Non-destructive Leaf Area Index estimation via guided optical imaging for large scale greenhouse environments

This paper presents a financially viable and non-destructive rail-based video monitoring method that utilizes optical image segmentation to estimate the canopy leaf area index (LAI) of greenhouse tomato plants. The LAI is directly related to the time-dependent crop growth and indicates plant health and potential crop yields. A railguided mobile camera system was commissioned that records continuous images by scanning multiple rows of two tomato plant species for over two years. UNET semantic image segmentation of the individual image frames was performed to compute the relative leaf area over time. This study also describes the image annotation process necessary to train the neural network and evaluate the segmentation results. The results are calibrated and compared to the defoliation-based (destructive) LAI estimation performed by the grower. This UNET segmentation performs well, which is enabled through the controlled environment and the well-defined boundary conditions provided by the greenhouse environment and the managed measurement conditions. Our results deviate from the manual LAI estimation by less than ten percent. Further, we are able to minimize confusion between foreground and background plants and other obstructions with an estimated error smaller than three percent, which is strictly necessary to produce reproducible results.

Automated summary

This paper presents a rail-based video monitoring method for estimating the leaf area index (LAI) of greenhouse tomato plants. The method utilizes optical image segmentation and UNET semantic image segmentation to calculate the relative leaf area over time. The results show that this method performs well with an error of less than ten percent compared to manual estimation, and it is able to accurately distinguish foreground and background plants.