Model-Based Statistical Features for Mobile Phone Image of Tomato Plant Disease Classification

Hlaing, Chit Su; Zaw, Sai Maung Maung

doi:10.1109/pdcat.2017.00044

Cited by 34 publications

(11 citation statements)

References 10 publications

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“…The author has not used image segmentation in this suggested approach because segmentation causes any lack of image detail during classification 15 . A new model‐based texture function based on histogram matching and goodness of fit results was also proposed by researchers.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The author has not used image segmentation in this suggested approach because segmentation causes any lack of image detail during classification. 15 A new model-based texture function based on histogram matching and goodness of fit results was also proposed by researchers. For scale invariant function transform (SIFT) texture function simulation, the author successfully proposed and applied the GEV (generalized extreme value) probability distribution model.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Recent advances in agricultural disease image recognition technologies: A review

Bondre

Patil

2023

Concurrency and Computation

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In the world of intelligent agriculture, agricultural disease picture detection plays a critical role. Plant disease identification must be efficient if agricultural production is to be increased sustainably. Anomalies in plants affected by viruses, insects, nutritional deficiencies, or poor weather conditions have traditionally been diagnosed by human experts. Deep learning (DL) and transfer learning, two sophisticated machine learning techniques synonymous with the advancement of artificial intelligence technologies in recent years, have started to be used for the identification of agricultural diseases.However, a variety of significant obstacles exist in the way of widespread use of these approaches. This paper looks at DL and transfer learning in particular, and discusses recent developments in using these advanced technologies to recognize agricultural disease images. Many DL architectures have recently been adopted, along with visualization tools, which are critical for identifying signs and classifying plant diseases. The analysis and evaluation of these two approaches show that transfer learning is the better choice given existing agricultural disease data tools. The paper then looks at the key problems that need to be addressed for research in this area to advance, such as image dataset creation, the collection of big data auxiliary domains, and the optimization of the transfer learning process. The construction of technically feasible agricultural disease image recognition systems requires the creation of image datasets collected under real cultivation conditions. This survey aims to learn more about DL capabilities in plant disease detection to increase device efficiency and accuracy in future research.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Recent advances in agricultural disease image recognition technologies: A review

Bondre

Patil

2023

Concurrency and Computation

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“…For the background segmentation, the RGB color spaces (G channel and the difference of G and B channels) and HSI (S channel) were used; where the thresholds are established in a heuristic, adaptive or statistical way (Hlaing & Zaw, 2017;Sharma et al, 2017;Singh & Misra, 2017), however, no error metric is used to evaluate the segmentation result, since it is generally done qualitatively, in Camargo & Smith (2009) two metrics are proposed to measure segmentation results, however the focus of this work is to identify the type of disease that the plant has with an SVM and not its severity percentage. For the detection of the disease, we have worked with the H channel from HSI and HSV, and the I3 channel from I1I2I3, thresholding by means of an intensity histogram, the Otsu method or the channel average, obtaining an accuracy up to 96% compared to other channels such as CR and A from the YCbCr and LAB color spaces respectively, with a low computational cost (Chaudhary et al, 2012;Sharma et al, 2017).…”

Section: State Of the Artmentioning

confidence: 99%

“…In the second approach, shown in Figure 6b, the background extraction is performed with the RGB's G and B channels. This method was proposed by Hlaing & Zaw (2017). In the third approach, shown in Figure 6c, the RGB's G and B and HSI's S channels are used.…”

Section: Background Segmentationmentioning

confidence: 99%

Detection of foliar diseases using image processing techniques

2020

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This paper presents the development of a methodology to detect the percentage of affected area of Phytophthora infestans disease in tomato plants, using digital image processing techniques to extract the regions of interest with color analysis, where the YIQ and TSL models for the detection of the disease. The method consists of solving one of the most common problems in images that is segmentation, in this case the background and the disease with the Plant Village database, which was captured under uncontrolled lighting conditions. In the experiments conducted, it is observed that our method achieved a performance of 98.60% for the detection of healthy pixels and 98.17% in detection of sick pixels. This process was subjected to comparison against other alternatives of the state of the art like K-means with HSV and LAB, showing a referred error regarding the leaf size of 4.32 ± 5.44% in the detection of the disease and a computational time of 0.03 ± 0.01 [s] in comparison with the other procedures, in addition, this methodology was implemented to detect the foliar diseases black Sigatoka and yellow Sigatoka in banana leaves obtaining satisfactory results.

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“…Most research groups have used SVM classifiers to recognise a variety of leaf diseases in crops such as maize [29], tomato [30], chilli [31], potato [32], wheat [33], grapes [34] and rice [35] using red, green and blue (RGB) image data. The technique proposed in [36] achieved almost 89.38% accuracy for corn leaves.…”

Section: Introductionmentioning

confidence: 99%

Identifying Individual Nutrient Deficiencies of Grapevine Leaves Using Hyperspectral Imaging

et al. 2021

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The efficiency of a vineyard management system is directly related to the effective management of nutritional disorders, which significantly downgrades vine growth, crop yield and wine quality. To detect nutritional disorders, we successfully extracted a wide range of features using hyperspectral (HS) images to identify healthy and individual nutrient deficiencies of grapevine leaves. Features such as mean reflectance, mean first derivative reflectance, variation index, mean spectral ratio, normalised difference vegetation index (NDVI) and standard deviation (SD) were employed at various stages in the ultraviolet (UV), visible (VIS) and near-infrared (N.I.R.) regions for our experiment. Leaves were examined visually in the laboratory and grouped as either healthy (i.e. control) or unhealthy. Then, the features of the leaves were extracted from these two groups. In a second experiment, features of individual nutrient-deficient leaves (e.g., N, K and Mg) were also analysed and compared with those of control leaves. Furthermore, a customised support vector machine (SVM) was used to demonstrate that these features can be utilised with a high degree of effectiveness to identify unhealthy samples and not only to distinguish from control and nutrient deficient but also to identify individual nutrient defects. Therefore, the proposed work corroborated that HS imaging has excellent potential to analyse features based on healthiness and individual nutrient deficiencies of grapevine leaves.

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Model-Based Statistical Features for Mobile Phone Image of Tomato Plant Disease Classification

Cited by 34 publications

References 10 publications

Recent advances in agricultural disease image recognition technologies: A review

Recent advances in agricultural disease image recognition technologies: A review

Detection of foliar diseases using image processing techniques

Identifying Individual Nutrient Deficiencies of Grapevine Leaves Using Hyperspectral Imaging

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