A New Crop Spectral Signatures Database Interactive Tool (CSSIT)

Awad, Mohamad M.; Alawar, Bassem; Jbeily, Rana

doi:10.3390/data4020077

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…This result suggested that the regions between 523 to 527 and 709 to 723 nm are important spectral wavelengths that can be further analysed to discriminate among pepper cultivars. Awad et al (2019) stated that the range from 700 to 750 nm (Red Edge) was always significant for species discrimination. Prospere et al (2014) mentioned that the Red Edge is the transition between the strong absorbance in the red region by chlorophyll pigment and the relatively high reflectivity in the NIR region attributed to the scattering effect caused by the internal leaf structure.…”

Section: Selected Physiological Characteristics Of Peppermentioning

confidence: 99%

Foliar Spectral Signature Analysis of Three Pepper (Piper nigrum L.) Cultivars in Malaysia

Kevin Muyang Tawie Anak Sulok,

Angela Tida Henry Ganie,

Kho Pei Ee

et al. 2023

JTPP

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Pepper (Piper nigrum L.) is an important cash crop for smallholders in the state of Sarawak, Malaysia. At present, there are more than eight pepper cultivars being planted in the state since the 1950s. In this study, the unique spectral signatures of three high yielding pepper cultivars in Malaysia were examined using a ground-based spectral sensor to detect varietal differences. Concurrent foliar spectral signatures, chlorophyll concentration, and Normalized Difference Vegetation Index (NDVI) of cultivar Kuching, Semongok Aman and Semongok Emas were recorded under normal field conditions. Foliar chlorophyll concentration and NDVI results showed that all the matured pepper vines under this assessment were in healthy condition. From a rough observation, except for a slight increase in the visible (VIS) region of the spectrum (500 to 699 nm) for cultivar Semongok Aman and Semongok Emas, spectral reflectance of all three pepper cultivars were similar in the near-infrared region (700 to 1000 nm). The results for First Derivative Curves analysis indicated that the 684.5 to 756.5 nm region showed the highest separation followed by region with the second lowest separation (504.5 to 540.5 nm). The highest value was obtained in the Red Edge Position (REP) for cultivar Kuching, Semongok Aman and Semongok Emas which were 709, 706 and 710, respectively. The highest value obtained in the VIS region for cultivar Kuching, Semongok Aman and Semongok Emas were 523, 524 and 527, respectively. These fundamental findings suggested that it is possible to discriminate pepper cultivars through field spectroscopy and first derivative analysis. In addition, this information may be used for further studies related to precision agriculture particularly in integrated nutrient and disease management.

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Section: Selected Physiological Characteristics Of Peppermentioning

confidence: 99%

Foliar Spectral Signature Analysis of Three Pepper (Piper nigrum L.) Cultivars in Malaysia

Kevin Muyang Tawie Anak Sulok,

Angela Tida Henry Ganie,

Kho Pei Ee

et al. 2023

JTPP

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“…Sat-4 [48], Sat-6 [48], UCMerced [49], and Brazilian Coffee scenes [50] datasets are among the most popular patch-level labelled datasets. In addition to the commonly used datasets, some tools provide the users with access to annotated/semi-annotated databases, which are usually collected by combining information from different resources that target particular uses, for example, crops [51,52], forests [53], or wetlands [54] monitoring.…”

Section: Data Sources and Datasetsmentioning

confidence: 99%

Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review

2020

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Lately, with deep learning outpacing the other machine learning techniques in classifying images, we have witnessed a growing interest of the remote sensing community in employing these techniques for the land use and land cover classification based on multispectral and hyperspectral images; the number of related publications almost doubling each year since 2015 is an attest to that. The advances in remote sensing technologies, hence the fast-growing volume of timely data available at the global scale, offer new opportunities for a variety of applications. Deep learning being significantly successful in dealing with Big Data, seems to be a great candidate for exploiting the potentials of such complex massive data. However, there are some challenges related to the ground-truth, resolution, and the nature of data that strongly impact the performance of classification. In this paper, we review the use of deep learning in land use and land cover classification based on multispectral and hyperspectral images and we introduce the available data sources and datasets used by literature studies; we provide the readers with a framework to interpret the-state-of-the-art of deep learning in this context and offer a platform to approach methodologies, data, and challenges of the field.

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“…HSI has the ability to provide both spectral and spatial information from the images, which allows the evaluation of spatially varied subtle spectral changes to infer the biophysical and biochemical status of the plants [5][6][7][8][9][10]. Research of plant classification and status monitoring based on the spectral library technique is of great significance for plant monitoring using hyperspectral remote sensing [11][12][13]. The spectral characteristics of different crop plants or plants under different statuses were studied to support monitoring tasks.…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning and Hyperspectral Images to Assess Damages to Corn Plant Caused by Glyphosate and to Evaluate Recoverability

et al. 2021

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Glyphosate is the most widely used herbicide in crop production due to the widespread adoption of glyphosate-resistant (GR) crops. However, the spray of glyphosate onto non-target crops from ground or aerial applications can cause severe injury to non-GR corn plants. To evaluate the crop damage of the non-GR corn plants from glyphosate and the recoverability of the damaged plants, we used the hyperspectral imaging (HSI) technique in field experiments with different glyphosate application rates. This study investigated the spectral characteristic of corn plants and assessed the corn plant damage from glyphosate. Based on HSI image analysis, a spectral variation pattern was observed at 1 week after treatment (WAT), 2 WAT, and 3 WAT from the glyphosate-treated non-GR corn plants. It was further found that the corn plants treated with glyphosate rates equal to or higher than 0.5X (X = 0.866 kilograms acid equivalents/hectare (kg ae/ha) represents the recommended spray rate for GR corn) would suffer unrecoverable damage. Using the Jeffries–Matusita distance as the spectral sensitivity criterion, three sensitive bands from the measured spectra were selected to create two spectral indices for crop recoverability differentiation in band ratio and normalization forms, respectively. With the two spectral indices, the corn plants recoverable and unrecoverable from damage were classified with an overall accuracy greater than 95%. Then, three machine learning algorithms (k-nearest neighbors, random forest, and support vector machine) were respectively combined with the successive projections algorithm to create models to relate selected feature spectral bands to glyphosate spray rates. The results indicated that the models achieved reasonable accuracy, especially in the group of recoverable plants. This study illustrated the potential of the hyperspectral imaging technique for evaluating crop damage from herbicides and recoverability of the injured plants using different data analysis and machine learning modeling approaches for practical weed management in crop fields.

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A New Crop Spectral Signatures Database Interactive Tool (CSSIT)

Cited by 8 publications

References 23 publications

Foliar Spectral Signature Analysis of Three Pepper (Piper nigrum L.) Cultivars in Malaysia

Foliar Spectral Signature Analysis of Three Pepper (Piper nigrum L.) Cultivars in Malaysia

Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review

Using Machine Learning and Hyperspectral Images to Assess Damages to Corn Plant Caused by Glyphosate and to Evaluate Recoverability

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