Detection of soybean aphids in a greenhouse using an image processing technique

Maharlooei, Mohammadmehdi; Sivarajan, Saravanan; Bajwa, Sreekala G.; Harmon, Jason P.; Nowatzki, John

doi:10.1016/j.compag.2016.11.019

Cited by 58 publications

(32 citation statements)

References 17 publications

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“…This straightforward method gives a performance baseline of our segmentation task. The second method (referenced as method 2 in following text) is a 2017 research [9] on segmentation of aphids. This method achieved high performance counting aphids on upper surface of soybean leaves.…”

Section: Evaluation On Annotated Test Datasetmentioning

confidence: 99%

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Automatic Segmentation and Counting of Aphid Nymphs on Leaves Using Convolutional Neural Networks

et al. 2018

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The presence of pests is one of the main problems in crop production, and obtaining reliable statistics of pest infestation is essential for pest management. Detection of pests should be automated because human monitoring of pests is time-consuming and error-prone. Aphids are among the most destructive pests in greenhouses and they reproduce quickly. Automatic detection of aphid nymphs on leaves (especially on the lower surface) using image analysis is a challenging problem due to color similarity and complicated background. In this study, we propose a method for segmentation and counting of aphid nymphs on leaves using convolutional neural networks. Digital images of pakchoi leaves at different aphid infestation stages were obtained, and corresponding pixel-level binary mask annotated. In the test, segmentation results by the proposed method achieved high overlap with annotation by human experts (Dice coefficient of 0.8207). Automatic counting based on segmentation showed high precision (0.9563) and recall (0.9650). The correlation between aphid nymph count by the proposed method and manual counting was high (R2 = 0.99). The proposed method is generic and can be applied for other species of pests.

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Section: Evaluation On Annotated Test Datasetmentioning

confidence: 99%

“…Barbedo [8] regarded areas in specific eccentricity range as aphid nymphs. Maharlooei and colleagues [9] utilized size filtering to remove non-aphid objects in their segmentation results. These strategies achieved low misclassification rate, but manual correction was involved in some cases.…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation and Counting of Aphid Nymphs on Leaves Using Convolutional Neural Networks

et al. 2018

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“…There are a few studies dedicated to pest detection in stored products [20][21][22][23][24], but those are not considered here. Also, although the majority of methods for pest monitoring using RGB images employs some kind of machine learning algorithm, there are a few studies that use only image processing techniques such as mathematical morphology [25,26], thresholding [27][28][29] and template matching [30]. While those methods fall a little outside the scope of this article, they are addressed in the text whenever deemed relevant.…”

Section: Introductionmentioning

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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“…Hence, the generalization ability for different species of insects is limited. Secondly, most of current researches use insect images collected in an ideal lab environment without implementation in the wild [ 5 , 8 , 9 , 10 , 18 , 19 , 22 ]. Although a small number of studies [ 7 ] use insect images collected in the wild, these images at high resolution must be transmitted to a server where the counting and identification task is completed.…”

Section: Introductionmentioning

confidence: 99%

A Vision-Based Counting and Recognition System for Flying Insects in Intelligent Agriculture

Zhong

Gao

Lei

et al. 2018

Sensors

170

105

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Rapid and accurate counting and recognition of flying insects are of great importance, especially for pest control. Traditional manual identification and counting of flying insects is labor intensive and inefficient. In this study, a vision-based counting and classification system for flying insects is designed and implemented. The system is constructed as follows: firstly, a yellow sticky trap is installed in the surveillance area to trap flying insects and a camera is set up to collect real-time images. Then the detection and coarse counting method based on You Only Look Once (YOLO) object detection, the classification method and fine counting based on Support Vector Machines (SVM) using global features are designed. Finally, the insect counting and recognition system is implemented on Raspberry PI. Six species of flying insects including bee, fly, mosquito, moth, chafer and fruit fly are selected to assess the effectiveness of the system. Compared with the conventional methods, the test results show promising performance. The average counting accuracy is 92.50% and average classifying accuracy is 90.18% on Raspberry PI. The proposed system is easy-to-use and provides efficient and accurate recognition data, therefore, it can be used for intelligent agriculture applications.

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Detection of soybean aphids in a greenhouse using an image processing technique

Cited by 58 publications

References 17 publications

Automatic Segmentation and Counting of Aphid Nymphs on Leaves Using Convolutional Neural Networks

Automatic Segmentation and Counting of Aphid Nymphs on Leaves Using Convolutional Neural Networks

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

A Vision-Based Counting and Recognition System for Flying Insects in Intelligent Agriculture

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