Estimating Relative Chlorophyll Content in Rice Leaves Using Unmanned Aerial Vehicle Multi-Spectral Images and Spectral–Textural Analysis

Abstract

Leaf chlorophyll content is crucial for monitoring plant growth and photosynthetic capacity. The Soil and Plant Analysis Development (SPAD) values are widely utilized as a relative chlorophyll content index in ecological agricultural surveys and vegetation remote sensing applications. Multi-spectral cameras are a cost-effective alternative to hyperspectral cameras for agricultural monitoring. However, the limited spectral bands of multi-spectral cameras restrict the number of vegetation indices (VIs) that can be synthesized, necessitating the exploration of other options for SPAD estimation. This study evaluated the impact of using texture indices (TIs) and VIs, alone or in combination, for estimating rice SPAD values during different growth stages. A multi-spectral camera was attached to an unmanned aerial vehicle (UAV) to collect remote sensing images of the rice canopy, with manual SPAD measurements taken immediately after each flight. Random forest (RF) was employed as the regression method, and evaluation metrics included coefficient of determination (R2) and root mean squared error (RMSE). The study found that textural information extracted from multi-spectral images could effectively assess the SPAD values of rice. Constructing TIs by combining two textural feature values (TFVs) further improved the correlation of textural information with SPAD. Utilizing both VIs and TIs demonstrated superior performance throughout all growth stages. The model works well in estimating the rice SPAD in an independent experiment in 2022, proving that the model has good generalization ability. The results suggest that incorporating both spectral and textural data can enhance the precision of rice SPAD estimation throughout all growth stages, compared to using spectral data alone. These findings are of significant importance in the fields of precision agriculture and environmental protection.

Regional Monitoring of Leaf ChlorophyII Content of Summer Maize by Integrating Multi-Source Remote Sensing Data

“…The introduction of multi-source data (ground hyperspectral data, UAV visible-light data, and environment temperature data, etc.) [47] and texture indices can obviously improve simulation accuracy of LCC [48]. But in the comparison of different machine learning algorithms, different researchers may find different optimal models for LCC inversion under different conditions.…”

Application of RGB UAV images to identify spectral patterns and estimate rice production

“…To secure rice productivity, early monitoring is important to identify the crop condition, to improve rice crop management, and eventually to estimate rice production (Sari et al, 2021). Rice plant characteristics that can be monitored are plant height (Muangprakhon & Kaewplang, 2021), chlorophyll content index (Ban et al, 2022;Wang et al, 2023), number of tillers (Munibah et al, 2022) and leaf area index (LAI) (Gong et al, 2021). Those characteristics are important to understand the crop condition.…”

“…Growth-associated characteristics of plants, such as chlorophyll content and water content, can be quantified using remotesensing instruments (Chapman et al, 2014). Leaf chlorophyll and plant photosynthesis are strongly correlated, as plants with higher chlorophyll have a greater photosynthetic capacity and accumulate more photosynthetic products (Wang et al, 2023).…”

Cunninghamia lanceolata Canopy Relative Chlorophyll Content Estimation Based on Unmanned Aerial Vehicle Multispectral Imagery and Terrain Suitability Analysis

“…As a result, it is more difficult for multispectral imagery to accurately capture changes in forest SPAD than hyperspectral imagery. Previous studies have attempted to enhance the prediction of plant canopy SPAD by modifying the features integrated into modeling [22][23][24] and by exploring and contrasting diverse model construction techniques [25][26][27]. However, factors like geographical region, lighting conditions, image capture elevation, and terrain can impact model construction and predictive performance [28,29].…”