Deep recursive super resolution network with Laplacian Pyramid for better agricultural pest surveillance and detection

Yue, Yi; Cheng, Xi; Zhang, Di; Wu, Yunzhi; Zhao, Yang; Chen, Yiqiong; Fan, Guohua; Zhang, Youhua

doi:10.1016/j.compag.2018.04.004

Cited by 46 publications

(23 citation statements)

References 28 publications

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“…The system was capable of distinguishing red turpentine beetles from five other beetle species. Yue et al [64] focused their study on the development of an effective algorithm for increasing the resolution of images captured in the field. The proposed strategy employed a deep recursive super resolution network with Laplacian Pyramid for high resolution reconstruction of the images.…”

Section: Pest Detection Methodsmentioning

confidence: 99%

Detecting and Classifying Pests in Crops Using Proximal Images and Machine Learning: A Review

Barbedo

2020

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Pest management is among the most important activities in a farm. Monitoring all different species visually may not be effective, especially in large properties. Accordingly, considerable research effort has been spent towards the development of effective ways to remotely monitor potential infestations. A growing number of solutions combine proximal digital images with machine learning techniques, but since species and conditions associated to each study vary considerably, it is difficult to draw a realistic picture of the actual state of the art on the subject. In this context, the objectives of this article are (1) to briefly describe some of the most relevant investigations on the subject of automatic pest detection using proximal digital images and machine learning; (2) to provide a unified overview of the research carried out so far, with special emphasis to research gaps that still linger; (3) to propose some possible targets for future research.

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Section: Pest Detection Methodsmentioning

confidence: 99%

Detecting and Classifying Pests in Crops Using Proximal Images and Machine Learning: A Review

Barbedo

2020

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“…What's more, we also pretrained our discriminative model using the trained VGG19 model to supply an initialization when training our PSRGAN to avoid undesired local optima. Figure 11 and Figure 12 respectively show the visual comparison between our proposed method and other state-ofthe-art PSNR-oriented methods including SRdenseNet [31], DSRNLP [7], VDSR [32], SESR [33], LapSRN [34] and PSNR and SSIM are also provided for reference. It could be observed from Figure 11 and Figure 12 that our proposed PSRGAN outperforms the previous state-of-the-art PSNRoriented methods in details and sharpness.…”

Section: Training Detailsmentioning

confidence: 99%

“…Cheng et al [6] used a fine-tuning method to classify and identify a 10-classes pest dataset with deep convolutional neural networks (DCNNs), which achieved satisfactory recognition results. Yue et al [7] proposed a super-resolution method for agricultural pest image restoration and detection and also gained a high detection result. These previous works demonstrate the feasibility and effectiveness of applying deep learning in the field of pest identification.…”

Section: Introductionmentioning

confidence: 99%

Agricultural Pest Super-Resolution and Identification With Attention Enhanced Residual and Dense Fusion Generative and Adversarial Network

et al. 2020

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The growth of the most significant field crops such as rice, wheat, maize, and soybean are influenced because of various pests. And crop production is decreased due to various categories of insects. Deep learning technologies significantly increased the efficiency of identifying and controlling agricultural pests attack. However, agricultural pests images obtained are often obscure and unclear because of the sparse density of cameras deployed in the real farmland. This always makes pests difficult to recognize and monitor. Additionally, the existing classification and segmentation methods are not satisfying for the identification of low-resolution images because they are pre-trained on the clear and high-resolution datasets. Therefore, it is crucial to restore and upscale the obtained low-resolution pest images in order to improve classification accuracy and the recall rate of the instance segmentation. In this paper, we propose a generative adversarial network (GAN) with quadra-attention and residual and dense fusion mechanisms to transform low-resolution pest images. Compared with previous state-of-the-art PSNR-oriented superresolution methods, our proposed method is more powerful in image reconstruction and achieves the state of the art performance. The experiment results show that after reconstructing with our proposed gan, the recall rate increased by 182.89% and classification accuracy also improved a lot. Besides, our proposed method could decrease the density of the camera layout in the agricultural Internet of Things (IOT) monitor systems and the cost of infrastructure, which is practical for real-world applications.

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“…Yue et al [2] developed a super-resolution method for agricultural pests disease restoration and detection. A plant disease diagnosis system was proposed by Kawasaki et al [3] to identify two leaf diseases in cucumber plants by using a CNN.…”

Section: Introductionmentioning

confidence: 99%

Crop Leaf Disease Image Super-Resolution and Identification With Dual Attention and Topology Fusion Generative Adversarial Network

et al. 2020

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For agricultural disease image identification, obtained images are typically unclear, which can lead to poor identification results in real production environments. The quality of an image has a significant impact on the identification accuracy of pre-trained image classifiers. To address this problem, we propose a generative adversarial network with dual-attention and topology-fusion mechanisms called DATFGAN. This network can effectively transform unclear images into clear and high-resolution images. Additionally, the weight sharing scheme in our proposed network can significantly reduce the number of parameters. Experimental results demonstrate that DATFGAN yields more visually pleasing results than state-of-the-art methods. Additionally, treated images are evaluated based on identification tasks. The results demonstrate that the proposed method significantly outperforms other methods and is sufficiently robust for practical use.

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Deep recursive super resolution network with Laplacian Pyramid for better agricultural pest surveillance and detection

Cited by 46 publications

References 28 publications

Detecting and Classifying Pests in Crops Using Proximal Images and Machine Learning: A Review

Detecting and Classifying Pests in Crops Using Proximal Images and Machine Learning: A Review

Agricultural Pest Super-Resolution and Identification With Attention Enhanced Residual and Dense Fusion Generative and Adversarial Network

Crop Leaf Disease Image Super-Resolution and Identification With Dual Attention and Topology Fusion Generative Adversarial Network

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