Crops Leaf Disease Recognition From Digital and RS Imaging Using Fusion of Multi Self-Attention RBNet Deep Architectures and Modified Dragonfly Optimization

计算机科学 高光谱成像 人工智能 深度学习 过程(计算) 模式识别(心理学) 机器学习 操作系统
作者
Irfan Haider,Muhammad Attique Khan,Muhammad Nazir,Ameer Hamza,Omar Alqahtani,M. Turki-Hadj Alouane,Anum Masood
出处
期刊:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:17: 7260-7277 被引量:3
标识
DOI:10.1109/jstars.2024.3378298
摘要

Globally, pests and plant diseases severely threaten forestry and agriculture. Plant protection could be substantially enhanced by using non-contact, extremely effective, and reasonably priced techniques for identifying and tracking pests and plant diseases across large geographic areas. Precision agriculture is the study of using other technologies, such as hyperspectral remote sensing (RS), to increase cultivation instead of traditional agricultural methods with less negative environmental effects. In this work, we proposed a novel deep-learning architecture and optimization algorithm for crop leaf disease recognition. In the initial step, a multilevel contrast enhancement technique is proposed for a better visual of the disease on the leaves of cotton and wheat. After that, we proposed three novel residual block and self-attention mechanisms named 3-RBNet Self, 5-RBNet Self, and 9-RBNet Self. After that, the proposed models are trained on enhanced images and later extracted deep features from the self-attention layer. The 5-RBNET Self and 9-RBNET Self performed well in terms of accuracy and precision rate; therefore, we did not consider the 3-RBNET Self for the next process. The dragonfly optimization algorithm is proposed for the best feature selection and applied to the self-attention features of 5-RBNET Self and 9-RBNET Self models to improve the classification performance further and reduce the computational cost. The proposed method is evaluated on two publically available crop disease images, such as the Cotton, Wheat, and EuroSAT datasets. For both crops, the proposed method obtained a maximum accuracy of 98.60 and 93.90%, respectively, whereas for the EuroSAT, the proposed method obtained an accuracy of 83.10%. Compared to the results with recent techniques, the proposed method shows improved accuracy and precision rate.
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