Estimation of Apple Tree Leaf Chlorophyll Content Based on Machine Learning Methods

Leaf chlorophyll content (LCC) is one of the most important factors affecting photosynthetic capacity and nitrogen status, both of which influence crop harvest. However, the development of rapid and nondestructive methods for leaf chlorophyll estimation is a topic of much interest. Hence, this study explored the use of the machine learning approach to enhance the estimation of leaf chlorophyll from spectral reflectance data. The objective of this study was to evaluate four different approaches for estimating the LCC of apple tree leaves at five growth stages (the 1st, 2nd, 3rd, 4th and 5th growth stages): (1) univariate linear regression (ULR); (2) multivariate linear regression (MLR); (3) support vector regression (SVR); and (4) random forest (RF) regression. Samples were collected from the leaves on the eastern, western, southern and northern sides of apple trees five times (1st, 2nd, 3rd, 4th and 5th growth stages) over three consecutive years (2016–2018), and experiments were conducted in 10–20-year-old apple tree orchards. Correlation analysis results showed that LCC and ST, LCC and vegetation indices (VIs), and LCC and three edge parameters (TEP) had high correlations with the first-order differential spectrum (FODS) (0.86), leaf chlorophyll index (LCI) (0.87), and (SDr − SDb)/ (SDr + SDb) (0.88) at the 3rd, 3rd, and 4th growth stages, respectively. The prediction models of different growth stages were relatively good. The MLR and SVR models in the LCC assessment of different growth stages only reached the highest R2 values of 0.79 and 0.82, and the lowest RMSEs were 2.27 and 2.02, respectively. However, the RF model evaluation was significantly better than above models. The R2 value was greater than 0.94 and RMSE was less than 1.37 at different growth stages. The prediction accuracy of the 1st growth stage (R2 = 0.96, RMSE = 0.95) was best with the RF model. This result could provide a theoretical basis for orchard management. In the future, more models based on machine learning techniques should be developed using the growth information and physiological parameters of orchards that provide technical support for intelligent orchard management.

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Assessment of rice leaf chlorophyll content using visible bands at different growth stages at both the leaf and canopy scale

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2014.03.018 ◽

2014 ◽

Vol 32 ◽

pp. 35-45 ◽

Cited By ~ 60

Author(s):

M.M. Saberioon ◽

M.S.M. Amin ◽

A.R. Anuar ◽

A. Gholizadeh ◽

A. Wayayok ◽

...

Keyword(s):

Chlorophyll Content ◽

Growth Stages ◽

Leaf Chlorophyll Content ◽

Rice Leaf ◽

Leaf Chlorophyll ◽

Different Growth Stages

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Molecular Detection of Quantitative Trait Loci for Leaf Chlorophyll Content at Different Growth-Stages of Rice (Oryza sativa L.)

Asian Journal of Plant Sciences ◽

10.3923/ajps.2007.518.522 ◽

2007 ◽

Vol 6 (3) ◽

pp. 518-522 ◽

Cited By ~ 6

Author(s):

Hailong Zuo . ◽

Ke Xiao . ◽

Yanjun Dong . ◽

Jianlong Xu . ◽

Zhikang Li . ◽

...

Keyword(s):

Oryza Sativa ◽

Quantitative Trait Loci ◽

Chlorophyll Content ◽

Quantitative Trait ◽

Molecular Detection ◽

Oryza Sativa L ◽

Growth Stages ◽

Leaf Chlorophyll Content ◽

Leaf Chlorophyll ◽

Different Growth Stages

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Response of Maize (Zea mays L.) Secondary Growth Parameters to Conservation Agriculture and Conventional Tillage Systems in Zimbabwe

Journal of Agricultural Science ◽

10.5539/jas.v8n11p112 ◽

2016 ◽

Vol 8 (11) ◽

pp. 112 ◽

Cited By ~ 3

Author(s):

Regina Hlatywayo ◽

Blessing Mhlanga ◽

Upenyu Mazarura ◽

Walter Mupangwa ◽

Christian Thierfelder

Keyword(s):

Chlorophyll Content ◽

Secondary Growth ◽

Conservation Agriculture ◽

Growth Stages ◽

Plant Vigour ◽

Training Centre ◽

Leaf Chlorophyll Content ◽

Lattice Design ◽

Leaf Chlorophyll ◽

Maize Varieties

Previous, research focused mainly on the effects of conservation agriculture (CA) and conventional practices (CP) on crop yield mostly. A study was conducted at five sites in Zimbabwe from 2012 to 2014 to investigate effects of CA and CP practices on emergence, chlorophyll content, early vigour and grain yield of different maize varieties using 12 hybrids and 4 open pollinated varieties (OPVs). The experiment was laid as a 4 × 4 alpha lattice design with three replications. Emergence was higher under CA (75%) at University of Zimbabwe (UZ) in 2012/13 and Domboshawa Training Centre (DTC) (67%) compared to CP (71% and 39% respectively). Lower early plant vigour was observed under CA compared to CP at most sites. CA had lower leaf chlorophyll content during the early crop growth stages compared to CP. However, at some instances, CA had higher leaf chlorophyll content (45 units) than CP (35 units) at 78 days after sowing in Zimuto 2012/13. For maize yield, CA outperformed CP on a sandy loamy soil (3050 kg ha-1 vs 2656 kg ha-1) and clay soil (4937 kg ha-1 vs 4274 kg ha-1). However, on a sandy soil, CP outperformed CA (1764 vs 1313 kg ha-1). Our results suggest that tillage effects on early maize plant vigor, leaf chlorophyll content and the final yield can be site and season specific. Furthermore, a delay of nutrient release for plant uptake under CA systems was found and potentially implies investigations of new fertilization strategies for such cropping systems.

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Nondestructive Measurement of Grape Leaf Chlorophyll Content Using Multi-Spectral Imaging Technology and Calibration Models

Advanced Engineering Forum ◽

10.4028/www.scientific.net/aef.1.365 ◽

2011 ◽

Vol 1 ◽

pp. 365-369 ◽

Cited By ~ 1

Author(s):

Gang Lv ◽

Hai Qing Yang

Keyword(s):

Linear Regression ◽

Chlorophyll Content ◽

Spectral Imaging ◽

Nondestructive Measurement ◽

Imaging Technology ◽

Leaf Chlorophyll Content ◽

Independent Validation ◽

Leaf Chlorophyll ◽

Validation Set ◽

Grape Leaf

Nondestructive measurement of grape leaf chlorophyll content is essential for precision vineyard management. Multi-spectral imaging technology was adopted for image acquisition of grape leave. For each leaf, a color (R-G-B) image and a near-infrared (NIR) image were taken. These images were then transformed into three vegetation indices, e.g. RVI, NDVI and GNDVI. Calibration models were established, by single-variable linear regression, multi-variable linear regression and BP-ANN. Three color space systems, e.g. R-G-B, CIE XYZ and HIS, were examined with the purpose of model optimization. A total of 112 leave were divided into a calibration set(62) and an independent validation set(50). A SPAD-502 chlorophyll meter was used for reference measurement. The single-variable linear regression result shows that the NDVI index is most significant for the measurement of leaf chlorophyll content with coefficient of determination (r2) of 0.70 for calibration set and 0.69 for independent validation set. It is found that the model for R-index produces higher accuracy than those for G- and B-index, which confirms that chlorophyll content can be correlated with R-grayscale values. By comparison, the multi-variable linear regression models based on R-G-B-NIR achieves higher prediction accuracy with r2 of 0.8174. To further improve the prediction accuracy, several BP-ANN models were developed. The best result was achieved for R-G-B-NIR with r2 of 0.99 for independent validation set. It is concluded that multi-spectral imaging technology coupled with BP-ANN calibration model of R-G-B-NIR grayscales is promising for nondestructive measurement of grape leaf chlorophyll content. This method proposed in the study is worthy of being further examined for in situ determination of nutrition diagnose of grape plant.

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Hyperspectral reflectance sensing for quantifying leaf chlorophyll content in wasabi leaves using spectral pre-processing techniques and machine learning algorithms

International Journal of Remote Sensing ◽

10.1080/01431161.2020.1826065 ◽

2020 ◽

Vol 42 (4) ◽

pp. 1311-1329

Author(s):

Rei Sonobe ◽

Hiroto Yamashita ◽

Harumi Mihara ◽

Akio Morita ◽

Takashi Ikka

Keyword(s):

Machine Learning ◽

Chlorophyll Content ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Hyperspectral Reflectance ◽

Leaf Chlorophyll Content ◽

Leaf Chlorophyll ◽

Processing Techniques

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Scaling Effects on Chlorophyll Content Estimations with RGB Camera Mounted on a UAV Platform Using Machine-Learning Methods

Sensors ◽

10.3390/s20185130 ◽

2020 ◽

Vol 20 (18) ◽

pp. 5130 ◽

Cited By ~ 1

Author(s):

Yahui Guo ◽

Guodong Yin ◽

Hongyong Sun ◽

Hanxi Wang ◽

Shouzhi Chen ◽

...

Keyword(s):

Machine Learning ◽

Chlorophyll Content ◽

Scale Effects ◽

Coefficient Of Determination ◽

Support Vector ◽

Flight Altitude ◽

Chlorophyll Contents ◽

Leaf Chlorophyll ◽

Quantitative Remote Sensing ◽

Rgb Images

Timely monitoring and precise estimation of the leaf chlorophyll contents of maize are crucial for agricultural practices. The scale effects are very important as the calculated vegetation index (VI) were crucial for the quantitative remote sensing. In this study, the scale effects were investigated by analyzing the linear relationships between VI calculated from red–green–blue (RGB) images from unmanned aerial vehicles (UAV) and ground leaf chlorophyll contents of maize measured using SPAD-502. The scale impacts were assessed by applying different flight altitudes and the highest coefficient of determination (R2) can reach 0.85. We found that the VI from images acquired from flight altitude of 50 m was better to estimate the leaf chlorophyll contents using the DJI UAV platform with this specific camera (5472 × 3648 pixels). Moreover, three machine-learning (ML) methods including backpropagation neural network (BP), support vector machine (SVM), and random forest (RF) were applied for the grid-based chlorophyll content estimation based on the common VI. The average values of the root mean square error (RMSE) of chlorophyll content estimations using ML methods were 3.85, 3.11, and 2.90 for BP, SVM, and RF, respectively. Similarly, the mean absolute error (MAE) were 2.947, 2.460, and 2.389, for BP, SVM, and RF, respectively. Thus, the ML methods had relative high precision in chlorophyll content estimations using VI; in particular, the RF performed better than BP and SVM. Our findings suggest that the integrated ML methods with RGB images of this camera acquired at a flight altitude of 50 m (spatial resolution 0.018 m) can be perfectly applied for estimations of leaf chlorophyll content in agriculture.

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Using Spectral Reflectance to Estimate the Leaf Chlorophyll Content of Maize Inoculated With Arbuscular Mycorrhizal Fungi Under Water Stress

Frontiers in Plant Science ◽

10.3389/fpls.2021.646173 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jinhua Sun ◽

Liu Yang ◽

Xitian Yang ◽

Jie Wei ◽

Lantao Li ◽

...

Keyword(s):

Water Stress ◽

Chlorophyll Content ◽

Spectral Data ◽

Carbon Dioxide Concentration ◽

Machine Learning Algorithms ◽

Least Square ◽

Support Vector ◽

Important Indicator ◽

Leaf Chlorophyll Content ◽

Leaf Chlorophyll

Leaf chlorophyll content is an important indicator of the growth and photosynthesis of maize under water stress. The promotion of maize physiological growth by (AMF) has been studied. However, studies of the effects of AMF on the leaf chlorophyll content of maize under water stress as observed through spectral information are rare. In this study, a pot experiment was carried out to spectrally estimate the leaf chlorophyll content of maize subjected to different durations (20, 35, and 55 days); degrees of water stress (75%, 55% and 35% water supply) and two inoculation treatments (inoculation with Funneliformis mosseae and no inoculation). Three machine learning algorithms, including the back propagation (BP) method, least square support vector machine (LSSVM) and random forest (RF) method, were used to estimate the leaf chlorophyll content of maize. The results showed that AMF increased the leaf chlorophyll content, net photosynthetic rate (A), stomatal conductance (gs), transpiration rate (E), and water use efficiency (WUE) of maize but decreased the intercellular carbon dioxide concentration (Ci) of maize and atmospheric vapor pressure deficit (VPD) regardless of the water stress duration and degree. The first-order differential spectral data can better reflect the correlation between leaf chlorophyll content and spectrum of inoculated maize when compared with original spectral data. The BP model performed bestin modeling the maize leaf chlorophyll content, yielding the largest R2-values and smallest root mean square error (RMSE) values, regardless of stress duration. These results provide a reliable basis for the effective monitoring of the leaf chlorophyll content of maize under water stress.

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QUBO formulations for training machine learning models

Scientific Reports ◽

10.1038/s41598-021-89461-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Prasanna Date ◽

Davis Arthur ◽

Lauren Pusey-Nazzaro

Keyword(s):

Machine Learning ◽

Linear Regression ◽

Large Scale ◽

Support Vector ◽

Quantum Computers ◽

Np Hard ◽

Learning Models ◽

Moore’S Law ◽

Moore's Law ◽

Machine Learning Models

AbstractTraining machine learning models on classical computers is usually a time and compute intensive process. With Moore’s law nearing its inevitable end and an ever-increasing demand for large-scale data analysis using machine learning, we must leverage non-conventional computing paradigms like quantum computing to train machine learning models efficiently. Adiabatic quantum computers can approximately solve NP-hard problems, such as the quadratic unconstrained binary optimization (QUBO), faster than classical computers. Since many machine learning problems are also NP-hard, we believe adiabatic quantum computers might be instrumental in training machine learning models efficiently in the post Moore’s law era. In order to solve problems on adiabatic quantum computers, they must be formulated as QUBO problems, which is very challenging. In this paper, we formulate the training problems of three machine learning models—linear regression, support vector machine (SVM) and balanced k-means clustering—as QUBO problems, making them conducive to be trained on adiabatic quantum computers. We also analyze the computational complexities of our formulations and compare them to corresponding state-of-the-art classical approaches. We show that the time and space complexities of our formulations are better (in case of SVM and balanced k-means clustering) or equivalent (in case of linear regression) to their classical counterparts.

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