Detection of chlorophyll content based on optical properties of maize leaves

Pan, Weidong; Cheng, Xiaodong; Du, Rongyu; Zhu, Xinhua; Guo, Wenchuan

doi:10.1016/j.saa.2024.123843

Cited by 9 publications

(3 citation statements)

References 29 publications

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Section: Introductionmentioning

confidence: 99%

“…Furthermore, it is a good indicator of mutations, stress, and nutritional status [3]. Understanding the chlorophyll content of plants is crucial for guiding plant cultivation management, such as determining the amount and timing of fertilization [4]. Chlorophyll is traditionally measured visually and in the laboratory (oven drying and solvent extraction followed by spectrophotometric determination, respectively).…”

Section: Introductionmentioning

confidence: 99%

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High-Throughput Analysis of Leaf Chlorophyll Content in Aquaponically Grown Lettuce Using Hyperspectral Reflectance and RGB Images

Taha,

Mao,

Wang

et al. 2024

Plants

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Chlorophyll content reflects plants’ photosynthetic capacity, growth stage, and nitrogen status and is, therefore, of significant importance in precision agriculture. This study aims to develop a spectral and color vegetation indices-based model to estimate the chlorophyll content in aquaponically grown lettuce. A completely open-source automated machine learning (AutoML) framework (EvalML) was employed to develop the prediction models. The performance of AutoML along with four other standard machine learning models (back-propagation neural network (BPNN), partial least squares regression (PLSR), random forest (RF), and support vector machine (SVM) was compared. The most sensitive spectral (SVIs) and color vegetation indices (CVIs) for chlorophyll content were extracted and evaluated as reliable estimators of chlorophyll content. Using an ASD FieldSpec 4 Hi-Res spectroradiometer and a portable red, green, and blue (RGB) camera, 3600 hyperspectral reflectance measurements and 800 RGB images were acquired from lettuce grown across a gradient of nutrient levels. Ground measurements of leaf chlorophyll were acquired using an SPAD-502 m calibrated via laboratory chemical analyses. The results revealed a strong relationship between chlorophyll content and SPAD-502 readings, with an R2 of 0.95 and a correlation coefficient (r) of 0.975. The developed AutoML models outperformed all traditional models, yielding the highest values of the coefficient of determination in prediction (Rp2) for all vegetation indices (VIs). The combination of SVIs and CVIs achieved the best prediction accuracy with the highest Rp2 values ranging from 0.89 to 0.98, respectively. This study demonstrated the feasibility of spectral and color vegetation indices as estimators of chlorophyll content. Furthermore, the developed AutoML models can be integrated into embedded devices to control nutrient cycles in aquaponics systems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High-Throughput Analysis of Leaf Chlorophyll Content in Aquaponically Grown Lettuce Using Hyperspectral Reflectance and RGB Images

Taha,

Mao,

Wang

et al. 2024

Plants

View full text Add to dashboard Cite

show abstract

“…As a non-destructive, efficient, and realtime remote sensing monitoring method, hyperspectral technology can use the differences in the absorption and reflection of light energy by chlorophyll in different plants to achieve LCC quantitative estimation [9]. Pan et al [10] found a high correlation between the derivative spectra of maize leaves at 714 nm and their chlorophyll content. Recent studies have demonstrated the efficacy of employing a diverse array of remote sensing data and physical and empirical models in enhancing the precision of LCC estimation.…”

Section: Introductionmentioning

confidence: 99%

Ground-Based Hyperspectral Estimation of Maize Leaf Chlorophyll Content Considering Phenological Characteristics

Guo,

Jiang,

Miao

et al. 2024

Remote Sensing

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Accurately measuring leaf chlorophyll content (LCC) is crucial for monitoring maize growth. This study aims to rapidly and non-destructively estimate the maize LCC during four critical growth stages and investigate the ability of phenological parameters (PPs) to estimate the LCC. First, four spectra were obtained by spectral denoising followed by spectral transformation. Next, sensitive bands (Rλ), spectral indices (SIs), and PPs were extracted from all four spectra at each growth stage. Then, univariate models were constructed to determine their potential for independent LCC estimation. The multivariate regression models for the LCC (LCC-MR) were built based on SIs, SIs + Rλ, and SIs + Rλ + PPs after feature variable selection. The results indicate that our machine-learning-based LCC-MR models demonstrated high overall accuracy. Notably, 83.33% and 58.33% of these models showed improved accuracy when the Rλ and PPs were successively introduced to the SIs. Additionally, the model accuracies of the milk-ripe and tasseling stages outperformed those of the flare–opening and jointing stages under identical conditions. The optimal model was created using XGBoost, incorporating the SI, Rλ, and PP variables at the R3 stage. These findings will provide guidance and support for maize growth monitoring and management.

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Carbon Dot‐Based Smart Soil with Automatically Adjustable Porosity and Aggregate Size

Niu,

Jiang,

Zhan

et al. 2024

Adv Funct Materials

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With the world's population expanding rapidly, modern agriculture is evolving at a swift pace, leading to a significant increase in food production, primarily through the use of chemical fertilizers. However, this heavy reliance on fertilizers has led to issues such as soil compaction and decrease of earthworm abundance. Herein this study reports a smart soil consisting of conventional soil and carbon dots (CDot) based smart material which is capable of automatically adjusting its porosity and soil aggregate size. Due to the responsiveness, The CDot‐based smart soil can lift its height up to 150% upon humidity stimulation due to the increased porosity which is similar to the effect caused by earthworms. Based on this effect, this smart soil can increase the growth rate of maize seedlings by 40%, thus showing great potentials in agriculture.

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Detection of chlorophyll content based on optical properties of maize leaves

Cited by 9 publications

References 29 publications

High-Throughput Analysis of Leaf Chlorophyll Content in Aquaponically Grown Lettuce Using Hyperspectral Reflectance and RGB Images

High-Throughput Analysis of Leaf Chlorophyll Content in Aquaponically Grown Lettuce Using Hyperspectral Reflectance and RGB Images

Ground-Based Hyperspectral Estimation of Maize Leaf Chlorophyll Content Considering Phenological Characteristics

Carbon Dot‐Based Smart Soil with Automatically Adjustable Porosity and Aggregate Size

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