Adaptive snake optimization-enabled deep learning-based multi-classification using leaf images

被引:0
作者
Vineeta Singh
Vandana Dixit Kaushik
机构
[1] Chhatrapati Shahu Ji Maharaj University,Department of Computer Science and Engineering, School of Engineering and Technology (UIET)
[2] Harcourt Butler Technical University,Department of Computer Science and Engineering
来源
Signal, Image and Video Processing | 2024年 / 18卷
关键词
Plant leaf classification; Disease detection; Deep learning; Convolution neural network; Adaptive snake optimizer;
D O I
暂无
中图分类号
学科分类号
摘要
Plants role is crucial for the preservation of earth's environment and ecology by maintaining healthy surroundings. Because of their proximity during the entire year, the leaves are crucial for identifying a plant. Plant’s diseases may disturb the leaf in between planting and the harvesting stage, resulting a significant loss in crop production and commercial benefit. As a result, leaf disease detection is difficult task in the field of agriculture. However, it necessitates a large workforce, additional processing time, and good knowledge of plant diseases. To address this problem, an optimized deep learning (DL) approach is employed for leaf type classification as well as disease detection. Initially, the leaf image is preprocessed with anisotropic filtering before being segmented using Mask-R-CNN. The multiclassification of leaf type and disease detection is carried out here. In leaf type classification process, the SqueezeNet with adaptive snake optimizer (ASO) is used. In disease detection process, the deep Qnet with proposed ASO is utilized. The proposed ASO-SqueezeNet technique performed better in classification process, with accuracy, precision, sensitivity, specificity and F-measure are 0.924, 0.888, 0.930, 0.935, and 0.909. Furthermore, the performance of ASO-deep Qnet in disease detection process is 0.919, 0.870, 0.922, 0.924, and 0.911, respectively.
引用
收藏
页码:3043 / 3052
页数:9
相关论文
共 50 条
[41]   Classification of brain tumours from MRI images using deep learning-enabled hybrid optimization algorithm [J].
Raju, Sudhakar ;
Veera, Venkateswara Rao Peddireddy .
NETWORK-COMPUTATION IN NEURAL SYSTEMS, 2023, :408-437
[42]   Deep learning-based classification of eye diseases using Convolutional Neural Network for OCT images [J].
Elkholy, Mohamed ;
Marzouk, Marwa A. .
FRONTIERS IN COMPUTER SCIENCE, 2024, 5
[43]   Deep Learning-Based Faults Detection and Classification in Photovoltaic Systems Using Voltage and Current Images [J].
Alsudi, Izziyyah M. ;
Abulaila, Mohammad ;
Al-Aubidy, Kasim M. .
JORDAN JOURNAL OF ELECTRICAL ENGINEERING, 2024, 10 (04) :500-519
[44]   A Deep Learning-Based Weld Defect Classification Method Using Radiographic Images With a Cylindrical Projection [J].
Chang, Yasheng ;
Wang, Weiku .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2021, 70
[45]   DeepCerviCancer-Deep Learning-Based Cervical Image Classification using Colposcopy and Cytology Images [J].
Kalbhor M. ;
Shinde S. ;
Lahade S. ;
Choudhury T. .
EAI Endorsed Transactions on Pervasive Health and Technology, 2023, 9 (01)
[46]   Deep Learning Based Grapevine Leaf Classification Using Augmented Images and Multi-Classifier Fusion for Improved Accuracy and Precision [J].
Elkassar, Ahmed .
2024 14TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING, ICEENG 2024, 2024, :190-192
[47]   Multi-classification of Alzheimer's Disease by NSGA-II Slices Optimization and Fusion Deep Learning [J].
Rojas-Valenzuela, Ignacio ;
Rojas, Ignacio ;
Delgado-Marquez, Elvira ;
Valenzuela, Olga .
ARTIFICIAL LIFE AND EVOLUTIONARY COMPUTATION, WIVACE 2023, 2024, 1977 :284-297
[48]   Deep Learning-based Plant Classification: VGG16 and Leaf Morphology [J].
Kant, Vishnu .
2024 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT CYBER PHYSICAL SYSTEMS AND INTERNET OF THINGS, ICOICI 2024, 2024, :752-756
[49]   Automated machine learning-based radiomics analysis versus deep learning-based classification for thyroid nodule on ultrasound images: a multi-center study [J].
Liu, Zelong ;
Deyer, Louisa ;
Yang, Arnold ;
Liu, Steven ;
Gong, Jingqi ;
Yang, Yang ;
Huang, Mingqian ;
Doshi, Amish ;
Lu, Meng ;
Lee, Denise ;
Deyer, Timothy ;
Mei, Xueyan .
2022 IEEE 22ND INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE 2022), 2022, :23-28
[50]   Diagnosis and multi-classification of lung diseases in CXR images using optimized deep convolutional neural network [J].
Ashwini, S. ;
Arunkumar, J. R. ;
Prabu, R. Thandaiah ;
Singh, Ngangbam Herojit ;
Singh, Ngangbam Phalguni .
SOFT COMPUTING, 2023, 28 (7-8) :6219-6233