11/16/2023 0 Comments Late blight tomato leavesThis study has also provided a basis for further development of classification methods of sugar beet diseases in different developmental stages. Spectral reflectance has been measured in fields by using a handheld spectroradiometer in the range of 400–1050 nm, and the highest correlation coefficients (r = 0.85) have been detected in the visible region. Spectral signatures of spectral data have been analysed in leaves to differentiate sugar beet diseases 15. Ground-level reflectance spectra have also been obtained for the in-field detection of plant nitrogen 12, 13, 14. Amongst these four models, the model established by SMLR is the most efficient in predicting disease severity with a root mean square difference of 4.9% and a coefficient of determination of 0.82. Partial least squares (PLS) regression, stepwise multiple logistic regression (SMLR) and their combinations have been applied to derive four predictive models. The ability of reflectance spectroscopy in three regions, namely, ultraviolet, visible and near-infrared, has been evaluated indoors to determine the disease severity of tomato leaves infected with Xanthomonas perforans 11. The correct recognition rates of intact, sprouted and severely sprouted kernels are 100%, approximately 94% and 98%, respectively. Reflectance at 728 and 878 nm is utilised to classify sprouted and intact kernels, and the wavelength region above 720 nm is set to categorise sprouted kernels according to different levels of severity. A visible near-infrared spectrometer (400–1000 nm) is used in a reflectance mode to distinguish sprouted and intact wheat kernels under laboratory conditions 10. However, these approaches involve destructive methods, entail time- and labour-consuming processes and require specialised skills 5, 6, 7, 8.Īdvancements in agricultural technology have offered opportunities for the non-destructive detection of plant diseases through spectroscopy 9. Laboratory test approaches, such as polymerase chain reaction, enzyme-linked immunosorbent assay and loop-mediated isothermal amplification, are highly specific and sensitive to identify diseases on plant tissue samples. Conventional scouting for foliar diseases relies primarily on the visual inspection of leaf colour patterns and crown structures. Most of the foliar diseases, such as late blight, target and bacterial spots, are favoured by warm temperatures or prolonged periods of wetness, which are typical in Florida. Tomato diseases are caused by several factors, including fungal, bacterial and viral infections 1, 2, 3, 4. However, a series of diseases has threatened tomato production in Florida, resulting in large losses in fresh and processed tomato production. According to data from the US Department of Agriculture, Florida is the leading state in fresh-market tomato production. Further work may incorporate the proposed technique into an image system that can be operated to monitor multi-diseased tomato plants in fields.įresh-market tomatoes are produced in every state in the US, where 20 states support commercial-scale production. Late stage leaves could be distinguished more easily than the two other disease categories caused by similar symptoms on the multi-diseased leaves. Amongst the examined leaves, the healthy ones had the highest accuracy (100%) and the lowest error rate (0) because of their uniform tissues. SVIs with weight coefficients ranking from 1 to 30 in each selected PC were applied to a K-nearest neighbour for classification. Results revealed six principal components (PCs) whose eigenvalues were greater than 1. Principal component analysis was conducted to evaluate SVIs. Fifty-seven spectral vegetation indices (SVIs) were calculated in accordance with methods published in previous studies and established in this study. One healthy leaf and three diseased tomato leaves ( late blight, target and bacterial spots) were defined into four stages (healthy, asymptomatic, early stage and late stage) and collected from a field. In this study, a high-resolution portable spectral sensor was used to investigate the feasibility of detecting multi-diseased tomato leaves in different stages, including early or asymptomatic stages. Several diseases have threatened tomato production in Florida, resulting in large losses, especially in fresh markets.
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