An Intelligent Hybrid Experimental-Based Deep Learning Algorithm for Tomato-Sorting Controllers

Haggag, Mayar; Abdelhay, Samaa; Mecheter, Asma; Gowid, Samer; Musharavati, Farayi; Ghаni, Sаud

doi:10.1109/access.2019.2932730

Cited by 35 publications

(15 citation statements)

References 20 publications

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“…7 shows the whole NLB detection model based on multi-input RPN network and multi-scale fusion. The model consists of the improved RPN network [32], [33] and transmission module, a total of 11 layers, and the Softmax is adopted to be the classifier [34]. The RPN network replaces the original classification layer with the multi-scale feature input network (conv1, conv2, conv3, conv4) for anchor fine-tuning, and use the 4-layer convolution (conv5, conv6, conv7, conv8) as the detection layer.…”

Section: E Multi-input Rpn Network Combined With Multi-scale Fusion mentioning

confidence: 99%

Northern Maize Leaf Blight Detection Under Complex Field Environment Based on Deep Learning

et al. 2020

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Northern maize leaf blight is one of the major diseases that endanger the health of maize. The complex background of the field and different light intensity make the detection of diseases more difficult. A multi-scale feature fusion instance detection method, based on convolutional neural network, is proposed to detect maize leaf blight. The proposed technique incorporates three major steps of data set preprocessing part, fine-tuning network and detection module. In the first step, the improved retinex is used to process data sets, which successfully solves the problem of poor detection effects caused by high-intensity light. In the second step, the improved RPN is utilized to adjust the anchor box of diseased leaves. The improved RPN network identifies and deletes negative anchors, which reduces the search space of the classifier and provides better initial information for the detection network. In this paper, a transmission module is designed to connect the fine-tuning network with the detection module. On the one hand, the transmission module fuses the features of the low-level and high-level to improve the detection accuracy of small target diseases. On the other hand, the transmission module converts the feature map associated with the fine-tuning network to the detection module, thus realizing the feature sharing between the detection module and the fine-tuning network. In the third step, the detection module takes the optimized anchor as input, focuses on detecting the diseased leaves. By sharing the features of the transmission module, the time-consuming process of using candidate regions layer by layer to detect is eliminated. Therefore, the efficiency of the whole model has reached the efficiency of the one-stage model. In order to further optimize the detection effect of the model, we replace the loss function with generalized intersection over union (GIoU). After 60000 iterations, the highest mean average precision (mAP) reaches 91.83%. The experimental results indicate that the improved model outperforms several existing methods in terms of greater precision and frames per second (FPS). INDEX TERMS Northern maize leaf blight, disease detection, transmission module, retinex, single shot multiBox detector (SSD). I. INTRODUCTION Maize is one of the major food crops in the world. The planting area and output of maize in the world are only lower than that of wheat and rice [1]. In addition to be an excellent feed for animal husbandry, maize is also an important raw material for the development of light industrial products. The associate editor coordinating the review of this manuscript and approving it for publication was Liandong Zhu.

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Section: E Multi-input Rpn Network Combined With Multi-scale Fusion mentioning

confidence: 99%

Northern Maize Leaf Blight Detection Under Complex Field Environment Based on Deep Learning

et al. 2020

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“…The hybrid CNN-ANN based algorithm is observed superior during the validation and testing. The classification accuracy for the ripe and defective sample is found to be 90% [8]. Basri et.al [9] proposes a faster R-CNN method based on deep learning for multi-fruit detection using MobileNet model on TensorFlow platform.…”

Section: Previous Workmentioning

confidence: 99%

Automatic Detection and Classification of Nutrients Deficiency in Fruit Based on Automated Machine Learning

Yogesh*,

Dubey,

Ratan

2019

IJEAT

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Machine learning-based classification and detection of surface defect of fruit involve manual feature identification and selection from input datasets. Deep learning discovers the useful features from the input data. This approach simplifies the training of the neural network and makes them faster. The selection of useful patterns from the fruit features results in better accuracy. The number of layers represents the depth of the model. Neural network provides learning to the model. As the dataset contains many features. It is obvious that all features are not relevant to the system. The proposed system learns from these features by identifying the pattern and select the relevant features. This is the most crucial phase of the machine learning to identify the appropriate features to make the system faster and accurate. In this paper, we propose solving fruit surface defect detection using Automated Machine Learning (AML). The outcome is the prediction of the fruit surface defect in terms of probability due to nutrient deficiency

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“…Mayar Haggag and et.al. [2] introduced a hybrid CNN-ANN based algorithm and computational results are used to access the real time performance of the proposed algorithm. Systematic Mapping system [3] highlighted the challenges and research opportunities in the field of cast sorting.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of Child Items in a Gold Tree using Support Vector Machine Classifier

Sabeenian*,

Paramasivam,

et al. 2019

IJRTE

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Sorting of images has been a challenge in Machine Learning Algorithms over the years. Various algorithms have been proposed to sort an image but none of them are able to sort the image clearly. The drawback of the existing systems is that the sorted image is not clearly identified. So, to overcome this drawback we have proposed a novel approach to sort the children of a tree and match them with the existing designs. The images will be sorted on the basis of the class of the image. The images are taken from the image and manual binning of those images are done. Then the images are trained and tested. GLCM feature is extracted from the trained and tested images which are later on fed to the SVM classifier. The classification of image is then done with the help of SVM classifier. Around 7000 images are trained on SVM and used for classification. More than 300 different classes have been created in the database for comparison. Realtime images of child items are captured and fed to the SVM for classifying. The main application of this image is the use in distinguishing the designs in the ornaments. The various parts of the ornaments can be differentiated clearly. Thus, the proposed method is precise as compared to the existing methods.

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An Intelligent Hybrid Experimental-Based Deep Learning Algorithm for Tomato-Sorting Controllers

Cited by 35 publications

References 20 publications

Northern Maize Leaf Blight Detection Under Complex Field Environment Based on Deep Learning

Northern Maize Leaf Blight Detection Under Complex Field Environment Based on Deep Learning

Automatic Detection and Classification of Nutrients Deficiency in Fruit Based on Automated Machine Learning

Classification of Child Items in a Gold Tree using Support Vector Machine Classifier

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