Plant diseases serve as one of main threats to food security and crop production. It is thus valuable to exploit recent advances of artificial intelligence to assist plant disease diagnosis. One popular approach is to transform this problem as a leaf image classification task, which can be then addressed by the powerful convolutional neural networks (CNNs). However, the performance of CNN-based classification approach depends on a large amount of high-quality manually labeled training data, which are inevitably introduced noise on labels in practice, leading to model overfitting and performance degradation. To overcome this problem, we propose a novel framework that incorporates rectified meta-learning module into common CNN paradigm to train a noise-robust deep network without using extra supervision information. The proposed method enjoys the following merits: i) A rectified meta-learning is designed to pay more attention to unbiased samples, leading to accelerated convergence and improved classification accuracy. ii) Our method is free on assumption of label noise distribution, which works well on various kinds of noise. iii) Our method serves as a plug-and-play module, which can be embedded into any deep models optimized by gradient descent based method. Extensive experiments are conducted to demonstrate the superior performance of our algorithm over the state-of-the-arts.
The Institute for Sustainable Industries and Liveable Cities (ISILC), Victoria University（维多利亚大学可持续产业与宜居城市研究所（ISILC））；Namal Institute（纳马尔学院）；Charles Sturt University（查尔斯特大学）；Victoria University（维多利亚大学）
In the Agriculture sector, control of plant leaf diseases is crucial as it influences the quality and production of plant species with an impact on the economy of any country. Therefore, automated identification and classification of plant leaf disease at an early stage is essential to reduce economic loss and to conserve the specific species. Previously, to detect and classify plant leaf disease, various Machine Learning models have been proposed; however, they lack usability due to hardware incompatibility, limited scalability and inefficiency in practical usage. Our proposed DeepLens Classi…
Advances in deep learning and transfer learning have paved the way for various automation classification tasks in agriculture, including plant diseases, pests, weeds, and plant species detection. However, agriculture automation still faces various challenges, such as the limited size of datasets and the absence of plant-domain-specific pretrained models. Domain specific pretrained models have shown state of art performance in various computer vision tasks including face recognition and medical imaging diagnosis. In this paper, we propose AgriNet dataset, a collection of 160k agricultural image…
This paper proposes an unsupervised anomaly detection technique for image-based plant disease diagnosis. The construction of large and publicly available datasets containing labeled images of healthy and diseased crop plants led to growing interest in computer vision techniques for automatic plant disease diagnosis. Although supervised image classifiers based on deep learning can be a powerful tool for plant disease diagnosis, they require a huge amount of labeled data. The data mining technique of anomaly detection includes unsupervised approaches that do not require rare samples for training…
Hosei University（法政大学）；Saitama Agricultural Technology Research Center（埼玉县农业技术研究中心）
Automated plant diagnosis using images taken from a distance is often insufficient in resolution and degrades diagnostic accuracy since the important external characteristics of symptoms are lost. In this paper, we first propose an effective pre-processing method for improving the performance of automated plant disease diagnosis systems using super-resolution techniques. We investigate the efficiency of two different super-resolution methods by comparing the disease diagnostic performance on the practical original high-resolution, low-resolution, and super-resolved cucumber images. Our method …
School of Electronics Engineering and Computer Science Peking University（北京大学电子工程与计算机科学学院）；Institute of Sports Medicine Peking University Third Hospital（北京大学第三医院运动医学研究所）
The process of determining which disease or condition explains a person's symptoms and signs can be very complicated and may be inaccurate in some cases. The general belief is that diagnosing diseases relies on doctors' keen intuition, rich experience and professional equipment. In this work, we employ ideas from recent advances in plantar pressure research and from the powerful capacity of the convolutional neural network for learning representations. Here, we propose a model using convolutional neural network based on plantar pressure for medical diagnosis. Our model learns a network that ma…
Bangladesh University of Business and Technology（孟加拉国商业技术大学）
A disease that limits a plant from its maximal capacity is defined as plant disease. From the perspective of agriculture, diagnosing plant disease is crucial, as diseases often limit plants' production capacity. However, manual approaches to recognize plant diseases are often temporal, challenging, and time-consuming. Therefore, computerized recognition of plant diseases is highly desired in the field of agricultural automation. Due to the recent improvement of computer vision, identifying diseases using leaf images of a particular plant has already been introduced. Nevertheless, the most intr…
Hosei University（法政大学）；Saitama Agricultural Technology Research Center（埼玉县农业技术研究中心）
In image-based plant diagnosis, clues related to diagnosis are often unclear, and the other factors such as image backgrounds often have a significant impact on the final decision. As a result, overfitting due to latent similarities in the dataset often occurs, and the diagnostic performance on real unseen data (e,g. images from other farms) is usually dropped significantly. However, this problem has not been sufficiently explored, since many systems have shown excellent diagnostic performance due to the bias caused by the similarities in the dataset. In this study, we investigate this problem…
Hosei University（法政大学）；Saitama Agricultural Technology Research Center（埼玉农业技术研究中心）
The collection of high-resolution training data is crucial in building robust plant disease diagnosis systems, since such data have a significant impact on diagnostic performance. However, they are very difficult to obtain and are not always available in practice. Deep learning-based techniques, and particularly generative adversarial networks (GANs), can be applied to generate high-quality super-resolution images, but these methods often produce unexpected artifacts that can lower the diagnostic performance. In this paper, we propose a novel artifact-suppression super-resolution method that i…
Hosei University（法政大学）；Saitama Agricultural Technology Research Center（埼玉县农业技术研究中心）
Many applications for the automated diagnosis of plant disease have been developed based on the success of deep learning techniques. However, these applications often suffer from overfitting, and the diagnostic performance is drastically decreased when used on test datasets from new environments. In this paper, we propose LeafGAN, a novel image-to-image translation system with own attention mechanism. LeafGAN generates a wide variety of diseased images via transformation from healthy images, as a data augmentation tool for improving the performance of plant disease diagnosis. Thanks to its own…
Ferdowsi University of Mashhad（马什哈德菲尔多西大学）；University of Western Australia（西澳大学）
In the last decades, the area under cultivation of maize products has increased because of its essential role in the food cycle for humans, livestock, and poultry. Moreover, the diseases of plants impact food safety and can significantly reduce both the quality and quantity of agricultural products. There are many challenges to accurate and timely diagnosis of the disease. This research presents a novel scheme based on a deep neural network to overcome the mentioned challenges. Due to the limited number of data, the transfer learning technique is employed with the help of two well-known archit…
Hosei University（法政大学）；Saitama Agricultural Technology Research Center（埼玉县农业技术研究中心）
Practical automated detection and diagnosis of plant disease from wide-angle images (i.e. in-field images containing multiple leaves using a fixed-position camera) is a very important application for large-scale farm management, in view of the need to ensure global food security. However, developing automated systems for disease diagnosis is often difficult, because labeling a reliable wide-angle disease dataset from actual field images is very laborious. In addition, the potential similarities between the training and test data lead to a serious problem of model overfitting. In this paper, we…
Rethinking Plant Disease Diagnosis: Bridging the Academic-Practical Gap with Vision Transformers and Zero-Shot Learning
Department of Computer Science, Mohamed El Bachir El Ibrahimi University（计算机科学系，莫亨德·埃巴希里·埃布拉希米大学）；Laboratory of Physical Chemistry and Biological Materials（物理化学与生物材料实验室）；Laboratory of Informatics and its Applications of M’sila, Mohamed Boudiaf University（信息学及其应用实验室，莫赫梅德·布迪亚夫大学）；Department of Computer Science, Mohamed Boudiaf University（计算机科学系，莫赫梅德·布迪亚夫大学）；Department of agricultural science, Mohamed El Bachir El Ibrahimi University（农业科学系，莫亨德·埃巴希里·埃布拉希米大学）；Laboratoire d’Amélioration et de Développement de la Production Végétale et Animale (LADPVA), University of Ferhat ABBAS (UFAS-Sétif1)（植物和动物生产改良与发展实验室（LADPVA），费哈特·阿巴斯大学（UFAS-Sétif1））
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）
Fusing Perceptual Vision Experts with Multimodal LLMs for Explainable Plant Disease Diagnosis: From Benchmark Imagery to Real-World Robotic Field Validation
Cornell University（康奈尔大学）；University of the Peloponnese（伯罗奔尼撒大学）；Agricultural University of Athens（雅典农业大学）
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）
The Hong Kong University of Science and Technology（香港科技大学）
In recent years, deep learning has vastly improved the identification and diagnosis of various diseases in plants. In this report, we investigate the problem of pathology classification using images of a single leaf. We explore the use of standard benchmark models such as VGG16, ResNet101, and DenseNet 161 to achieve a 0.945 score on the task. Furthermore, we explore the use of the newer EfficientNet model, improving the accuracy to 0.962. Finally, we introduce the state-of-the-art idea of semi-supervised Noisy Student training to the EfficientNet, resulting in significant improvements in both…
Apple diseases, if not diagnosed early, can lead to massive resource loss and pose a serious threat to humans and animals who consume the infected apples. Hence, it is critical to diagnose these diseases early in order to manage plant health and minimize the risks associated with them. However, the conventional approach of monitoring plant diseases entails manual scouting and analyzing the features, texture, color, and shape of the plant leaves, resulting in delayed diagnosis and misjudgments. Our work proposes an ensembled system of Xception, InceptionResNet, and MobileNet architectures to de…
Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The tr…
Automated Plant Disease and Pest Detection System Using Hybrid Lightweight CNN-MobileViT Models for Diagnosis of Indigenous Crops
Department of Computer Science \& Engineering；Department of Information Technology Mekelle University - Mekelle Institute of Technology, Ethiopia Email
Tesla STEM High School（特斯拉STEM高中）；Lockheed Martin Corporation（洛克希德·马丁公司）；Seattle University（西雅图大学）
Automated plant diagnosis is a technology that promises large increases in cost-efficiency for agriculture. However, multiple problems reduce the effectiveness of drones, including the inverse relationship between resolution and speed and the lack of adequate labeled training data. This paper presents a two-step machine learning approach that analyzes low-fidelity and high-fidelity images in sequence, preserving efficiency as well as accuracy. Two data-generators are also used to minimize class imbalance in the high-fidelity dataset and to produce low-fidelity data that is representative of UA…
Precision Synthesis of Multi-Tracer PET via VLM-Modulated Rectified Flow for Stratifying Mild Cognitive Impairment
School of Mathematics and Statistics, Xi'an Jiaotong University（西安交通大学数学与统计学学院）；Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University（教育部生物医学信息工程重点实验室，西安交通大学生命科学与技术学院）；Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University（首都医科大学宣武医院放射科与核医学科）；Research Center for Intelligent Medical Equipment and Devices (IMED), Xi'an Jiaotong University（智能医疗设备与器件研究中心（IMED），西安交通大学）
CNN-Transformer Rectified Collaborative Learning for Medical Image Segmentation
School of Information and Communication Engineering, Dalian University of Technology（大连理工大学信息与通信工程学院）；Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province（辽宁省泛在网络与服务软件重点实验室）；DUT-RU International School of Information Science and Engineering, Dalian University of Technology（大连理工大学-鲁东大学国际信息科学与工程学院）；Affiliated Zhongshan Hospital of Dalian University（大连大学附属中山医院）
Apple orchards in the U.S. are under constant threat from a large number of pathogens and insects. Appropriate and timely deployment of disease management depends on early disease detection. Incorrect and delayed diagnosis can result in either excessive or inadequate use of chemicals, with increased production costs, environmental, and health impacts. We have manually captured 3,651 high-quality, real-life symptom images of multiple apple foliar diseases, with variable illumination, angles, surfaces, and noise. A subset, expert-annotated to create a pilot dataset for apple scab, cedar apple ru…
PDPM Indian Institute of Information Technology, Design and Manufacturing（PDPM印度信息技术设计与制造学院）
Plant diseases are the primary cause of crop losses globally, with an impact on the world economy. To deal with these issues, smart agriculture solutions are evolving that combine the Internet of Things and machine learning for early disease detection and control. Many such systems use vision-based machine learning methods for real-time disease detection and diagnosis. With the advancement in deep learning techniques, new methods have emerged that employ convolutional neural networks for plant disease detection and identification. Another trend in vision-based deep learning is the use of visio…
Plant Doctor: A hybrid machine learning and image segmentation software to quantify plant damage in video footage
Waseda University（早稻田大学）；Kyoto Prefecture Momoyama High School（京都府立桃山高等学校）；Kyushu University（九州大学）；The University of Tokyo Hospital（东京大学医学部附属医院）；Ryukoku University（龙谷大学）
Many existing techniques provide automatic estimation of crop damage due to various diseases. However, early detection can prevent or reduce the extend of damage itself. The limited performance of existing techniques in early detection is lack of localized information. We instead propose a dataset with annotations for each diseased segment in each image. Unlike existing approaches, instead of classifying images into either healthy or diseased, we propose to provide localized classification for each segment of an images. Our method is based on Mask RCNN and provides location as well as extend o…
Makerere University（麦克雷雷大学）；National Crops Resources Research Institute（国家作物资源研究所）
Pests and diseases pose a key challenge to passion fruit farmers across Uganda and East Africa in general. They lead to loss of investment as yields reduce and losses increases. As the majority of the farmers, including passion fruit farmers, in the country are smallholder farmers from low-income households, they do not have the sufficient information and means to combat these challenges. While, passion fruits have the potential to improve the well-being of these farmers as they have a short maturity period and high market value , without the required knowledge about the health of their crops,…
Fuzhou University（福州大学）；Minjiang University（闽江学院）；South China University of Technology（华南理工大学）
The automatic detection of skin diseases via dermoscopic images can improve the efficiency in diagnosis and help doctors make more accurate judgments. However, conventional skin disease recognition systems may produce high confidence for out-of-distribution (OOD) data, which may become a major security vulnerability in practical applications. In this paper, we propose a multi-scale detection framework to detect out-of-distribution skin disease image data to ensure the robustness of the system. Our framework extracts features from different layers of the neural network. In the early layers, rec…
Dynamic Meta-Ensemble Framework for Efficient and Accurate Deep Learning in Plant Leaf Disease Detection on Resource-Constrained Edge Devices
South West University Science and Technology, School of Computer Science and Technology（西南科技大学计算机科学与技术学院）；South West University Science and Technology, School of Control and Information Engineering Technology（西南科技大学控制与信息工程学院）
LeafNet: A Large-Scale Dataset and Comprehensive Benchmark for Foundational Vision-Language Understanding of Plant Diseases
School of Electrical Engineering, Korea University（韩国大学电气工程学院）；Department of Integrative Biology, The University of Texas at Austin（德克萨斯大学奥斯汀分校整合生物学系）；Department of Software Engineering, FPT University（FPT大学软件工程系）
Emory University（埃默里大学）；Stony Brook University（石溪大学）；Georgia State University（佐治亚州立大学）
Liver steatosis is known as the abnormal accumulation of lipids within cells. An accurate quantification of steatosis area within the liver histopathological microscopy images plays an important role in liver disease diagnosis and trans-plantation assessment. Such a quantification analysis often requires a precise steatosis segmentation that is challenging due to abundant presence of highly overlapped steatosis droplets. In this paper, a deep learning model Mask-RCNN is used to segment the steatosis droplets in clumps. Extended from Faster R-CNN, Mask-RCNN can predict object masks in addition …
Crop diseases are a major threat to food security and their rapid identification is important to prevent yield loss. Swift identification of these diseases are difficult due to the lack of necessary infrastructure. Recent advances in computer vision and increasing penetration of smartphones have paved the way for smartphone-assisted disease identification. Most of the plant diseases leave particular artifacts on the foliar structure of the plant. This study was conducted in 2020 at Department of Computer Science and Engineering, University of Engineering and Technology, Lahore, Pakistan to che…
AgroVisNet: A lightweight Convolutional Network and the BD-PlantDX Expert-Validated Benchmark for Radish, Potato and Pointed Gourd Disease Classification
Bangladesh Army University of Science & Technology（孟加拉国陆军科技大学）；Rajshahi University of Engineering & Technology（拉杰沙希工程技术大学）
SugarcaneShuffleNet: A Very Fast, Lightweight Convolutional Neural Network for Diagnosis of 15 Sugarcane Leaf Diseases
Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka 1000, Bangladesh（机器人与机电工程系，达卡大学，达卡1000，孟加拉国）；GIS and Remote Sensing Lab, Gazipur Agricultural University, Gazipur 1706, Bangladesh（地理信息系统与遥感实验室，加兹ipur农业大学，加兹ipur 1706，孟加拉国）；Bangladesh Sugarcrop Research Institute, Ishwardi, Pabna, Bangladesh（孟加拉国甘蔗作物研究 institute，Ishwardi，Pabna，孟加拉国）；Department of Computer Science and Engineering, University of Dhaka, Dhaka 1000, Bangladesh（计算机科学与工程系，达卡大学，达卡1000，孟加拉国）
