Identifying Two of Tomatoes Leaf Viruses Using Support Vector Machine

Mokhtar, Usama; Ali, Mona A. S.; Hassanien, Aboul Ella; Hefny, Hesham A.

doi:10.1007/978-81-322-2250-7_77

Cited by 76 publications

(26 citation statements)

References 17 publications

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“…In addition, traditional approaches to disease classification via machine learning typically focus on a small number of classes usually within a single crop. Examples include a feature extraction and classification pipeline using thermal and stereo images in order to classify tomato powdery mildew against healthy tomato leaves (Raza et al, 2015 ); the detection of powdery mildew in uncontrolled environments using RGB images (Hernández-Rabadán et al, 2014 ); the use of RGBD images for detection of apple scab (Chéné et al, 2012 ) the use of fluorescence imaging spectroscopy for detection of citrus huanglongbing (Wetterich et al, 2012 ) the detection of citrus huanglongbing using near infrared spectral patterns (Sankaran et al, 2011 ) and aircraft-based sensors (Garcia-Ruiz et al, 2013 ) the detection of tomato yellow leaf curl virus by using a set of classic feature extraction steps, followed by classification using a support vector machines pipeline (Mokhtar et al, 2015 ), and many others. A very recent review on the use of machine learning on plant phenotyping (Singh et al, 2015 ) extensively discusses the work in this domain.…”

Section: Discussionmentioning

confidence: 99%

Using Deep Learning for Image-Based Plant Disease Detection

2016

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Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global scale.

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Section: Discussionmentioning

confidence: 99%

Using Deep Learning for Image-Based Plant Disease Detection

2016

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“…Similarly, in sugar beet (Beta vulgaris L.), early identification of three diseases, Cercospora leaf spot, leaf rust, and powdery mildew, was performed using SVM with a radial basis function as kernel [38]. The same idea was deployed in cotton to identify damage by green stink bug, bacterial angular blight, and Ascochyta blight using a SVM classifier [39], as well as in tomato (Solanum lycopersicum L.) to identify viruses: tomato yellow leaf curl virus and tomato yellow leaf curl disease [40].…”

Section: Approaches To Icqpmentioning

confidence: 99%

Machine Learning for High-Throughput Stress Phenotyping in Plants

Singh

Ganapathysubramanian

Sarkar

2016

Trends in Plant Science

736

472

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Advances in automated and high-throughput imaging technologies have resulted in a deluge of high-resolution images and sensor data of plants. However, extracting patterns and features from this large corpus of data requires the use of machine learning (ML) tools to enable data assimilation and feature identification for stress phenotyping. Four stages of the decision cycle in plant stress phenotyping and plant breeding activities where different ML approaches can be deployed are (i) identification, (ii) classification, (iii) quantification, and (iv) prediction (ICQP). We provide here a comprehensive overview and user-friendly taxonomy of ML tools to enable the plant community to correctly and easily apply the appropriate ML tools and best-practice guidelines for various biotic and abiotic stress traits.

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“…Different approaches to disease detection and classification via machine learning in tomato crops have been analysed. First, using RGB images and different machine learning algorithms (SVM, linear kernel, quadratic kernel (QK), radial basis function (RBF), multilayer perceptron (MLP), and polynomial kernel), tomato yellow leaf curl disease (TYLCD) is detected [11]. This approach obtained accuracy of 90 % in average.…”

Section: Related Workmentioning

confidence: 99%

A Benchmarking of Learning Strategies for Pest Detection and Identification on Tomato Plants for Autonomous Scouting Robots Using Internal Databases

et al. 2019

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Greenhouse crop production is growing throughout the world and early pest detection is of particular importance in terms of productivity and reduction of the use of pesticides. Conventional eye observation methods are nonefficient for large crops. Computer vision and recent advances in deep learning can play an important role in increasing the reliability and productivity. This paper presents the development and comparison of two different approaches for vision based automated pest detection and identification, using learning strategies. A solution that combines computer vision and machine learning is compared against a deep learning solution. The main focus of our work is on the selection of the best approach based on pest detection and identification accuracy. The inspection is focused on the most harmful pests on greenhouse tomato and pepper crops, Bemisia tabaci and Trialeurodes vaporariorum. A dataset with a huge number of infected tomato plants images was created to generate and evaluate machine learning and deep learning models. The results showed that the deep learning technique provides a better solution because (a) it achieves the disease detection and classification in one step, (b) gets better accuracy, (c) can distinguish better between Bemisia tabaci and Trialeurodes vaporariorum, and (d) allows balancing between speed and accuracy by choosing different models.

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Identifying Two of Tomatoes Leaf Viruses Using Support Vector Machine

Cited by 76 publications

References 17 publications

Using Deep Learning for Image-Based Plant Disease Detection

Using Deep Learning for Image-Based Plant Disease Detection

Machine Learning for High-Throughput Stress Phenotyping in Plants

A Benchmarking of Learning Strategies for Pest Detection and Identification on Tomato Plants for Autonomous Scouting Robots Using Internal Databases

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