A Hybrid Technique for Plant Disease Identification and Localisation in Real-time
Indian Institute of Technology Kanpur（印度理工学院坎普尔分校）；University of Washington（华盛顿大学）；National P.G. College（国立PG学院）；C.S.A. University of Agriculture & Technology（CSA农业与技术大学）
Over the past decade, several image-processing methods and algorithms have been proposed for identifying plant diseases based on visual data. DNN (Deep Neural Networks) have recently become popular for this task. Both traditional image processing and DNN-based methods encounter significant performance issues in real-time detection owing to computational limitations and a broad spectrum of plant disease features. This article proposes a novel technique for identifying and localising plant disease based on the Quad-Tree decomposition of an image and feature learning simultaneously. The proposed algorithm significantly improves accuracy and faster convergence in high-resolution images with relatively low computational load. Hence it is ideal for deploying the algorithm in a standalone processor in a remotely operated image acquisition and disease detection system, ideally mounted on drones and robots working on large agricultural fields. The technique proposed in this article is hybrid as it exploits the advantages of traditional image processing methods and DNN-based models at different scales, resulting in faster inference. The F1 score is approximately 0.80 for four disease classes corresponding to potato and tomato crops.
PlantSeg: A Large-Scale In-the-wild Dataset for Plant Disease Segmentation
The University of Queensland（昆士兰大学）；School of Electrical Engineering and Computer Science, The University of Queensland（昆士兰大学电气工程与计算机科学学院）；School of Agriculture and Food Sustainability, The University of Queensland（昆士兰大学农业与食品可持续发展学院）；CSIRO（联邦科学与工业研究组织）
Region Proposals for Saliency Map Refinement for Weakly-supervised Disease Localisation and Classification
Australian Institute for Machine Learning, The University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；Institute for Systems and Robotics, Instituto Superior Tecnico（高等技术学院系统与机器人研究所）
PND-Net: Plant Nutrition Deficiency and Disease Classification using Graph Convolutional Network
BITS Pilani（毕拉尼理工学院）；Jadavpur University（jadavpur大学）；University of Hradec Kralove（赫拉德茨-克拉洛韦大学）；Skoda Auto University（斯柯达汽车大学）；Universiti Teknologi Malaysia（马来西亚理工大学）
Enhancing Plant Disease Detection: A Novel CNN-Based Approach with Tensor Subspace Learning and HOWSVD-MD
University of Biskra（比斯克拉大学）；University Center of Barika（巴里卡大学中心）；College of Engineering and Information Technology, University of Dubai（迪拜大学工程与信息技术学院）；University of Sharjah（沙迦大学）；Institute of Artificial Intelligence, De Montfort University（德蒙福特大学人工智能学院）
Self-supervised transformer-based pre-training method with General Plant Infection dataset
Key Laboratory of Smart Agriculture Systems, Ministry of Education, China Agricultural University（智能农业系统重点实验室，教育部，中国农业大学）；College of Engineering, China Agricultural University（工程学院，中国农业大学）；College of Economics and Management, China Agricultural University（经济管理学院，中国农业大学）；College of Information and Electrical Engineering, China Agricultural University（信息与电气工程学院，中国农业大学）
Development and evaluation of automated localisation and reconstruction of all fruits on tomato plants in a greenhouse based on multi-view perception and 3D multi-object tracking
An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI
Ahsanullah University of Science and Technology（阿沙努拉科学与技术大学）；Southeast University（东南大学）；Bangladesh University of Engineering and Technology（孟加拉工程与技术大学）
Involution-Infused DenseNet with Two-Step Compression for Resource-Efficient Plant Disease Classification
Department of Computer Science and Engineering, School of Data and Sciences, BRAC University（计算机科学与工程系，数据与科学学院，BRAC大学）；Computing for Sustainability and Social Good (C2SG) Research Group, Department of Computer Science and Engineering, United International University（可持续性与社会公益（C2SG）研究组，计算机科学与工程系，联合国际大学）
Robust Plant Disease Diagnosis with Few Target-Domain Samples
Department of Applied Informatics, Graduate School of Science and Engineering, Hosei University（应用信息系，理工学研究院，恒生大学）；Department of Clinical Plant Science, Faculty of Bioscience and Applied Chemistry, Hosei University（临床植物科学系，生物科学与应用化学学部，恒生大学）
ViTaL: An Advanced Framework for Automated Plant Disease Identification in Leaf Images Using Vision Transformers and Linear Projection For Feature Reduction
An efficient plant disease detection using transfer learning approach
Mohan Babu University（莫汉巴布大学）；University of Nigeria（尼日利亚大学）；Tecnologico de Monterrey（蒙特雷理工学院）；Coal City University（煤炭城大学）；Saveetha Institute of Medical and Technical Sciences（萨维塔医学与技术科学学院）
