상추잎 너비와 길이 예측을 위한 합성곱 신경망 모델 비교

서지정보

ㆍ발행기관 : 한국생물환경조절학회 ㆍ수록지정보 : 생물환경조절학회지(구 시설원예ㆍ식물공장) / 32권 / 4호
ㆍ저자명 : 송지수, 김동석, 김효성, 정은지, 황현정, 박재성

목차

서 론
재료 및 방법
1. 하드웨어 및 소프트웨어
2. 데이터 세트
3. 영상 전처리
4. K겹 교차검증
5. 모델 아키텍처 선택
6. 성능지표
7. Keras.callbacks
8. 학습 모델
결과 및 고찰
1. 콜백 함수
2. 아키텍처 비교
3. 손실함수 및 활성화함수 비교
4. 옵티마이저 비교
결 론
적 요
사 사

영어 초록

Determining the size or area of a plant's leaves is an important factor in predicting plant growth and improving the productivity of indoor farms. In this study, we developed a convolutional neural network (CNN)-based model to accurately predict the length and width of lettuce leaves using photographs of the leaves. A callback function was applied to overcome data limitations and overfitting problems, and K-fold cross-validation was used to improve the generalization ability of the model. In addition, ImageDataGenerator function was used to increase the diversity of training data through data augmentation. To compare model performance, we evaluated pre-trained models such as VGG16, Resnet152, and NASNetMobile. As a result, NASNetMobile showed the highest performance, especially in width prediction, with an R_squared value of 0.9436, and RMSE of 0.5659. In length prediction, the R_squared value was 0.9537, and RMSE of 0.8713. The optimized model adopted the NASNetMobile architecture, the RMSprop optimization tool, the MSE loss functions, and the ELU activation functions. The training time of the model averaged 73 minutes per Epoch, and it took the model an average of 0.29 seconds to process a single lettuce leaf photo. In this study, we developed a CNN-based model to predict the leaf length and leaf width of plants in indoor farms, which is expected to enable rapid and accurate assessment of plant growth status by simply taking images. It is also expected to contribute to increasing the productivity and resource efficiency of farms by taking appropriate agricultural measures such as adjusting nutrient solution in real time.

참고 자료

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