  LAI (Leaf Area Index) is of great importance for crop yield estimation in
agronomy. It is directly related to plant growth status, net assimilation rate,
plant photosynthesis, and carbon dioxide in the environment. How to measure LAI
accurately and efficiently is the key to the crop yield estimation problem.
Manual measurement consumes a lot of human resources and material resources.
Remote sensing technology is not suitable for near-Earth LAI measurement.
Besides, methods based on traditional digital image processing are greatly
affected by environmental noise and image exposure. Nowadays, deep learning is
widely used in many fields. The improved FCN (Fully Convolutional Network) is
proposed in our study for LAI measure task. Eighty-two cucumber images
collected from our greenhouse are labeled to fine-tuning the pre-trained model.
The result shows that the improved FCN model performs well on our dataset. Our
method's mean IoU can reach 0.908, which is 11% better than conventional
methods and 4.7% better than the basic FCN model.
