Evaluation of a One-Dimensional Convolution Neural Network for Chlorophyll Content Estimation Using a Compact Spectrometer

Abstract

Leaf chlorophyll content is used as a major indicator of plant stress and growth, and hyperspectral remote sensing is frequently used to monitor the chlorophyll content. Hyperspectral reflectance has been used to evaluate vegetation properties such as pigment content, plant structure and physiological features using portable spectroradiometers. However, the prices of these devices have not yet decreased to consumer-affordable levels, which prevents widespread use. In this study, a system based on a cost-effective fingertip-sized spectrometer (Colorcompass-LF, a total price for the proposed solution was approximately 1600 USD) was evaluated for its ability to estimate the chlorophyll contents of radish and wasabi leaves and was compared with the Analytical Spectral Devices FieldSpec4. The chlorophyll contents per leaf area (cm2) of radish were generally higher than those of wasabi and ranged from 42.20 to 94.39 μg/cm2 and 11.39 to 40.40 μg/cm2 for radish and wasabi, respectively. The chlorophyll content was estimated using regression models based on a one-dimensional convolutional neural network (1D-CNN) that was generated after the original reflectance from the spectrometer measurements was de-noised. The results from an independent validation dataset confirmed the good performance of the Colorcompass-LF after spectral correction using a second-degree polynomial, and very similar estimation accuracies were obtained for the measurements from the FieldSpec4. The coefficients of determination of the regression models based on 1D-CNN were almost same (with R2 = 0.94) and the ratios of performance to deviation based on reflectance after spectral correction using a second-degree polynomial for the Colorcompass-LF and the FieldSpec4 were 4.31 and 4.33, respectively.

Pomelo Tree Detection Method Based on Attention Mechanism and Cross-Layer Feature Fusion

“…Convolutional Neural Networks (CNNs) are among the best-known deep learning-based methods owing to their good image interpretability. CNNs are widely used to solve agricultural production problems, including plant pest detection and classification [23,24], plant leaf identification and classification [25,26], weed identification and classification [27,28], fruit and vegetable harvesting and identification [29,30], and land cover classification [31,32].…”

Assessment of the Drought-Tolerance Criteria for Screening Peach Cultivars

“…The chlorophyll content in leaves is often used as a basic indicator of plant stress and growth [52]. A high chlorophyll amount is generally regarded as one of the criteria for selecting plants for drought-tolerance breeding [53].…”