• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권4호
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• pp.959-979
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• 2024

Rice pest identification is essential in modern agriculture for the health of rice crops. As global rice consumption rises, yields and quality must be maintained. Various methodologies were employed to identify pests, encompassing sensor-based technologies, deep learning, and remote sensing models. Visual inspection by professionals and farmers remains essential, but integrating technology such as satellites, IoT-based sensors, and drones enhances efficiency and accuracy. A computer vision system processes images to detect pests automatically. It gives real-time data for proactive and targeted pest management. With this motive in mind, this research provides a novel farmland fertility algorithm with a deep learning-based automated rice pest detection and classification (FFADL-ARPDC) technique. The FFADL-ARPDC approach classifies rice pests from rice plant images. Before processing, FFADL-ARPDC removes noise and enhances contrast using bilateral filtering (BF). Additionally, rice crop images are processed using the NASNetLarge deep learning architecture to extract image features. The FFA is used for hyperparameter tweaking to optimise the model performance of the NASNetLarge, which aids in enhancing classification performance. Using an Elman recurrent neural network (ERNN), the model accurately categorises 14 types of pests. The FFADL-ARPDC approach is thoroughly evaluated using a benchmark dataset available in the public repository. With an accuracy of 97.58, the FFADL-ARPDC model exceeds existing pest detection methods.

• 생물환경조절학회지
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• 제31권3호
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• pp.255-260
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• 2022

본 논문은 딥러닝 알고리즘을 이용하여 딸기 영상 데이터의 병충해 존재 여부를 자동으로 검출할 수 있는 서비스 모델을 제안한다. 또한 병징에 특화된 분할 이미지 데이터 세트를 제안하여 딥러닝 모델의 병충해 검출 성능을 향상한다. 딥러닝 모델은 CNN 기반 YOLO를 선정하여 기존의 R-CNN 기반 모델의 느린 학습속도와 추론속도를 개선하였다. 병충해 검출 모델을 학습하기 위해 일반적인 데이터 세트와 제안하는 분할 이미지 데이터 세트를 구축하였다. 딥러닝 모델이 일반적인 학습 데이터 세트를 학습했을 때 병충해 검출률은 81.35%이며 병충해 검출 신뢰도는 73.35%이다. 반면 딥러닝 모델이 분할 이미지 학습 데이터 세트를 학습했을 때 병충해 검출률은 91.93%이며 병충해 검출 신뢰도는 83.41%이다. 따라서 분할 이미지 데이터를 학습한 딥러닝 모델의 성능이 우수하다는 것을 증명할 수 있었다.

• Journal of Microbiology and Biotechnology
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• 제20권1호
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• pp.193-201
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• 2010

A specific and rapid real-time PCR assay for detecting Ralstonia solanacearum in horticultural soil and plant tissues was developed in this study. The specific primers RSF/RSR were designed based on the upstream region of the UDP-3-O-acyl-GlcNAc deacetylase gene from R. solanacearum, and a PCR product of 159 bp was amplified specifically from 28 strains of R. solanacearum, which represent all genetically diverse AluI types and all 6 biovars, but not from any other nontarget species. The detection limit of $10^2\;CFU/g$ tomato stem and horticultural soil was achieved in this real-time PCR assay. The high sensitivity and specificity observed with field samples as well as with artificially infected samples suggested that this method might be a useful tool for detection and quantification of R. solanacearum in precise forecast and diagnosis.

• 한국멀티미디어학회논문지
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• 제23권1호
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• pp.50-57
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• 2020

The work that detection and elimination to disease and pest have important in agricultural field because it is directly related to the production of the crops, early detection and treatment of the disease insects. Image classification technology based on traditional computer vision have not been applied in part such as disease and pest because that is falling a accuracy to extraction and classification of feature. In this paper, we proposed model that determine to disease and pest of shiitake based on deep-CNN which have high image recognition performance than exist study. For performance evaluation, we compare evaluation with Alexnet to a proposed deep learning evaluation model. We were compared a proposed model with test data and extend test data. The result, we were confirmed that the proposed model had high performance than Alexnet which approximately 48% and 72% such as test data, approximately 62% and 81% such as extend test data.

• Nuclear Engineering and Technology
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• 제54권2호
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• pp.514-522
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• 2022

Non-destructive testing (NDT) technology is a widely used inspection method for agricultural products. Compared with the conventional inspection method, there is no extensive sample preparation for NDT technology, and the sample is not damaged. In particular, NDT technology is used to inspect the internal structure of agricultural products infested by pests. The introduction and spread of pests during the import and export process can cause significant damage to the agricultural environment. Until now, pest detection in agricultural products and quarantine processes have been challenging because they used external inspection methods. However, NDT technology is advantageous in these inspection situations. In this pilot study, we investigated the feasibility of X-ray computed tomography (X-ray CT) and magnetic resonance imaging (MRI) to identify pest infestation in agricultural products. Three kinds of artificially pest-infested fruits (mango, tangerine, and chestnut) were non-destructively inspected using X-ray CT and MRI. X-ray CT was able to identify all pest infestations in fruits, while MRI could not detect the pest-infested chestnut. In addition, X-ray CT was superior to the quarantine process than MRI based on the contrast-to-noise ratio (CNR), image acquisition time, and cost. Therefore, X-ray CT is more appropriate for the pest quarantine process of fruits than MRI.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2022년도 추계학술대회
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• pp.89-89
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• 2022

Soybean (Glycine max (L.) Merr.) is a representative food resource. To preserve the integrity of soybean, it is necessary to protect soybean yield and seed quality from threats of various pests and diseases. Riptortus pedestris is a well-known insect pest that causes the greatest loss of soybean yield in South Korea. This pest not only directly reduces yields but also causes disorders and diseases in plant growth. Unfortunately, no resistant soybean resources have been reported. Therefore, it is necessary to identify the distribution and movement of Riptortus pedestris at an early stage to reduce the damage caused by insect pests. Conventionally, the human eye has performed the diagnosis of agronomic traits related to pest outbreaks. However, due to human vision's subjectivity and impermanence, it is time-consuming, requires the assistance of specialists, and is labor-intensive. Therefore, the responses and behavior patterns of Riptortus pedestris to the scent of mixture R were visualized with a 3D model through the perspective of artificial intelligence. The movement patterns of Riptortus pedestris was analyzed by using time-series image data. In addition, classification was performed through visual analysis based on a deep learning model. In the object tracking, implemented using the YOLO series model, the path of the movement of pests shows a negative reaction to a mixture Rina video scene. As a result of 3D modeling using the x, y, and z-axis of the tracked objects, 80% of the subjects showed behavioral patterns consistent with the treatment of mixture R. In addition, these studies are being conducted in the soybean field and it will be possible to preserve the yield of soybeans through the application of a pest control platform to the early stage of soybeans.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2021년도 추계학술대회
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• pp.115-115
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• 2021

• 한국작물학회:학술대회논문집
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• 한국작물학회 2022년도 춘계학술대회
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• pp.22-22
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• 2022

• 한국빅데이터학회지
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• 제1권1호
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• pp.19-22
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• 2016

최근 IT 기술을 활용하여 농작물의 병충해 조기 진단에 관한 연구가 활발히 진행되고 있다. 본 논문은 카메라 센서를 통해 받아온 작물의 잎사귀 이미지를 분석하여 병충해를 조기에 감지할 수 있는 이미지 프로세싱 기법에 대해 논한다. 본 논문은 개선된 K 평균 클러스터링 방법을 활용하여 잎사귀 질병 감염 여부를 진단하는 알고리즘을 제안한다. 잎사귀 감염 분류 실험을 통해, 제안한 알고리즘이 정성적인 평가에서 더 좋은 성능을 나타낸 것으로 분석되었다.

• 보존과학연구
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• 통권21호
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• pp.5-55
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• 2000

The cultural properties are damaged by various causes according to the characteristics of material, the condition of preservation, and the period of time. Especially, biodeterioration makes lots of damages in organic properties than inorganic ones. The damages of wooden cultural properties by insects usually are caused by the three orders; Isoptera, Coleoptera, and Hymenoptera. As the result of investigation on the state of 141 buildings of wooden cultural properties in 1999, some of them were damaged by many kinds off actors; wasp, powder post beetle, cigarette beetle, termite, decay, and physical cracking. And it was found that the patterns of damages were related to species-specific habits of insects. There are several methods of pest control for the prevention of wooden cultural properties from damages caused by insects. Those are as follows; physical control, chemical control, biological control, and integrated pest management. When insects and fungi were detected at the wooden buildings, the fumigation is best treatment to stop biodeterioration. And then, wood materials also need to be treated with insecticidal and antiseptic chemicals to avoid a reinfestation, because the fumigant is volatile. The six commercial chemicals which are applied to the insecticidal and antiseptic treatment of wooden cultural properties were purchased to test their abilities. According to the comparative results of efficacy of them in laboratory, chemical D showed excellent efficacy in all items, including antiseptic and termiticidal items. The goal of these pest controls is to protect wooden buildings from insects and microorganisms. The most effective method used currently is chemical control(fumigation, insecticidal and anticeptic chemical treatment), but it has to be treated periodically to control pest effectively. Recently environmentally-friendly control methods such as bait system or biological treatments are replacing traditional barrier treatments using large amounts of chemicals. Especially, termite is a social insect which makes a colony. Although a building with fumigation treatment is safe for a while, once attacked building has a risk of damage by reinfestation of termite. Therefore, to control termites from damaged building, the entire colony including reproductives(queen and king) and larvae around buildings must beeliminated. Bait system can be used as a preventive measure in early detection of them through termites colony monitoring and baiting. It would be the most effective for termite control if bait system would be used together with the chemical controls.