Unmanned aerial vehicles assisted rice seedling detection using shark smell optimization with deep learning model
Tài liệu tham khảo
Gilanie, 2021, RiceNet: convolutional neural networks-based model to classify Pakistani grown rice seed types, Multimedia Syst., 27, 867, 10.1007/s00530-021-00760-2
Yang, 2020, A near real-time deep learning approach for detecting rice phenology based on UAV images, Agricult. Forest Meteorol., 287, 10.1016/j.agrformet.2020.107938
Margapuri, 2021, Seed classification using synthetic image datasets generated from low-altitude UAV imagery, 116
Dilmurat, 2022, Estimating crop seed composition using machine learning from multisensory UAV data, Remote Sens., 14, 4786, 10.3390/rs14194786
Kumar, 2021, Fungal blast disease detection in rice seed using machine learning, Int. J. Adv. Comput. Sci. Appl., 12
Conrad, 2020, Machine learning-based presymptomatic detection of rice sheath blight using spectral profiles, Plant Phenom., 2020, 10.34133/2020/8954085
Tan, 2022, Machine learning approaches for rice seedling growth stages detection, Front. Plant Sci., 13, 10.3389/fpls.2022.914771
F. Liao, X. Feng, Z. Li, D. Wang, C. Xu, G. Chu, H. Ma, Q. Yao, S. Chen, A Spatio-Temporal Conval Neural Network Model for Rice Nutrient Level Diagnosis at Rice Panicle Initiation Stage. Available at SSRN 4272680.
Muharam, 2021, UAV-and random-forest-AdaBoost (RFA)-based estimation of rice plant traits, Agronomy, 11, 915, 10.3390/agronomy11050915
Yamaguchi, 2020, Feasibility of combining deep learning and RGB images obtained by unmanned aerial vehicle for leaf area index estimation in rice, Remote Sens., 13, 84, 10.3390/rs13010084
Anuar, 2022, Aerial imagery paddy seedlings inspection using deep learning, Remote Sens., 14, 274, 10.3390/rs14020274
Tseng, 2022, Rice seedling detection in UAV images using transfer learning and machine learning, Remote Sens., 14, 2837, 10.3390/rs14122837
Yang, 2021, A UAV open dataset of rice paddies for deep learning practice, Remote Sens., 13, 1358, 10.3390/rs13071358
Wu, 2019, Automatic counting of in situ rice seedlings from UAV images based on a deep fully convolutional neural network, Remote Sens., 11, 691, 10.3390/rs11060691
Wang, 2021, Recognition of rice seedling rows based on row vector grid classification, Comput. Electron. Agric., 190, 10.1016/j.compag.2021.106454
Nguyen-Quoc, 2020, Rice seed image classification based on HOG descriptor with missing values imputation, TELKOMNIKA (Telecommun. Comput. Electron. Control), 18, 1897, 10.12928/telkomnika.v18i4.14069
Ma, 2019, Fully convolutional network for rice seedling and weed image segmentation at the seedling stage in paddy fields, PLoS One, 14, 10.1371/journal.pone.0215676
Nirmalapriya, 2023, Fractional aquila spider monkey optimization based deep learning network for classification of brain tumor, Biomed. Signal Process. Control, 79, 10.1016/j.bspc.2022.104017
Yu, 2021, NestNet: A multiscale convolutional neural network for remote sensing image change detection, Int. J. Remote Sens., 42, 4898, 10.1080/01431161.2021.1906982
Manjunath, 2022, Backward movement oriented shark smell optimization-based audio steganography using encryption and compression strategies, Digit. Signal Process., 122, 10.1016/j.dsp.2021.103335
Shanmuganathan, 2022, Deep learning LSTM recurrent neural network model for prediction of electric vehicle charging demand, Sustainability, 14, 10207, 10.3390/su141610207
https://github.com/aipal-nchu/RiceSeedlingDataset.