Local descriptors for soybean disease recognition

Pires, Rillian Diello Lucas; Gonçalves, Diogo Nunes; Oruê, Jonatan Patrick Margarido; Kanashiro, Wesley Eiji Sanches; Rodrigues, José Fernando; Machado, Bruno Brandoli; Gonçalves, Wesley Nunes

doi:10.1016/j.compag.2016.04.032

Cited by 78 publications

(34 citation statements)

References 28 publications

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“…Color histogram detects the diseased area of the image where other features are combined to generate the classification [9]. To classify and detect the diseased parts in the leaf images various methods like SIFT, HOG, Pyramid histogram word, Dense SIFT are compared [10]. SFTA extraction is composed based on the wavelet transformation, where image is decomposed into multiple sub bands using wavelet transformation and Texture feature is extracted using SFTA algorithm [11].…”

Section: Related Workmentioning

confidence: 99%

Automatic Detection of Plant Disease and Insect Attack using EFFTA Algorithm

Gangadharan¹,

Rosline²,

Dhanasekaran³

et al. 2020

IJACSA

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The diagnosis of plant disease by computer vision using digital image processing methodology is a key for timely intervention and treatment of healthy agricultural procedure and to increase the yield by natural means. Timely addressal of these ailments can be the difference between the prevention and perishing of an ecosystem. To make the system more efficient and feasible we have proposed an algorithm called Enhanced Fusion Fractal Texture Analysis (EFFTA). The proposed method consists of Feature Fusion technique which combines SIFT-Scale Invariant Feature Transform and DWT-Discrete Wavelet Transform based SFTA-Segment Based Fractal Texture Analysis. Image as a whole can be detected by shape, texture and color. SIFT is used to detect the texture feature, it extracts the set of descriptors that is very useful in local texture recognition and it captures accurate key points for detecting the diseased area. Further extraction of texture is considered and that can be performed by WSFTA method. It adopts intra-class analysis and inter-class analysis. Extracted features trained using Back Propagation Neural Network. It improves and expands the success rate and accuracy of extraction also it provides higher precision and efficiency when compared to the other traditional methods.

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Section: Related Workmentioning

confidence: 99%

Automatic Detection of Plant Disease and Insect Attack using EFFTA Algorithm

Gangadharan¹,

Rosline²,

Dhanasekaran³

et al. 2020

IJACSA

View full text Add to dashboard Cite

show abstract

“…Spatial grids of SIFT descriptors is also used in that study. As for disease detection, a set of three soybean diseases are classified in [25] on scanned leaves. Best results are achieved using a multiscale grid in the form of the Pyramid Histogram of Words (PHOW) method.…”

Section: Related Workmentioning

confidence: 99%

Comparison of SIFT Encoded and Deep Learning Features for the Classification and Detection of Esca Disease in Bordeaux Vineyards

et al. 2018

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Grapevine wood fungal diseases such as esca are among the biggest threats in vineyards nowadays. The lack of very efficient preventive (best results using commercial products report 20% efficiency) and curative means induces huge economic losses. The study presented in this paper is centered around the in-field detection of foliar esca symptoms during summer, exhibiting a typical "striped" pattern. Indeed, in-field disease detection has shown great potential for commercial applications and has been successfully used for other agricultural needs such as yield estimation. Differentiation with foliar symptoms caused by other diseases or abiotic stresses was also considered. Two vineyards from the Bordeaux region (France, Aquitaine) were chosen as the basis for the experiment. Pictures of diseased and healthy vine plants were acquired during summer 2017 and labeled at the leaf scale, resulting in a patch database of around 6000 images (224 × 224 pixels) divided into red cultivar and white cultivar samples. Then, we tackled the classification part of the problem comparing state-of-the-art SIFT encoding and pre-trained deep learning feature extractors for the classification of database patches. In the best case, 91% overall accuracy was obtained using deep features extracted from MobileNet network trained on ImageNet database, demonstrating the efficiency of simple transfer learning approaches without the need to design an ad-hoc specific feature extractor. The third part aimed at disease detection (using bounding boxes) within full plant images. For this purpose, we integrated the deep learning base network within a "one-step" detection network (RetinaNet), allowing us to perform detection queries in real time (approximately six frames per second on GPU). Recall/Precision (RP) and Average Precision (AP) metrics then allowed us to evaluate the performance of the network on a 91-image (plants) validation database. Overall, 90% precision for a 40% recall was obtained while best esca AP was about 70%. Good correlation between annotated and detected symptomatic surface per plant was also obtained, meaning slightly symptomatic plants can be efficiently separated from severely attacked plants.

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“…In this work, the LibSVM library was used (Chang & Lin, 2011) for SVM classifiers with linear (L-SVM) and chi-square kernel (Chi-SVM) functions. Cross-validation partitions the image dataset into complementary folds for ensuring each fold has the same proportion of each class (Montoliu et al, 2015;Pires et al, 2016). The SVM classifier needs to adjust some internal parameters, so a 10-fold crossvalidation with 10 repetitions was used to evaluate the entire dataset in this study (Wen et al, 2009;Olgun et al, 2016).…”

Section: Classificationmentioning

confidence: 99%

“…In another study by applying dense SIFT features and SVM, 40 different wheat grain varieties were classified with a satisfactory accuracy rate (Olgun et al, 2016). Pires et al (2016) introduced a high accuracy method based on image local descriptors and SVM classifier for detecting soybean disease.…”

Section: Introductionmentioning

confidence: 99%