Monitoring drought induced photosynthetic and fluorescent variations of potatoes by visible and thermal imaging analysis

Accurate determination of photosynthetic parameters is critical to evaluate crop physiological and growth processes. This study aimed to estimate photosynthetic and fluorescence variables of potatoes by visible and thermal imaging fusion analysis. Two-years pot experiments were conducted in a climate chamber with different irrigation treatments. Multi-modal image features of crop canopy were extracted from both visible and thermal images by color component extraction, discrete wavelet transformation, gray level co-occurrence matrix, and local binary pattern algorithms. Extracted features were subsequently employed to build partial least squares regression (PLSR) models for estimation of transpiration rate (Tr), net photosynthetic rate (An), stomatal conductance (GSW), electron transport rate (ETR), and maximum photochemical efficiency under Photosystem II (Fv'/Fm'). Results showed that Mask Region-Convolutional Neural Network (Mask R-CNN) performed satisfactorily on canopy segmentation with intersection over union of 87.29 % and 86.93 % in visible and thermal images, respectively. Three different types of models that either using only visible image features (PLSRRGB), or only thermal image features (PLSRT) or both visible and thermal image features (PLSRRGB+T) as inputs were compared. Results showed that PLSRRGB+T had superior estimation performance in terms of R2 and RMSE. It achieved the highest R2 of 0.85 with An and the lowest R2 of 0.66 with GSW for Zhongshu 5, while it had the highest R2 of 0.86 with Fv'/Fm', and the lowest R2 of 0.71 with Tr and An for D681. This implied the potential of visible and thermal image-driven method for quick and accurate estimation of photosynthetic traits of crops grown in controlled environment.