A simple implementation for a course project.
The topic is to predict the fresh weight, dry weight, and leaf area with EfficientNet B7 to advance production in controlled environments such as greenhouses and vertical farms.
The image dataset and ground truth download link is here: https://library.wur.nl/WebQuery/wurpubs/586469 Be advised, the RGB_277/309/322.png are missing, so are Depth_277/309/322.png.
To run this code, you need:
- The source code (project.py)
- The image dataset: Downloaded link: https://library.wur.nl/WebQuery/wurpubs/586469
- The trained model(Optional): Download link https://www.dropbox.com/scl/fi/pbqemgbq8zg1mn92ptn7x/best_model_weights-b6.pth?rlkey=2bdtbt4zerau9nanr1esxpvg4&dl=0
Reference:
Tan, M., & Le, Q. (2019, May). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning (pp. 6105-6114). PMLR.
Hemming, S., de Zwart, H. F., Elings, A., Bijlaard, M., van Marrewijk, B., & Petropoulou, A. (2021). 3rd Autonomous Greenhouse Challenge: Online Challenge Lettuce Images.
Lin, Z., Fu, R., Ren, G., Zhong, R., Ying, Y., & Lin, T. (2022). Automatic monitoring of lettuce fresh weight by multi-modal fusion based deep learning. Frontiers in Plant Science, 13, 980581.
Lauguico, S., Concepcion, R., Tobias, R. R., Alejandrino, J., De Guia, J., Guillermo, M., ... & Dadios, E. (2020, December). Machine vision-based prediction of lettuce phytomorphological descriptors using deep learning networks. In 2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) (pp. 1-6). IEEE.
Bakker, J. C., Bot, G. P. A., Challa, H., & Van de Braak, N. J. (Eds.). (1995). Greenhouse climate control: an integrated approach. Wageningen Academic Publishers.
Fu, L., Gao, F., Wu, J., Li, R., Karkee, M., & Zhang, Q. (2020). Application of consumer RGB-D cameras for fruit detection and localization in field: A critical review. Computers and Electronics in Agriculture, 177, 105687.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
Bauer, A., Bostrom, A. G., Ball, J., Applegate, C., Cheng, T., Laycock, S., ... & Zhou, J. (2019). Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production. Horticulture research, 6.