An efficient adaptive feature selection with deep learning model-based paddy plant leaf disease classification

被引:0
作者
Ratnesh Kumar Dubey
Dilip Kumar Choubey
机构
[1] Indian Institute of Information Technology Bhagalpur,Department of Computer Science and Engineering
来源
Multimedia Tools and Applications | 2024年 / 83卷
关键词
Paddy leaf plant; Deep learning; Median filter; SV-RFE; ABi-LSTM; Rain optimization algorithm;
D O I
暂无
中图分类号
学科分类号
摘要
Agriculture is the essential source of national income for some nations including India. Infections in crops/plants are serious causes of reduced quantity and quality of production, resulting in economic loss. Therefore, the detection of diseases in crops is very essential. Plant disease symptoms are evident in different parts of plants. However, plant leaves are commonly used to diagnose infection. Therefore, in this paper, we focus on automatic leaf disease detection using the deep learning model. The presentedmodel consists of four phases namely, pre-processing, feature extraction, feature selection, and classification. At first, the captured paddy leaf images are converted into an RGB color modelthe median filter is used to remove the noise present in the green band. Then, the texture and color features are extracted from the green band. After the feature extraction, important features are selected using a combination of machine learning and optimization algorithm. Here, initially, the features are selected using support vector machine-recursive feature elimination (SV-RFE) and an adaptive rain optimization algorithm (ARO). Then, the common features are selected. The selected features are given to the adaptive bi-long short-term memory (ABi-LSTM) classifier to classify an image as Blast disease, Bacterial Leaf Blight disease, Tungro, or normal image. The efficiency of the presented technique is estimatedbased on the accuracy, sensitivity, specificity, and performance compared with state-of-the-art works.
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页码:22639 / 22661
页数:22
相关论文
共 44 条
[1]  
Azim MA(2021)An effective feature extraction method for rice leaf disease classification TELKOMNIKA (Telecommunication Computing Electronics and Control) 19 463-470
[2]  
Islam MK(2022)Optimized Convolutional Neural Network for Road Detection with Structured Contour and Spatial Information Int J Pattern Recognit Artif Intell 36 2252002-7317
[3]  
Rahman M(2023)Lane detection in intelligent vehicle system using optimal 2-tier deep convolutional neural network Multimed Tools Appl 82 7293-424
[4]  
Jahan F(2022)Plant nutrition under climate change and soil carbon sequestration Sustainability 14 914-1686
[5]  
Dewangan DK(2021)Patterns of agricultural diversification in China and its policy implications for agricultural modernization Int J Environ Res Public Health 18 4978-523
[6]  
Sahu SP(2019)A review of plant leaf fungal diseases and its environment speciation Bioengineered 10 409-94
[7]  
Dewangan DK(2020)Zinc-based nanomaterials for diagnosis and management of plant diseases: Ecological safety and future prospects J Fungi 6 222-undefined
[8]  
Sahu SP(2021)Impact of climate change on agriculture and its mitigation strategies: A review Sustainability 13 1318-undefined
[9]  
Elbasiouny H(2021)Identification of Paddy Leaf Diseases using Evolutionary and Machine Learning Methods Turkish J Comput Mathem Educ 12 1672-undefined
[10]  
El-Ramady H(2018)Plant leaf disease detection and classification using image processing Int J Res Eng 5 516-undefined