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조완현(Wanhyun Cho),김상균(Sangkyoon Kim),나명환(MyungHwan Na),김덕현(Deok Hyeon Kim) 한국자료분석학회 2020 Journal of the Korean Data Analysis Society Vol.22 No.1
In this paper, we try to develop a growth management system that can maximize the harvest by examining frequently the growing status of fruit trees using statistical regression model and image processing technique. First, we segment the background, stem and strawberry body from a strawberry image. Second, we calculate the height and width of the strawberry by counting number of pixels from the grid given in the background. Third, a multiple regression model is used to derive a predictive function that can predict its weight from the given height and width of strawberries. Fourth, the accuracy of the prediction is verified by comparing the actual value with the predicted value of the weight of the strawberry using the verification data. Fifth, we conducted experiments using verification images to verify the performance of the constructed prediction model. From the experimental results, the value of the coefficient of determination for the constructed model is 0.98771, which is very high value. Finally, based on the algorithms derived so far, we have developed a platform that can actually be used to manage strawberry growth in farms.
딥러닝 알고리즘을 이용한 토마토에서 발생하는 여러가지 병해충의 탐지와 식별에 대한 웹응용 플렛폼의 구축
나명환 ( Na¸ Myung Hwan ),조완현 ( Cho¸ Wanhyun ),김상균 ( Kim¸ Sangkyoon ) 한국품질경영학회 2020 품질경영학회지 Vol.48 No.4
Purpose: purpose of this study was to propose the web application platform which can be to detect and discriminate various diseases and pest of tomato plant based on the large amount of disease image data observed in the facility or the open field. Methods: The deep learning algorithms uesed at the web applivation platform are consisted as the combining form of Faster R-CNN with the pre-trained convolution neural network (CNN) models such as SSD_mobilenet v1, Inception v2, Resnet50 and Resnet101 models. To evaluate the superiority of the newly proposed web application platform, we collected 850 images of four diseases such as Bacterial cankers, Late blight, Leaf miners, and Powdery mildew that occur the most frequent in tomato plants. Of these, 750 were used to learn the algorithm, and the remaining 100 images were used to evaluate the algorithm. Results: From the experiments, the deep learning algorithm combining Faster R-CNN with SSD_mobilnet v1, Inception v2, Resnet50, and Restnet101 showed detection accuracy of 31.0%, 87.7%, 84.4%, and 90.8% respectively. Finally, we constructed a web application platform that can detect and discriminate various tomato deseases using best deep learning algorithm. If farmers uploaded image captured by their digital cameras such as smart phone camera or DSLR (Digital Single Lens Reflex) camera, then they can receive an information for detection, identification and disease control about captured tomato disease through the proposed web application platform. Conclusion: Incheon Port needs to act actively paying