• The Journal of Bigdata
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• v.1 no.1
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• pp.19-22
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• 2016

Many Studies have been actively conducted on the early diagnosis of the crop pest utilizing IT technology. The purpose of the paper is to discuss on the image processing method capable of detecting the crop leaf pest prematurely by analyzing the image of the leaf received from the camera sensor. This paper proposes an algorithm of diagnosing leaf infection by utilizing an improved K means clustering method. Leaf infection grouping test showed that the proposed algorithm illustrated a better performance in the qualitative evaluation.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.959-968
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• 2019

In the agricultural industry, wireless sensor network technology has being applied by utilizing various sensors and embedded systems. In particular, a lot of researches are being conducted to diagnose diseases of crops early by using sensor network. There are some difficulties on traditional research how to diagnose crop diseases is not practical for agriculture. This paper proposes the algorithm which enables to investigate and analyze the crop leaf image taken by image camera and detect the infected area within the image. We applied the enhanced k-means clustering method to the images captured at horticulture facility and categorized the areas in the image. Then we used the edge detection and edge tracking scheme to decide whether the extracted areas are located in inside of leaf or not. The performance was evaluated using the images capturing tomato leaves. The results of performance evaluation shows that the proposed algorithm outperforms the traditional algorithms in terms of classification capability.

• Journal of the Korea Society for Simulation
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• v.29 no.4
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• pp.1-7
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• 2020

Early detection and classification of crop diseases play significant role to help farmers to reduce disease spread and to increase agricultural productivity. Recently, many researchers have used deep learning techniques like convolutional neural network (CNN) classifier for crop disease inspection with dataset of crop leaf images (e.g., PlantVillage dataset). These researches present over 90% of classification accuracy for crop diseases, but they have ability to detect only the pre-trained diseases. This paper proposes an efficient disease inspection CNN model for new crops not used in the pre-trained model. First, we present a benchmark crop disease classifier (CDC) for the crops in PlantVillage dataset using VGG16. Then we build a modified crop disease classifier (mCDC) to inspect diseases for untrained crops. The performance evaluation results show that the proposed model outperforms the benchmark classifier.