Computer Science And Technology - Research Publications

Permanent URI for this collectionhttps://kr.cup.edu.in/handle/32116/82

Browse

Author: search.filters.author.Kumar, MunishSubject: search.filters.subject.CNN

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    CottonLeafNet: cotton plant leaf disease detection using deep neural networks
    (Springer, 2023-03-18T00:00:00) Singh, Paramjeet; Singh, Parvinder; Farooq, Umar; Khurana, Surinder Singh; Verma, Jitendra Kumar; Kumar, Munish
    India is a cover crop region whereby agricultural production sustains a substantial proportion of the populace and upon which the whole Indian economy is heavily reliant. As per research, it provides subsistence for around 70% of rural households. In terms of agricultural output and exports, India ranks second and ninth, respectively. However, it accomplishes the first position globally in terms of cotton exports thereby adequately contributing to the economy of the country. However, it has been documented that various crops especially cotton plants are severely harmed by various pests, extreme climatic variations, nutrient inadequacy and toxicity, and so on. Cotton plant diseases cause a wide range of illnesses ranging from bacterial to nutritional deficiency giving a hard time for the human eye to recognize. However, most of the researchers have considered only a few types of cotton leaf diseases and excluded many. Keeping these constraints in consideration, this research seeks to aid the detection of these diseases by employing deep learning paradigms. The research begins with acquiring a near-balanced dataset with 22 leaf disease types including bacterial, fungal, viral, nutrient deficiency, etc. followed by data augmentation to boost the performance of the models. Many algorithms were tested, however, CNN happens to be very efficient and productive. The proposed model when evaluated on the test set achieves an accuracy of 99.39% with a negligible error rate, thus outperforming all the existing approaches by consuming less computational time. The outcome portrays that the proposed approach has the efficiency to be implemented in real-time detection systems to aid the precise detection of cotton leaf diseases to help the farmers in taking appropriate actions. � 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
  • No Thumbnail Available
    Item
    An efficient approach for copy-move image forgery detection using convolution neural network
    (Springer, 2022-02-17T00:00:00) Koul, Saboor; Kumar, Munish; Khurana, Surinder Singh; Mushtaq, Faisel; Kumar, Krishan
    Digital imaging has become elementary in this novel era of technology with unconventional image forging techniques and tools. Since, we understand that digital image forgery is possible, it cannot be even presented as a piece of evidence anywhere. Dissecting this fact, we must dig unfathomable into the issue to help alleviate such derelictions. Copy-move and splicing of images to create a forged one prevail in this monarchy of digitalization. Copy-move involves copying one part of the image and pasting it to another part of the image while the latter involves merging of two images to significantly change the original image and create a new forged one. In this article, a novel slant using a convolutional neural network (CNN) has been proposed for automatic detection of copy-move forgery detection. For the experimental work, a benchmark dataset namely, MICC-F2000 is considered which consists of 2000 images in which 1300 are original and 700 are forged. The experimental results depict that the proposed model outperforms the other traditional methods for copy-move forgery detection. The results of copy-move forgery were highly promising with an accuracy of 97.52% which is 2.52% higher than the existing methods. � 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.