Optimum spectral and geometric parameters for early detection of laurel wilt disease in avocado

Castro, Ana Isabel de; Ehsani, Reza; Ploetz, R. C.; Crane, Jonathan H.; Abdulridha, Jaafar

doi:10.1016/j.rse.2015.09.011

Cited by 68 publications

(37 citation statements)

References 48 publications

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“…This is an easy to implement ratio, designed to detect structural and color differences in land classes [35], and is insensitive to soil effects, e.g., differences observed in this ratio have been used to separate vegetation and bare soil, as NIR reflectance is higher for vegetation than for bare soil, while more similar spectral values are shown for vegetation and bare soil when considering a broad waveband in the green region, which enhances these diferences [36]. The optimum ratio value for vegetation distinction was conducted using an automatic and iterative threshold approach, following the Otsu method [37], implemented in eCognition, in accordance with Torres-Sánchez et al [28].…”

mentioning

confidence: 99%

An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery

et al. 2018

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Accurate and timely detection of weeds between and within crop rows in the early growth stage is considered one of the main challenges in site-specific weed management (SSWM). In this context, a robust and innovative automatic object-based image analysis (OBIA) algorithm was developed on Unmanned Aerial Vehicle (UAV) images to design early post-emergence prescription maps. This novel algorithm makes the major contribution. The OBIA algorithm combined Digital Surface Models (DSMs), orthomosaics and machine learning techniques (Random Forest, RF). OBIA-based plant heights were accurately estimated and used as a feature in the automatic sample selection by the RF classifier; this was the second research contribution. RF randomly selected a class balanced training set, obtained the optimum features values and classified the image, requiring no manual training, making this procedure time-efficient and more accurate, since it removes errors due to a subjective manual task. The ability to discriminate weeds was significantly affected by the imagery spatial resolution and weed density, making the use of higher spatial resolution images more suitable. Finally, prescription maps for in-season post-emergence SSWM were created based on the weed maps-the third research contribution-which could help farmers in decision-making to optimize crop management by rationalization of the herbicide application. The short time involved in the process (image capture and analysis) would allow timely weed control during critical periods, crucial for preventing yield loss.

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mentioning

confidence: 99%

An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery

et al. 2018

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“…This is related to the progressive nature of disease development, which makes it easier to differentiate the late stage than the early stage. By detecting disease at the early stage, particularly at asymptomatic stage, growers can spray or remove the affected tree and manage the disease more cost effectively before the disease spreads to the rest of the grove [36]. Using smaller number of spectral bands it is possible to use inexpensive remote sensing tools such as small drones to detect the LW infected trees [36].…”

Section: Discussionmentioning

confidence: 99%

“…Spectral data were selected in the visible and near infrared domain from 350 to 950 nm. Spectral data were averaged for both stages to 10 nm and 40 nm based on previous studies [36,37]The purpose of averaging data to 10 and 40 nm is to reduce the numerous wavelengths and also reduce noise and interference between wavebands. Additionally, waveband filters for 10 and 40 nm are available in the market and are reasonably priced for future sensor development.…”

Section: Spectral Data Collectionmentioning

confidence: 99%

Detection and Differentiation between Laurel Wilt Disease, Phytophthora Disease, and Salinity Damage Using a Hyperspectral Sensing Technique

2016

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Laurel wilt (Lw) is a fatal disease. It is a vascular pathogen and is considered a major threat to the avocado industry in Florida. Many of the symptoms of Lw resemble those that are caused by other diseases or stress factors. In this study, the best wavelengths with which to discriminate plants affected by Lw from stress factors were determined and classified. Visible-near infrared (400-950 nm) spectral data from healthy trees and those with Lw, Phytophthora, or salinity damage were collected using a handheld spectroradiometer. The total number of wavelengths was averaged in two ranges: 10 nm and 40 nm. Three classification methods, stepwise discriminant (STEPDISC) analysis, multilayer perceptron (MLP), and radial basis function (RBF), were applied in the early stage of Lw infestation. The classification results obtained for MLP, with percent accuracy of classification as high as 98% were better than STEPDISC and RBF. The MLP neural network selected certain wavelengths that were crucial for correctly classifying healthy trees from those with stress trees. The results showed that there were sufficient spectral differences between laurel wilt, healthy trees, and trees that have other diseases; therefore, a remote sensing technique could diagnose Lw in the early stage of infestation.

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“…In subsequent work, spectral data were used to distinguish healthy, laurel wilt-affected, and Phytophthora root rot-affected avocado trees [191]. With a modified camera, spectral images were taken during helicopter surveys of commercial avocado orchards [192].…”

Section: Ecology and Epidemiologymentioning

confidence: 99%

“…Significant differences were observed in all VIs calculated among laurel wilt affected and healthy trees, although the best results were achieved with Excess Red, (Red-Green) and Combination 1. These results were used to modify a MCA-6 Tetracam camera with different spectral filters (580-10 nm, 650-10 nm, 740-10 nm, 750-10 nm, 760-10 nm and 850-40 nm), which was then used to take multispectral images of avocado trees at early, intermediate and late stages of laurel wilt development at three altitudes (180,250 and 300 m) [191]. Inexpensive devices that use this technology need to be developed.…”

Section: Ecology and Epidemiologymentioning

confidence: 99%

Laurel Wilt in Natural and Agricultural Ecosystems: Understanding the Drivers and Scales of Complex Pathosystems

Ploetz

Kendra²,

Choudhury

et al. 2017

Forests

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Laurel wilt kills members of the Lauraceae plant family in the southeastern United States. It is caused by Raffaelea lauricola T.C. Harr., Fraedrich and Aghayeva, a nutritional fungal symbiont of an invasive Asian ambrosia beetle, Xyleborus glabratus Eichhoff, which was detected in Port Wentworth, Georgia, in 2002. The beetle is the primary vector of R. lauricola in forests along the southeastern coastal plain of the United States, but other ambrosia beetle species that obtained the pathogen after the initial introduction may play a role in the avocado (Persea americana Miller) pathosystem. Susceptible taxa are naïve (new-encounter) hosts that originated outside Asia. In the southeastern United States, over 300 million trees of redbay (P. borbonia (L.) Spreng.) have been lost, and other North American endemics, non-Asian ornamentals and avocado-an important crop that originated in MesoAmerica-are also affected. However, there are no reports of laurel wilt on the significant number of lauraceous endemics that occur in the Asian homeland of R. lauricola and X. glabratus; coevolved resistance to the disease in the region has been hypothesized. The rapid spread of laurel wilt in the United States is due to an efficient vector, X. glabratus, and the movement of wood infested with the insect and pathogen. These factors, the absence of fully resistant genotypes, and the paucity of effective control measures severely constrain the disease's management in forest ecosystems and avocado production areas.

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Optimum spectral and geometric parameters for early detection of laurel wilt disease in avocado

Cited by 68 publications

References 48 publications

An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery

An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery

Detection and Differentiation between Laurel Wilt Disease, Phytophthora Disease, and Salinity Damage Using a Hyperspectral Sensing Technique

Laurel Wilt in Natural and Agricultural Ecosystems: Understanding the Drivers and Scales of Complex Pathosystems

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