Non-destructive photosynthetic pigments prediction using multispectral imagery and 2D-CNN

Abstract

Rapid assessment of plant photosynthetic pigments content is an essential issue in precise management farming. Such an assessment can represent the status of plants in their stages of growth. We have developed a new 2 Dimensional-Convolutional Neural Network (2D-CNN) architecture, the P3MNet. This architecture simultaneously predicts the content of 3 main photosynthetic pigments of a plant leaf in a nondestructive and real-time manner using multispectral images. Those pigments are chlorophyll, carotenoid, and anthocyanin. By illuminating with visible light, the reflectance of individual plant leaf at 10 different wavelengths – 350, 400, 450, 500, 550, 600, 650, 700, 750, and 800 nm – was captured in a form of 10 digital images. It was then used as the 2D-CNN input. Here, our result suggested that P3MNet outperformed AlexNet and VGG-9. After undergoing a training process using Adadelta optimization method for 1000 epochs, P3MNet has achieved superior MAE (Mean Absolute Error) in the average of 0.000778 ± 0.0001 for training and 0.000817 ± 0.0007 for validation (data range 0-1).