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Article

Comparison of Image Preprocessing Strategies for Convolutional Neural Network-Based Growth Stage Classification of Butterhead Lettuce in Industrial Plant Factories

1
Department of Biosystems Engineering, Seoul National University, Seoul 08826, Republic of Korea
2
Integrated Major in Global Smart Farm, Seoul National University, Seoul 08826, Republic of Korea
3
Institute of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Republic of Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(11), 6278; https://doi.org/10.3390/app15116278
Submission received: 3 April 2025 / Revised: 26 May 2025 / Accepted: 31 May 2025 / Published: 3 June 2025
(This article belongs to the Special Issue Applications of Image Processing Technology in Agriculture)

Abstract

The increasing need for scalable and efficient crop monitoring systems in industrial plant factories calls for image-based deep learning models that are both accurate and robust to domain variability. This study investigates the feasibility of CNN-based growth stage classification of butterhead lettuce (Lactuca sativa L.) using two data types: raw images and images processed through GrabCut–Watershed segmentation. A ResNet50-based transfer learning model was trained and evaluated on each dataset, and cross-domain performance was assessed to understand generalization capability. Models trained and tested within the same domain achieved high accuracy (Model 1: 99.65%; Model 2: 97.75%). However, cross-domain evaluations revealed asymmetric performance degradation—Model 1-CDE (trained on raw images, tested on preprocessed images) achieved 82.77% accuracy, while Model 2-CDE (trained on preprocessed images, tested on raw images) dropped to 34.15%. Although GrabCut–Watershed offered clearer visual inputs, it limited the model’s ability to generalize due to reduced contextual richness and oversimplification. In terms of inference efficiency, Model 2 recorded the fastest model-only inference time (0.037 s/image), but this excluded the segmentation step. In contrast, Model 1 achieved 0.055 s/image without any additional preprocessing, making it more viable for real-time deployment. Notably, Model 1-CDE combined the fastest inference speed (0.040 s/image) with stable cross-domain performance, while Model 2-CDE was both the slowest (0.053 s/image) and least accurate. Grad-CAM visualizations further confirmed that raw image-trained models consistently attended to meaningful plant structures, whereas segmentation-trained models often failed to localize correctly in cross-domain tests. These findings demonstrate that training with raw images yields more robust, generalizable, and deployable models. The study highlights the importance of domain consistency and preprocessing trade-offs in vision-based agricultural systems and lays the groundwork for lightweight, real-time AI applications in smart farming.
Keywords: commercialized plant factory; convolutional neural network; image preprocessing; transfer learning; deep learning; GrabCut–Watershed segmentation; cross-testing; inference time commercialized plant factory; convolutional neural network; image preprocessing; transfer learning; deep learning; GrabCut–Watershed segmentation; cross-testing; inference time

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MDPI and ACS Style

Kim, J.-S.G.; Chung, S.; Ko, M.; Song, J.; Shin, S.H. Comparison of Image Preprocessing Strategies for Convolutional Neural Network-Based Growth Stage Classification of Butterhead Lettuce in Industrial Plant Factories. Appl. Sci. 2025, 15, 6278. https://doi.org/10.3390/app15116278

AMA Style

Kim J-SG, Chung S, Ko M, Song J, Shin SH. Comparison of Image Preprocessing Strategies for Convolutional Neural Network-Based Growth Stage Classification of Butterhead Lettuce in Industrial Plant Factories. Applied Sciences. 2025; 15(11):6278. https://doi.org/10.3390/app15116278

Chicago/Turabian Style

Kim, Jung-Sun Gloria, Soo Chung, Myungjin Ko, Jihoon Song, and Soo Hyun Shin. 2025. "Comparison of Image Preprocessing Strategies for Convolutional Neural Network-Based Growth Stage Classification of Butterhead Lettuce in Industrial Plant Factories" Applied Sciences 15, no. 11: 6278. https://doi.org/10.3390/app15116278

APA Style

Kim, J.-S. G., Chung, S., Ko, M., Song, J., & Shin, S. H. (2025). Comparison of Image Preprocessing Strategies for Convolutional Neural Network-Based Growth Stage Classification of Butterhead Lettuce in Industrial Plant Factories. Applied Sciences, 15(11), 6278. https://doi.org/10.3390/app15116278

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