Sadia Alam Shammi^1,2, Yanbo Huang^1,*, Weiwei Xie^1,2, Gary Feng¹, Haile Tewolde¹, Xin Zhang³, Johnie Jenkins¹, Mark Shankle⁴

Phyton-International Journal of Experimental Botany, Vol.94, No.9, pp. 2745-2766, 2025, DOI:10.32604/phyton.2025.068955 - 30 September 2025

Abstract Crop leaf area index (LAI) and biomass are two major biophysical parameters to measure crop growth and health condition. Measuring LAI and biomass in field experiments is a destructive method. Therefore, we focused on the application of unmanned aerial vehicles (UAVs) in agriculture, which is a cost and labor-efficient method. Hence, UAV-captured multispectral images were applied to monitor crop growth, identify plant bio-physical conditions, and so on. In this study, we monitored soybean crops using UAV and field experiments. This experiment was conducted at the MAFES (Mississippi Agricultural and Forestry Experiment Station) Pontotoc Ridge-Flatwoods Branch… More >

Jeng-Shyang Pan^1,2, Wenda Li¹, Shu-Chuan Chu^1,*, Xiao Sui¹, Junzo Watada³

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3033-3062, 2024, DOI:10.32604/cmc.2024.056310 - 18 November 2024

Abstract The growth optimizer (GO) is an innovative and robust metaheuristic optimization algorithm designed to simulate the learning and reflective processes experienced by individuals as they mature within the social environment. However, the original GO algorithm is constrained by two significant limitations: slow convergence and high memory requirements. This restricts its application to large-scale and complex problems. To address these problems, this paper proposes an innovative enhanced growth optimizer (eGO). In contrast to conventional population-based optimization algorithms, the eGO algorithm utilizes a probabilistic model, designated as the virtual population, which is capable of accurately replicating the… More >

Jingcheng Zhang¹, Xingjian Zhou¹, Dong Shen¹, Qimeng Yu¹, Lin Yuan^2,*, Yingying Dong³

Phyton-International Journal of Experimental Botany, Vol.93, No.4, pp. 745-762, 2024, DOI:10.32604/phyton.2024.049734 - 29 April 2024

Abstract As an important rice disease, rice bacterial leaf blight (RBLB, caused by the bacterium Xanthomonas oryzae pv. oryzae), has become widespread in east China in recent years. Significant losses in rice yield occurred as a result of the disease’s epidemic, making it imperative to monitor RBLB at a large scale. With the development of remote sensing technology, the broad-band sensors equipped with red-edge channels over multiple spatial resolutions offer numerous available data for large-scale monitoring of rice diseases. However, RBLB is characterized by rapid dispersal under suitable conditions, making it difficult to track the disease at… More >

Lujuan Deng, Ruochong Fu^*, Zuhe Li, Boyi Liu, Mengze Xue, Yuhao Cui

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4071-4089, 2024, DOI:10.32604/cmc.2024.048200 - 26 March 2024

Abstract Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion scenarios. However, while continuously improving cross-modal feature extraction and fusion, ensuring the model’s detection speed is also a challenging issue. We have devised a deep learning network model for cross-modal pedestrian detection based on Resnet50, aiming to focus on more reliable features and enhance the model’s detection efficiency. This model employs a spatial attention mechanism to reweight the input visible light and infrared image data, enhancing the model’s focus on different More >

N. A. Lawrance^*, T. S. Shiny Angel

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3439-3455, 2023, DOI:10.32604/csse.2023.030311 - 03 April 2023

Abstract The practice of integrating images from two or more sensors collected from the same area or object is known as image fusion. The goal is to extract more spatial and spectral information from the resulting fused image than from the component images. The images must be fused to improve the spatial and spectral quality of both panchromatic and multispectral images. This study provides a novel picture fusion technique that employs L0 smoothening Filter, Non-subsampled Contour let Transform (NSCT) and Sparse Representation (SR) followed by the Max absolute rule (MAR). The fusion approach is as follows: More >

Lin Yuan¹, Qimeng Yu¹, Yao Zhang^2,*, Xiaochang Wang³, Ouguan Xu¹, Wenjing Li¹

Phyton-International Journal of Experimental Botany, Vol.92, No.3, pp. 747-761, 2023, DOI:10.32604/phyton.2023.025502 - 29 November 2022

Abstract Thosea sinensis Walker (TSW) rapidly spreads and severely damages the tea plants. Therefore, finding a reliable operational method for identifying the TSW-damaged areas via remote sensing has been a focus of a research community. Such methods also enable us to calculate the precise application of pesticides and prevent the subsequent spread of the pests. In this work, based on the unmanned aerial vehicle (UAV) platform, five band images of multispectral red-edge camera were obtained and used for monitoring the TSW in tea plantations. By combining the minimum redundancy maximum relevance (mRMR) with the selected spectral features,… More >

M. Rajakani^1,*, R. J. Kavitha², A. Ramachandran³

Computer Systems Science and Engineering, Vol.44, No.1, pp. 265-280, 2023, DOI:10.32604/csse.2023.024994 - 01 June 2022

Abstract Feature extraction is the most critical step in classification of multispectral image. The classification accuracy is mainly influenced by the feature sets that are selected to classify the image. In the past, handcrafted feature sets are used which are not adaptive for different image domains. To overcome this, an evolutionary learning method is developed to automatically learn the spatial-spectral features for classification. A modified Firefly Algorithm (FA) which achieves maximum classification accuracy with reduced size of feature set is proposed to gain the interest of feature selection for this purpose. For extracting the most efficient More >

Sidharth Samanta¹, Mrutyunjaya Panda¹, Somula Ramasubbareddy², S. Sankar³, Daniel Burgos^4,*

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1937-1948, 2021, DOI:10.32604/cmc.2021.014406 - 13 April 2021

Abstract Earth surveillance through aerial images allows more accurate identification and characterization of objects present on the surface from space and airborne platforms. The progression of deep learning and computer vision methods and the availability of heterogeneous multispectral remote sensing data make the field more fertile for research. With the evolution of optical sensors, aerial images are becoming more precise and larger, which leads to a new kind of problem for object detection algorithms. This paper proposes the “Sliding Region-based Convolutional Neural Network (SRCNN),” which is an extension of the Faster Region-based Convolutional Neural Network (RCNN) More >

Yanyun Jiang¹, Yuanjie Zheng^1,2,*, Xiaodan Sui¹, Wanzhen Jiao³, Yunlong He⁴, Weikuan Jia¹

Computer Systems Science and Engineering, Vol.36, No.3, pp. 537-549, 2021, DOI:10.32604/csse.2021.014578 - 18 January 2021

Abstract Multispectral imaging (MSI) technique is often used to capture images of the fundus by illuminating it with different wavelengths of light. However, these images are taken at different points in time such that eyeball movements can cause misalignment between consecutive images. The multispectral image sequence reveals important information in the form of retinal and choroidal blood vessel maps, which can help ophthalmologists to analyze the morphology of these blood vessels in detail. This in turn can lead to a high diagnostic accuracy of several diseases. In this paper, we propose a novel semi-supervised end-to-end deep… More >

Anju Asokan¹, J. Anitha¹, Bogdan Patrut², Dana Danciulescu³, D. Jude Hemanth^1,*

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 373-388, 2021, DOI:10.32604/cmc.2020.012364 - 30 October 2020

Abstract Multispectral images contain a large amount of spatial and spectral data which are effective in identifying change areas. Deep feature extraction is important for multispectral image classification and is evolving as an interesting research area in change detection. However, many deep learning framework based approaches do not consider both spatial and textural details into account. In order to handle this issue, a Convolutional Neural Network (CNN) based multi-feature extraction and fusion is introduced which considers both spatial and textural features. This method uses CNN to extract the spatio-spectral features from individual channels and fuse them More >