Monitoring of peanut leaves chlorophyll content based on drone-based multispectral image feature extraction

Huang, Qi; Wu, Zeyu; Zhang, Lei; Li, Jianwen; Zhou, Jingkang; Zou, Jun; Zhu, Bingyu

doi:10.1016/j.compag.2021.106292

Cited by 58 publications

(31 citation statements)

References 24 publications

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“…Thus, the increase in the RGR of the NaCl + CTS group may have been related to both NAR and LAR. The Chl content of leaves is commonly considered a reliable predictor of the health and photosynthesis capacity of plants during growth [60,61]. Chlorophyll degradation under salt stress is usually related to the accumulation of ROS, which causes lipid peroxidation of chloroplast membranes [62].…”

Section: Discussionmentioning

confidence: 99%

Exogenous Application of Chitosan Alleviate Salinity Stress in Lettuce (Lactuca sativa L.)

Zhang¹,

Wang²,

et al. 2021

Horticulturae

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Soil salinity is one of the major factors that affect plant growth and decrease agricultural productivity worldwide. Chitosan (CTS) has been shown to promote plant growth and increase the abiotic stress tolerance of plants. However, it still remains unknown whether the application of exogenous CTS can mitigate the deleterious effects of salt stress on lettuce plants. Therefore, the current study investigated the effect of foliar application of exogenous CTS to lettuce plants grown under 100 mM NaCl saline conditions. The results showed that exogenous CTS increased the lettuce total leaf area, shoot fresh weight, and shoot and root dry weight, increased leaf chlorophyll a, proline, and soluble sugar contents, enhanced peroxidase and catalase activities, and alleviated membrane lipid peroxidation, in comparison with untreated plants, in response to salt stress. Furthermore, the application of exogenous CTS increased the accumulation of K+ in lettuce but showed no significant effect on the K+/Na+ ratio, as compared with that of plants treated with NaCl alone. These results suggested that exogenous CTS might mitigate the adverse effects of salt stress on plant growth and biomass by modulating the intracellular ion concentration, controlling osmotic adjustment, and increasing antioxidant enzymatic activity in lettuce leaves.

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Section: Discussionmentioning

confidence: 99%

Exogenous Application of Chitosan Alleviate Salinity Stress in Lettuce (Lactuca sativa L.)

Zhang¹,

Wang²,

et al. 2021

Horticulturae

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show abstract

“…To avoid the difficulty of using standard chemical procedures for chlorophyll measurement, Hassanijalilian et al (2020) took RGB images of soybeans under field conditions with smartphones and selected the VIs with the best correlation with the SPAD meter readings to build an estimation model for the estimated chlorophyll content of soybeans. Qi et al (2021) calculated eight VIs through MS images obtained on a UAV platform and studied the ability of MS VIs to estimate the chlorophyll content of two types of peanuts, Yanghua 1 and Yueyou 45, under different planting densities. The results showed that this method could quickly obtain information on the chlorophyll content in the field and could infer the most suitable crop type and planting density for local planting conditions.…”

Section: Introductionmentioning

confidence: 99%

Performance comparison of RGB and multispectral vegetation indices based on machine learning for estimating Hopea hainanensis SPAD values under different shade conditions

et al. 2022

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Reasonable cultivation is an important part of the protection work of endangered species. The timely and nondestructive monitoring of chlorophyll can provide a basis for the accurate management and intelligent development of cultivation. The image analysis method has been applied in the nutrient estimation of many economic crops, but information on endangered tree species is seldom reported. Moreover, shade control, as the common seedling management measure, has a significant impact on chlorophyll, but shade levels are rarely discussed in chlorophyll estimation and are used as variables to improve model accuracy. In this study, 2-year-old seedlings of tropical and endangered Hopea hainanensis were taken as the research object, and the SPAD value was used to represent the relative chlorophyll content. Based on the performance comparison of RGB and multispectral (MS) images using different algorithms, a low-cost SPAD estimation method combined with a machine learning algorithm that is adaptable to different shade conditions was proposed. The SPAD values changed significantly at different shade levels (p < 0.01), and 50% shade in the orthographic direction was conducive to chlorophyll accumulation in seedling leaves. The coefficient of determination (R2), root mean square error (RMSE), and average absolute percent error (MAPE) were used as indicators, and the models with dummy variables or random effects of shade greatly improved the goodness of fit, allowing better adaption to monitoring under different shade conditions. Most of the RGB and MS vegetation indices (VIs) were significantly correlated with the SPAD values, but some VIs exhibited multicollinearity (variance inflation factor (VIF) > 10). Among RGB VIs, RGRI had the strongest correlation, but multiple VIs filtered by the Lasso algorithm had a stronger ability to interpret the SPAD data, and there was no multicollinearity (VIF < 10). A comparison of the use of multiple VIs to estimate SPAD indicated that Random forest (RF) had the highest fitting ability, followed by Support vector regression (SVR), linear mixed effect model (LMM), and ordinary least squares regression (OLR). In addition, the performance of MS VIs was superior to that of RGB VIs. The R2 of the optimal model reached 0.9389 for the modeling samples and 0.8013 for the test samples. These findings reinforce the effectiveness of using VIs to estimate the SPAD value of H. hainanensis under different shade conditions based on machine learning and provide a reference for the selection of image data sources.

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“…In satellite images, the computation of the index is carried out using spectral bands associated to wavelengths 800 nm, 505 nm, and 690 nm, respectively. However, for multispectral imagery collected by UAVs, specific bands can be employed, according to the following expression [38]:…”

Section: Comparison Methodsmentioning

confidence: 99%

Genetic Programming Approach for the Detection of Mistletoe Based on UAV Multispectral Imagery in the Conservation Area of Mexico City

Mejia-Zuluaga¹,

Dozal

Valdiviezo-N.

2022

Remote Sensing

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The mistletoe Phoradendron velutinum (P. velutinum) is a pest that spreads rapidly and uncontrollably in Mexican forests, becoming a serious problem since it is a cause of the decline of 23.3 million hectares of conifers and broadleaves in the country. The lack of adequate phytosanitary control has negative social, economic, and environmental impacts. However, pest management is a challenging task due to the difficulty of early detection for proper control of mistletoe infestations. Automating the detection of this pest is important due to its rapid spread and the high costs of field identification tasks. This paper presents a Genetic Programming (GP) approach for the automatic design of an algorithm to detect mistletoe using multispectral aerial images. Our study area is located in a conservation area of Mexico City, in the San Bartolo Ameyalco community. Images of 148 hectares were acquired by means of an Unmanned Aerial Vehicle (UAV) carrying a sensor sensitive to the R, G, B, red edge, and near-infrared bands, and with an average spatial resolution of less than 10 cm per pixel. As a result, it was possible to obtain an algorithm capable of classifying mistletoe P. velutinum at its flowering stage for the specific case of the study area in conservation area with an Overall Accuracy (OA) of 96% and a value of fitness function based on weighted Cohen’s Kappa (kw) equal to 0.45 in the test data set. Additionally, our method’s performance was compared with two traditional image classification methods; in the first, a classical spectral index, named Intensive Pigment Index of Structure 2 (SIPI2), was considered for the detection of P. velutinum. The second method considers the well-known Support Vector Machine classification algorithm (SVM). We also compare the accuracy of the best GP individual with two additional indices obtained during the solution analysis. According to our experimental results, our GP-based algorithm outperforms the results obtained by the aforementioned methods for the identification of P. velutinum.

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Monitoring of peanut leaves chlorophyll content based on drone-based multispectral image feature extraction

Cited by 58 publications

References 24 publications

Exogenous Application of Chitosan Alleviate Salinity Stress in Lettuce (Lactuca sativa L.)

Exogenous Application of Chitosan Alleviate Salinity Stress in Lettuce (Lactuca sativa L.)

Performance comparison of RGB and multispectral vegetation indices based on machine learning for estimating Hopea hainanensis SPAD values under different shade conditions

Genetic Programming Approach for the Detection of Mistletoe Based on UAV Multispectral Imagery in the Conservation Area of Mexico City

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