Comparison of Convolutional Neural Network (CNN) Models for Lettuce Leaf Width and Length Prediction

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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.

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