Exploring the Role of Vegetation Indices in Plant Diseases Identification

Meena, Sangeeta Vaibhav; Dhaka, Vijaypal Singh; Sinwar, Deepak

doi:10.1109/pdgc50313.2020.9315814

Cited by 8 publications

(4 citation statements)

References 29 publications

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“…With regard to the leaf chlorophyll index (LCI), previous studies have shown that this index has a high correlation with plant chlorophyll content [56]. Changes in a plant's physical and biochemical structure caused by a stress factor could influence chlorophyll as an important pigment in photosynthesis [30]; therefore, the LCI value will decrease due to the reduction in this pigment content [56]. Nonetheless, in the present study, LCI showed heterogeneity in both experimental years.…”

Section: Discussioncontrasting

confidence: 51%

“…Evaluation of vegetation can be conducted precisely using various VIs, but this greatly depends on the experimental questions [29] because each index has its own unique characteristics and usage purposes [26]. Near infrared (NIR)-and red-edge-based indices are used to measure plant health and biotic and abiotic stresses because they are better at showing plant reaction to stress within these bands [30]. RGB-based indices, which are calculated using visible reflectance bands [31], are the simplest and most commonly used UAV method for monitoring vegetation because they do not require a special, expensive multispectral camera.…”

Section: Introductionmentioning

confidence: 99%

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Estimating the Reduction in Cover Crop Vitality Followed by Pelargonic Acid Application Using Drone Imagery

2023

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Cultivation of cover crops is a valuable practice in sustainable agriculture. In cover crop management, the method of desiccation is an important consideration, and one widely used method for this is the application of glyphosate. With use of glyphosate likely to be banned soon in Europe, the purpose of this study was to evaluate the herbicidal effect of pelargonic acid (PA) as a bio-based substitute for glyphosate. This study presents the results of a two-year field experiment (2019 and 2021) conducted in northeast Germany. The experimental setup included an untreated control, three different dosages (16, 8, and 5 L/ha) of PA, and the active ingredients glyphosate and pyraflufen. A completely randomised block design was established. The effect of the herbicide treatments was assessed by a visual estimate of the percentage of crop vitality and a comparison assessment provided by an Ebee+ drone. Four vegetation indices (VIs) calculated from the drone images were used to verify the credibility of colour (RGB)-based and near-infrared (NIR)-based vegetation indices. The results of both types of assessment indicated that pelargonic acid was reasonably effective in controlling cover crops within a week of application. In both experimental years, the PA (16 L/ha) and PA_2T (double application of 8 L/ha) treatments demonstrated their highest herbicidal effect for up to seven days after application. PA (16 L/ha) vitality loss decreased over time, while PA_2T (double application of 8 L/ha) continued to exhibit an almost constant effect for longer due to the second application one week later. The PA dosage of 5 L/ha, pyraflufen, and a mixture of the two exhibited a smaller vitality loss than the other treatments. However, except for glyphosate, the herbicidal effect of all the other treatments decreased over time. At the end of the experiment, the glyphosate treatment (3 L/ha) demonstrated the lowest estimated vitality. The results of the drone assessments indicated that vegetation indices (VIs) can provide detailed information regarding crop vitality following herbicide application and that RGB-based indices, such as EXG, have the potential to be applied efficiently and cost-effectively utilising drone imagery. The results of this study demonstrate that pelargonic acid has considerable potential for use as an additional tool in integrated crop management.

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Section: Discussioncontrasting

confidence: 51%

Section: Introductionmentioning

confidence: 99%

Estimating the Reduction in Cover Crop Vitality Followed by Pelargonic Acid Application Using Drone Imagery

2023

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“…Empirical methods can effectively characterize spectral changes [26]. Some studies focus on developing crop-specific spectral indices [27,28]. In addition, some studies have analyzed different spectral transformation forms, such as logarithms, derivatives, and continuous wavelet transforms, to enhance the separability of spectra under different severity levels [29,30].…”

Section: Introductionmentioning

confidence: 99%

Spectral Detection of Peanut Southern Blight Severity Based on Continuous Wavelet Transform and Machine Learning

et al. 2023

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Peanut southern blight has a severe impact on peanut production and is one of the most devastating soil-borne fungal diseases. We conducted a hyperspectral analysis of the spectral responses of plants to peanut southern blight to provide theoretical support for detecting the severity of the disease via remote sensing. In this study, we collected leaf-level spectral data during the winter of 2021 and the spring of 2022 in a greenhouse laboratory. We explored the spectral response mechanisms of diseased peanut leaves and developed a method for assessing the severity of peanut southern blight disease by comparing the continuous wavelet transform (CWT) with traditional spectral indices and incorporating machine learning techniques. The results showed that the SVM model performed best and was able to effectively detect the severity of peanut southern blight when using CWT (WF770~780, 5) as an input feature. The overall accuracy (OA) of the modeling dataset was 91.8% and the kappa coefficient was 0.88. For the validation dataset, the OA was 90.5% and the kappa coefficient was 0.87. These findings highlight the potential of this CWT-based method for accurately assessing the severity of peanut southern blight.

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“…The average values of ExG, VARI, and GLI at RGB-based vegetation indices are more important to predicting maturity in dry peas because they are more sensitive to changes in chlorophyll content and plant health [94]. Previous studies have also shown that these indices are effective in predicting plant maturity in various crops, including wheat [95] and maize [19].…”

Section: Feature Selectionmentioning

confidence: 99%

Predicting Dry Pea Maturity Using Machine Learning and Advanced Sensor Fusion with Unmanned Aerial Systems (UASs)

Bazrafkan,

Navasca,

Kim

et al. 2023

Remote Sensing

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Maturity is an important trait in dry pea breeding programs, but the conventional process predominately used to measure this trait can be time-consuming, labor-intensive, and prone to errors. Therefore, a more efficient and accurate approach would be desirable to support dry pea breeding programs. This study presents a novel approach for measuring dry pea maturity using machine learning algorithms and unmanned aerial systems (UASs)-collected data. We evaluated the abilities of five machine learning algorithms (random forest, artificial neural network, support vector machine, K-nearest neighbor, and naïve Bayes) to accurately predict dry pea maturity on field plots. The machine learning algorithms considered a range of variables, including crop height metrics, narrow spectral bands, and 18 distinct color and spectral vegetation indices. Backward feature elimination was used to select the most important features by iteratively removing insignificant ones until the model’s predictive performance was optimized. The study’s findings reveal that the most effective approach for assessing dry pea maturity involved a combination of narrow spectral bands, red-edge, near-infrared (NIR), and RGB-based vegetation indices, along with image textural metrics and crop height metrics. The implementation of a random forest model further enhanced the accuracy of the results, exhibiting the highest level of accuracy with a 0.99 value for all three metrics precision, recall, and f1 scores. The sensitivity analysis revealed that spectral features outperformed structural features when predicting pea maturity. While multispectral cameras achieved the highest accuracy, the use of RGB cameras may still result in relatively high accuracy, making them a practical option for use in scenarios where cost is a limiting factor. In summary, this study demonstrated the effectiveness of coupling machine learning algorithms, UASs-borne LIDAR, and multispectral data to accurately assess maturity in peas.

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Exploring the Role of Vegetation Indices in Plant Diseases Identification

Cited by 8 publications

References 29 publications

Estimating the Reduction in Cover Crop Vitality Followed by Pelargonic Acid Application Using Drone Imagery

Estimating the Reduction in Cover Crop Vitality Followed by Pelargonic Acid Application Using Drone Imagery

Spectral Detection of Peanut Southern Blight Severity Based on Continuous Wavelet Transform and Machine Learning

Predicting Dry Pea Maturity Using Machine Learning and Advanced Sensor Fusion with Unmanned Aerial Systems (UASs)

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