Identification of Early Heat and Water Stress in Strawberry Plants Using Chlorophyll-Fluorescence Indices Extracted via Hyperspectral Images

Abstract

Strawberry (Fragaria × ananassa Duch) plants are vulnerable to climatic change. The strawberry plants suffer from heat and water stress eventually, and the effects are reflected in the development and yields. In this investigation, potential chlorophyll-fluorescence-based indices were selected to detect the early heat and water stress in strawberry plants. The hyperspectral images were used to capture the fluorescence reflectance in the range of 500 nm–900 nm. From the hyperspectral cube, the region of interest (leaves) was identified, followed by the extraction of eight chlorophyll-fluorescence indices from the region of interest (leaves). These eight chlorophyll-fluorescence indices were analyzed deeply to identify the best indicators for our objective. The indices were used to develop machine-learning models to assess the performance of the indicators by accuracy assessment. The overall procedure is proposed as a new workflow for determining strawberry plants’ early heat and water stress. The proposed workflow suggests that by including all eight indices, the random-forest classifier performs well, with an accuracy of 94%. With this combination of the potential indices, namely the red-edge vegetation stress index (RVSI), chlorophyll B (Chl-b), pigment-specific simple ratio for chlorophyll B (PSSRb), and the red-edge chlorophyll index (CIREDEDGE), the gradient-boosting classifier performs well, with an accuracy of 91%. The proposed workflow works well with a limited number of training samples which is an added advantage.

Use of HSI Coupled with Machine Learning for the Identification of Water Stress in Pear Seedling Leaves

“…As there is no literature focusing on the plant leaf directly while the plants are growing, our study is significant because it can provide a timely and accurate monitoring system for farmers to take timely irrigation methods. Specifically, Poobalasubramanian et al (2022) did a similar research to ours by using chlorophyll-fluorescence indices extracted via hyperspectral images; they tried to identify early heat and water stress in strawberry plants. However, unlike our study, which focuses on one leaf, their research considered all the canopies of leaves of one plant.…”

Utilizing Hyperspectral Reflectance and Machine Learning Algorithms for Non-Destructive Estimation of Chlorophyll Content in Citrus Leaves

“…Conversely, the absorption of fluorescence is particularly pronounced in the blue range (400-450 nm) and the red range (650-700 nm). And that is influenced by presence of pigment groups [15][16][17]. Furthermore, some pre-processing techniques have provided some better solutions to estimate vegetation characteristics from hyperspectral reflectance.…”

LiteCCLKNet: A lightweight criss‐cross large kernel convolutional neural network for hyperspectral image classification

“…As a crucial technology of HSI analysis, HSI classification aims to assign a label to each pixel [2]. Currently, HSI classification techniques have been predominantly used in several challenging applications related to earth observation and remote sensing, such as precision agriculture [3], environmental monitoring [4], geological exploration [5], etc.…”