A convolutional Riemannian texture model with differential entropic active contours for unsupervised pest detection

Dai, Shuanglu; Man, Hong

doi:10.1109/icassp.2017.7952312

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…The final detected regions are obtained by maximizing pixel-wise posterior probabilities on the estimated distributions. Experimental results show that effective detections can be achieved by the proposed method on forestry pests imaging datasets [15]. K. Dimililer-2017 et.al, developed an intelligent insect classification system that would be capable of detecting and classifying the eight insects most commonly found in paddy fields.…”

Section: Introductionmentioning

confidence: 99%

Pest Detection in Crop using Video and Image Processing

Chodey¹

2020

IJRASET

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Detection of pests of various species is one of the compelling tasks in pest management involving detection and classification which is much more difficult than common object detection because of the apparent differences among pest species. Early diagnosis of pest is more essential to enhance the production of healthy crops as well as to diminish the economic loss. However, one major challenge is the difficulty in determining the physical, chemical, and biological changes in plants during the asymptomatic stages of an infection. To overcome these challenge, a huge count of traditional techniques were formulated. Many algorithms were introduced to detect the pests from leaves. Such techniques are not efficient for all the types of pest. To overcome the above said problem, this paper deals with pest detection, identification using segmentation and classification algorithms.

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Section: Introductionmentioning

confidence: 99%

Pest Detection in Crop using Video and Image Processing

Chodey¹

2020

IJRASET

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“…The incorporation k-means clustering methodology with image processing was used to segment the pests or any object from the image [13]. Dai and Man used a convolutional Riemannian texture with differential entropic active contours to distinguish the background regions and expose pest regions [14]. Zhao et al obtained accurate contour of crop diseases and insect pests for the following recognition, taking the use of texture difference and active contour guided by the texture difference [15].…”

Section: Introductionmentioning

confidence: 99%

“…The intelligent recognition of robot car for Pyralidae insects. (1) Deep grooved wheel, (2) shell, (3) guardrail, (4) screen display, (5) camera, (6) mechanical arm, (7) vertical thread screw, (8) screw guardrail, (9) solar panels, (10) sensor integrator, (11) horizontal screw motor, (12) trap lamp, (13) the hardcore,(14) crossbar,(15) insect collecting board,(16) vertical thread screw-driven motor,(17) chassis,(18) car control buttons,(19) horizontal thread screw, and (20) trap top cover.…”

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confidence: 99%

Agricultural Robot for Intelligent Detection of Pyralidae Insects

Hu¹,

Liu²,

Zhao³

2019

Agricultural Robots - Fundamentals and Applications

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The Pyralidae insects are one of the main pests in economic crops. However, the manual detection and identification of Pyralidae insects are labor intensive and inefficient, and subjective factors can influence recognition accuracy. To address these shortcomings, an insect monitoring robot and a new method to recognize the Pyralidae insects are presented in this chapter. Firstly, the robot gets images by performing a fixed action and detects whether there are Pyralidae insects in the images. The recognition method obtains the total probability image by using reverse mapping of histogram and multi-template images, and then image contour can be extracted quickly and accurately by using constraint Otsu. Finally, according to the Hu moment characters, perimeter, and area characters, the contours can be filtrated, and recognition results with triangle mark can be obtained. According to the recognition results, the speed of the robot car and mechanical arm can be adjusted adaptively. The theoretical analysis and experimental results show that the proposed scheme has high timeliness and high recognition accuracy in the natural planting scene.

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