Transfer Learning-Based Framework for Classification of Pest in Tomato Plants

Pattnaik, Gayatri; Shrivastava, Vimal K.; Parvathi, K.

doi:10.1080/08839514.2020.1792034

Cited by 54 publications

(24 citation statements)

References 24 publications

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“…The two-channel residual attention network model (B-ARNet) was used to identify the images with an accuracy of about 89%. Pattnaik et al [ 39 ] proposed a pre-trained deep CNN framework for transfer learning for pest classification in tomato plants, and achieved the highest classification accuracy of 88.83% using DenseNet 169 model. However, in actual production, tomato may suffer from a variety of diseases or pests at the same time.…”

Section: Introductionmentioning

confidence: 99%

Early real-time detection algorithm of tomato diseases and pests in the natural environment

Wang

Zhu³

2021

Plant Methods

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Background Research on early object detection methods of crop diseases and pests in the natural environment has been an important research direction in the fields of computer vision, complex image processing and machine learning. Because of the complexity of the early images of tomato diseases and pests in the natural environment, the traditional methods can not achieve real-time and accurate detection. Results Aiming at the complex background of early period of tomato diseases and pests image objects in the natural environment, an improved object detection algorithm based on YOLOv3 for early real-time detection of tomato diseases and pests was proposed. Firstly, aiming at the complex background of tomato diseases and pests images under natural conditions, dilated convolution layer is used to replace convolution layer in backbone network to maintain high resolution and receptive field and improve the ability of small object detection. Secondly, in the detection network, according to the size of candidate box intersection ratio (IOU) and linear attenuation confidence score predicted by multiple grids, the obscured objects of tomato diseases and pests are retained, and the detection problem of mutual obscure objects of tomato diseases and pests is solved. Thirdly, to reduce the model volume and reduce the model parameters, the network is lightweight by using the idea of convolution factorization. Finally, by introducing a balance factor, the small object weight in the loss function is optimized. The test results of nine common tomato diseases and pests under six different background conditions are statistically analyzed. The proposed method has a F1 value of 94.77%, an AP value of 91.81%, a false detection rate of only 2.1%, and a detection time of only 55 Ms. The test results show that the method is suitable for early detection of tomato diseases and pests using large-scale video images collected by the agricultural Internet of Things. Conclusions At present, most of the object detection of diseases and pests based on computer vision needs to be carried out in a specific environment (such as picking the leaves of diseases and pests and placing them in the environment with light supplement equipment, so as to achieve the best environment). For the images taken by the Internet of things monitoring camera in the field, due to various factors such as light intensity, weather change, etc., the images are very different, the existing methods cannot work reliably. The proposed method has been applied to the actual tomato production scenarios, showing good detection performance. The experimental results show that the method in this study improves the detection effect of small objects and leaves occlusion, and the recognition effect under different background conditions is better than the existing object detection algorithms. The results show that the method is feasible to detect tomato diseases and pests in the natural environment.

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Section: Introductionmentioning

confidence: 99%

Early real-time detection algorithm of tomato diseases and pests in the natural environment

Wang

Zhu³

2021

Plant Methods

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“…The recognition of citrus flies is different from the other recognition tasks of crop pests [54,[61][62][63], e.g., the recognition of paddy pests [62] and tomato pests [63]. In those recognition tasks, most species of pests are from different families, and their appearances represent large variances.…”

Section: Discussion On Recognition Of Citrus Fliesmentioning

confidence: 99%

A Motor-Driven and Computer Vision-Based Intelligent E-Trap for Monitoring Citrus Flies

et al. 2021

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Citrus flies are important quarantine pests in citrus plantations. Electronic traps (e-traps) based on computer vision are the most popular types of equipment for monitoring them. However, most current e-traps are inefficient and unreliable due to requiring manual operations and lack of reliable detection and identification algorithms of citrus fly images. To address these problems, this paper presents a monitoring scheme based on automatic e-traps and novel recognition algorithms. In this scheme, the prototype of an automatic motor-driven e-trap is firstly designed based on a yellow sticky trap. A motor autocontrol algorithm based on Local Binary Pattern (LBP) image analysis is proposed to automatically replace attractants in the e-trap for long-acting work. Furthermore, for efficient and reliable statistics of captured citrus flies, based on the differences between two successive sampling images of the e-trap, a simple and effective detection algorithm is presented to continuously detect the newly captured citrus flies from the collected images of the e-trap. Moreover, a Multi-Attention and Multi-Part convolutional neural Network (MAMPNet) is proposed to exploit discriminative local features of citrus fly images to recognize the citrus flies in the images. Finally, extensive simulation experiments validate the feasibility and efficiency of the designed e-trap prototype and its autocontrol algorithm, as well as the reliability and effectiveness of the proposed detection and recognition algorithms for citrus flies.

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“…Pattnaik et al [34] suggested a transfer learning system for the classification of pests in tomato plants based on an existing deep CNN structure. The database for the analysis was obtained from 859 images in 10 groups from online sources.…”

Section: Related Workmentioning

confidence: 99%

Image-based Onion Disease (Purple Blotch) Detection using Deep Convolutional Neural Network

Zaki¹,

Narejo²,

Ahsan³

et al. 2021

IJACSA

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Agriculture on earth is the biggest need for human sustenance. Over years, many farming methods and components have become computerized to guarantee quicker production with higher quality. Because of the enlarged demand in the farming industry, agricultural produce must be cultivated using an efficient process. Onion (Allium cepa L.) is an economically valuable crop and is the second-largest vegetable crop in the world. The spread of various diseases highly affected the production of the onion crop. One of the serious and most common diseases of onion worldwide is purple blotch. To compensate for a limited amount of training dataset of healthy and infected onion crops, the proposed method employs a pretrained enhanced InceptionV3 model. The proposed model detects onion disease (purple blotch) from images by recognizing the abnormalities caused by the disease. The suggested approach achieves a classification accuracy of 85.47% in recognizing the disease. This research investigates a novel approach for the rapid and accurate diagnosis of plant/crop diseases, laying the theoretical foundation for the use of deep learning in agricultural information.

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Transfer Learning-Based Framework for Classification of Pest in Tomato Plants

Cited by 54 publications

References 24 publications

Early real-time detection algorithm of tomato diseases and pests in the natural environment

Early real-time detection algorithm of tomato diseases and pests in the natural environment

A Motor-Driven and Computer Vision-Based Intelligent E-Trap for Monitoring Citrus Flies

Image-based Onion Disease (Purple Blotch) Detection using Deep Convolutional Neural Network

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