Spectral prediction of<i>Phytophthora infestans</i>infection on tomatoes using artificial neural network (ANN)

Wang, X.; Zhang, M.; Zhu, Jiang; Geng, Shu

doi:10.1080/01431160701281007

Cited by 107 publications

(46 citation statements)

References 15 publications

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“…A number of studies aiming at identifying the most efficient spectral bands or vegetation indices (VIs) have been conducted at a foliar or canopy level for mapping and monitoring several important crop diseases (e.g., yellow rust, powdery mildew in winter wheat; late blight in tomato) (Wang et al 2008;Moshou et al 2011;Zhang et al 2012). These studies mostly rely on using airborne/satellite images (Franke and Menz 2007;Huang et al 2007;Calderón et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale

Yuan

Zhang

et al. 2015

Precision Agric

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Efficient crop protection management requires timely detection of diseases. The rapid development of remote sensing technology provides a possibility of spatial continuous monitoring of crop diseases over a large area. In this study, to monitor powdery mildew in winter wheat in an area where a severe disease infection occurred, the capability of high resolution (6 m) multi-spectral satellite imagery, SPOT-6, in disease mapping was assessed and validated using field survey data. Based on a rigorous feature selection process, five disease sensitive spectral features: green band, red band, normalized difference vegetation index, triangular vegetation index, and atmospherically-resistant vegetation index were selected from a group of candidate spectral features/variables. A spectral correction was processed on the selected features to eliminate possible baseline effect across different regions. Then, the disease mapping method was developed based on a spectral angle mapping technique. By validating against a set of field survey data, an overall mapping accuracy of 78 % and kappa coefficient of 0.55 were achieved. Such a moderate but practically acceptable accuracy suggests that the high resolution multispectral satellite image data would be of great potential in crop disease monitoring.

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Section: Introductionmentioning

confidence: 99%

Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale

Yuan

Zhang

et al. 2015

Precision Agric

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“…The results were compared with experimental data, and it was found that the predictions of the ANN model fit the experimental data more accurately in comparison to the various mathematical equations. Wang et al [46] developed a method to spectrally predict late blight infections on tomatoes based on ANN. The ANN was designed as a backpropagation (BP) neural network that used gradient -descent learning algorithm.…”

Section: Ff Tomatoesmentioning

confidence: 99%

Artificial Neural Networks in Fruits: A Comprehensive Review

Goyal¹

2014

IJIGSP

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Abstract-This review discusses the application of artificial neural networks (ANN) modeling in fruits. It covers all fruits in which ANN modeling has been applied. ANN is quite a new and easy computational modeling approach used for prediction, which has become popular and accepted by food industry, researchers, scientists and students. ANNs have been applied in almost every field of science and technology, viz., speech synthesis & recognition, pattern classification, adaptive interfaces between humans & complex physical systems, clustering, function approximation, image data compression, non-linear system modeling, associative memory, combinatorial optimization, control and several others, as they have proved valuable tools for obtaining the required output. ANN provides an exciting alternative method for solving a variety of problems in different areas of science and engineering. The aim of this communication is to discover the recent advances of ANN technology implemented in fruits, and discuss the critical role that ANN plays in predictive modelling.

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“…Feedforward backpropagation neural networks are well established as effective algorithms for use in image classification (e.g., [50][51][52]). Neural networks are especially useful for the mapping of geological materials, since individual geological classes are commonly characterized by substantial variation in reflectance properties as a result of spatial inhomogeneities in mineralogy, degree of chemical alteration, and surface exposure [44,53].…”

Section: Neural Network Classificationmentioning

confidence: 99%

Landsat-TM-Based Discrimination of Lithological Units Associated with the Purtuniq Ophiolite, Quebec, Canada

Leverington

Moon

2012

Remote Sensing

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Abstract:In order to better constrain the utility of multispectral datasets in the characterization of surface materials, Landsat Thematic Mapper (TM) data were evaluated in the discrimination of geological classes in the Cape Smith Belt of Quebec, a greenstone belt that hosts Early Proterozoic units including those of the Purtuniq ophiolite. Ground-based measurements collected for the study area highlight the importance of chemical alteration in controlling the reflectance properties of key geological classes. The spatial distribution of exposed lithologies in the study area was determined through (1) image classification using a feedforward backpropagation neural network classifier; and (2) generation of fraction images for spectral end members using a linear unmixing algorithm and ground reflectance data. Despite some shortcomings, the database of surface cover generated by the neural network classifier is a useful representation of the spatial distribution of exposed geological materials in the study area, with an overall agreement with ground truth of 87.7%. In contrast, the fraction images generated through unmixing are poor representations of ground truth for several key lithological classes. These results underscore both the considerable utility and marked limitations of Landsat TM data in the mapping of igneous and metamorphic lithologies.

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Spectral prediction ofPhytophthora infestansinfection on tomatoes using artificial neural network (ANN)

Cited by 107 publications

References 15 publications

Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale

Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale

Artificial Neural Networks in Fruits: A Comprehensive Review

Landsat-TM-Based Discrimination of Lithological Units Associated with the Purtuniq Ophiolite, Quebec, Canada

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