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 non-destructive 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).
Highlights
PHOTOSYNTHETIC pigments in plants have essential roles in the process of plant growth
In this study, we compared 5 2 Dimensional-Convolutional Neural Network (2D-CNN) architectures to predict the content of photosynthetic pigments in a plant leaf using its multispectral image
There was a digital image of a plant leaf consisting of 10 channels
Summary
PHOTOSYNTHETIC pigments in plants have essential roles in the process of plant growth. Gitelson et al [4,5,6] have developed several non-destructive methods based on spectral reflectance from spectrophotometer-based measurements to predict the content of three main photosynthetic pigments (chlorophyll, carotenoid, anthocyanin) in plant leaves. With reference to the positive results in our preliminary study using different species and without the wet chemical procedure [12], we hypothesized that, the prediction of photosynthetic pigments content in plant leaves would be approaching the results given by the spectrophotometerbased measurements and certainly better than those of 3channel (RGB) images. We need a tool that can perform simultaneous and automatic extraction of all color components, such as the convolution method This makes 2D-CNN superior to conventional ANN, especially in handling multispectral digital images. This visualization shows that the leaf sample is very little or even does not reflect light at these ranges
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