here, we have applied pre-trained VGG16 method to the cowpea as well as weed leaves custom dataset. Images of leaves have been taken from standard research farm, ICAR-NBPGR, Pusa campus, New Delhi. The dataset includes 1230 images of two classes. The basic principle of VGG16 network for automatic detection includes, feature extraction from input image, addition of convo layer, pooling layer addition, flatten (fully connected layer), and sigmoid for classifiers. Results from experiments show that the model efficiently classifies the cowpea and weed images. Here, accuracy rate on the training dataset is 97.58%, and that of test dataset set is 90.08%. Such results give hope for achieving agricultural reforms.

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A DNN-based semantic segmentation for detecting weed and crop

Cited by 81 publications

References 20 publications

Review of Weed Detection Methods Based on Computer Vision

Review of Weed Detection Methods Based on Computer Vision

Detecting Pine Trees Damaged by Wilt Disease Using Deep Learning Techniques Applied to Multi-Spectral Images

Development of a Novel Classification technique for detection of Cowpea Leaves using VGG16 Deep Convolutional Network

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