Dimensionality Reduction Using Rough Set Approach for Two Neural Networks-Based Applications

Sammany, Mohammed; Medhat, T.

doi:10.1007/978-3-540-73451-2_67

Cited by 21 publications

(9 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…El-Helly et al [10] used an artificial neural network to better recognize cucumber powdery mildew, downy mildew and leaves damaged by leaf dips. Sammany and Medhat [11] employed genetic algorithms to optimize neural networks and support vector machines for recognizing plant disease images. Baum et al [12] conducted disease recognition on barley and used edge detection [13] to separate the diseased area from the background area.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Object Detection Improvement for Tomato Disease

2020

View full text Add to dashboard Cite

To improve the recognition model accuracy of crop disease leaves and locating diseased leaves, this paper proposes an improved Faster RCNN to detect healthy tomato leaves and four diseases: powdery mildew, blight, leaf mold fungus and ToMV. First, we use a depth residual network to replace VGG16 for image feature extraction so we can obtain deeper disease features. Second, the k-means clustering algorithm is used to cluster the bounding boxes. We improve the anchoring according to the clustering results. The improved anchor frame tends toward the real bounding box of the dataset. Finally, we carry out a k-means experiment with three kinds of different feature extraction networks. The experimental results show that the improved method for crop leaf disease detection had 2.71% higher recognition accuracy and a faster detection speed than the original Faster RCNN.

show abstract

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Object Detection Improvement for Tomato Disease

2020

View full text Add to dashboard Cite

show abstract

“…In 2005, Liu et al [7] used back propagation neural networks to predict the occurrences of diseases and insect pests of apple trees using data of the previous 11 years. In 2007, Sammany and Medhat [8] used genetic algorithms to optimize the structure and parameters of neural networks and then used support vector machines and neural networks to identify plant diseases. In 2008, Tellaeche et al [9] identified weeds between rows of crops in the field by using Hough transform and Gabor filtering based on the perspective of geometry principle of the scene and solved the weed identification problem under different perspectives and different spatial frequencies.…”

Section: Related Workmentioning

confidence: 99%

Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques

Wang

Feng

Sun

et al. 2019

Computational Intelligence and Neuroscience

135

View full text Add to dashboard Cite

This study develops tomato disease detection methods based on deep convolutional neural networks and object detection models. Two different models, Faster R-CNN and Mask R-CNN, are used in these methods, where Faster R-CNN is used to identify the types of tomato diseases and Mask R-CNN is used to detect and segment the locations and shapes of the infected areas. To select the model that best fits the tomato disease detection task, four different deep convolutional neural networks are combined with the two object detection models. Data are collected from the Internet and the dataset is divided into a training set, a validation set, and a test set used in the experiments. The experimental results show that the proposed models can accurately and quickly identify the eleven tomato disease types and segment the locations and shapes of the infected areas.

show abstract

“…In 1999, some of the authors employed genetic algorithms to identify the condition by establishing identi cation factors based on some characteristics such as spectrum re ection and form [14]. In 2007, the authors suggested some machine learning models after the use of neural network models and suggest some parameters for support vector machines and genetic algorithms [15]. In 2011, researchers diagnosed the cotton leaf features based on color and spots which are then combined and discriminated with original images [16].…”

Section: Literature Studymentioning

confidence: 99%

Comparative Analysis of Segmentation Models to Detect Leaf Diseases in Tomato Plant

Harnal

Gautam

Singh

et al. 2022

Preprint

View full text Add to dashboard Cite

In agriculture around 22% of crop yield loss is due to living and non-living organisms such as biotic and abiotic stress/disease. The early-stage diagnosis of these stresses is an important issue for farmers through naked eyes. Using computer vision technologies can detect the pattern and clustering of diseases at an early stage. However, inthe last few years, computer vision-based deep learning technology is helpful for the diagnosis of biotic stress (single biotic and multi biotic) in tomato plant leaves.In this work, the PlantVillage dataset is gathered for the segmentation of object detection. The labeled, enhanced and augmented data has been used for training the model. The proposed hybrid Deep Segmentation Convolutional Neural Network (Hybrid-DSCNN)model has been segmenting the diseased objects in the tomato plant. This Hybrid-DSCNN is assembled usingU-Net and Seg-Net pre-trained models with instance segmentation for better detection of objects. The result of the proposed Hybrid DSCNN model is compared with other modified U-Net, M-SegNet, and modified U-SegNet in terms of Accuracy, Precision, Recall, Intersection over Union (IoU), and mean Intersection over Union (mIoU). The proposed model processed 1004 images in 30ns,whichis better than other compared models. The accuracy achieved using the proposed model is 98.24%,which is far better than other modified segmentation models. In this paper, the semantic segmented data has been recognized for the single and multiple leaf diseases for identification and classification.

show abstract

Dimensionality Reduction Using Rough Set Approach for Two Neural Networks-Based Applications

Cited by 21 publications

References 7 publications

Deep Learning-Based Object Detection Improvement for Tomato Disease

Deep Learning-Based Object Detection Improvement for Tomato Disease

Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques

Comparative Analysis of Segmentation Models to Detect Leaf Diseases in Tomato Plant

Contact Info

Product

Resources

About