• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.186-189
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• 2022

스마트팜 피노믹스 시스템은 재배하는 식물의 성장조건에 맞게 생육 환경을 일정하게 유지하고 관리하는 장치이지만, 그럼에도 불구하고 식물의 질병은 여러 가지 이유로 발생할 수 있다. 본 논문에서는 스마트팜 피노믹스 시스템에서 Mean Shift Segmentation 을 통한 식물의 질병을 자동으로 검출하는 식물 질병 검출 알고리즘을 제안한다. 식물의 질병 정도가 임의의 임계값을 넘을 경우, 해당 식물을 질병의 정도가 심한 식물로 판별하고, 적절한 수확시기를 결정하여 더 나은 상품성을 가진 식물을 재배할 수 있는 방법을 제시한다. 또한 식물의 질병이 급격하게 심해지는 기간을 확인하여 인간의 개입 없이 완전히 자동화된 시스템으로 더욱 세심하고 효율적인 식물 재배를 가능하게 함을 제시한다. 본 논문에서는 아이스버그(양상추)에 대한 재배 환경을 구축하여 생장 기간에 아이스버그에 발생하는 질병인 팁번 현상을 검출하는 실험을 진행하였다. 본 논문에서 제안한 방법은 다른 종류의 다양한 식물에서도 질병 검출이 가능하며, 스마트팜 피노믹스 시스템에서 질병 검출의 자동화를 위한 한 가지 방법으로 활용될 수 있을 것으로 기대된다.

• Korean Journal of Breeding Science
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• v.43 no.4
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• pp.233-240
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• 2011

Food security has been a main global issue due to climate changes and growing world population expected to 9 billion by 2050. While biodiversity is becoming more highlight, breeders are confronting shortage of various genetic materials needed for new variety to tackle food shortage challenge. Though biotechnology is still under debate on potential risk to human and environment, it is considered as one of alternative tools to address food supply issue for its potential to create a number of variations in genetic resource. The new technology, phenomics, is developing to improve efficiency of crop improvement. Phenomics is concerned with the measurement of phenomes which are the physical, morphological, physiological and/or biochemical traits of organisms as they change in response to genetic mutation and environmental influences. It can be served to provide better understanding of phenotypes at whole plant. For last decades, high-throughput screening (HTS) systems have been developed to measure phenomes, rapidly and quantitatively. Imaging technology such as thermal and chlorophyll fluorescence imaging systems is an area of HTS which has been used in agriculture. In this article, we review the current statues of high-throughput screening system in phenomics and its application for crop improvement.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.4
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• pp.1-8
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• 2019

Plant phenomics is a technique for observing and analyzing morphological features in order to select plant varieties of excellent traits. The conventional methods is difficult to apply to the phenomics system. because the color threshold value must be manually changed according to the detection target. In this paper, we propose the convolution neural network (CNN) structure that can automatically segment plants from the background for the phenomics system. The LeafNet consists of nine convolution layers and a sigmoid activation function for determining the presence of plants. As a result of the learning using the LeafNet, we obtained a precision of 98.0% and a recall rate of 90.3% for the plant seedlings images. This confirms the applicability of the phenomics system.

• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.923-935
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• 2022

This paper deals with detection and classification of leaf diseases for phenomics systems. As the smart farm systems of plants are increased, It is important to determine quickly the abnormal growth of plants without supervisors. This paper considers the color distribution and shape information of leaf diseases, and designs two deep leaning networks in training the leaf diseases. In the first step, color distribution of input image is analyzed for possible diseases. In the second step, the image is first partitioned into small segments using mean shift clustering, and the color information of each segment is inspected by the proposed Color Network. When a segment is determined as disease, the shape parameters of the segment are extracted and inspected by proposed Shape Network to classify the leaf disease types in the third step. According to the experiments with two types of diseases (frogeye/rust and tipburn) for apple leaves and iceberg, the leaf diseases are detected with 92.3% recall for a segment and with 99.3% recall for an input image where there are usually more than two disease segments. The proposed method is useful for detecting leaf diseases quickly in the smart farm environment, and is extendible to various types of new plants and leaf diseases without additional learning.