Plant disease detection is a huge problem and often require professional help to detect the disease. This research focuses on creating a deep learning model that detects the type of disease that affected the plant from the images of the leaves of the plants. The deep learning is done with the help of Convolutional Neural Network by performing transfer learning. The model is created using transfer learning and is experimented with both resnet 34 and resnet 50 to demonstrate that discriminative learning gives better results. This method achieved state of art results for the dataset used. The main goal is to lower the professional help to detect the plant diseases and make this model accessible to as many people as possible.
Indian Institute of Technology Gandhinagar（甘地纳格尔印度理工学院）
India loses 35% of the annual crop yield due to plant diseases. Early detection of plant diseases remains difficult due to the lack of lab infrastructure and expertise. In this paper, we explore the possibility of computer vision approaches for scalable and early plant disease detection. The lack of availability of sufficiently large-scale non-lab data set remains a major challenge for enabling vision based plant disease detection. Against this background, we present PlantDoc: a dataset for visual plant disease detection. Our dataset contains 2,598 data points in total across 13 plant species …
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 …
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）
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…
The agriculture sector is essential for every country because it provides a basic income to a large number of people and food as well, which is a fundamental requirement to survive on this planet. We see as time passes, significant changes come in the present era, which begins with Green Revolution. Due to improper knowledge of plant diseases, farmers use fertilizers in excess, which ultimately degrade the quality of food. Earlier farmers use experts to determine the type of plant disease, which was expensive and time-consuming. In today time, Image processing is used to recognize and catalog …
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…
Deakin-SWU Joint Research Centre on Big Data（迪肯大学-西南大学大数据联合研究中心）；Deakin University（迪肯大学）
Current annotation for plant disease images depends on manual sorting and handcrafted features by agricultural experts, which is time-consuming and labour-intensive. In this paper, we propose a self-supervised clustering framework for grouping plant disease images based on the vulnerability of Kernel K-means. The main idea is to establish a cross iterative under-clustering algorithm based on Kernel K-means to produce the pseudo-labeled training set and a chaotic cluster to be further classified by a deep learning module. In order to verify the effectiveness of our proposed framework, we conduc…
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…
Plant disease detection is an essential factor in increasing agricultural production. Due to the difficulty of disease detection, farmers spray various pesticides on their crops to protect them, causing great harm to crop growth and food standards. Deep learning can offer critical aid in detecting such diseases. However, it is highly inconvenient to collect a large volume of data on all forms of the diseases afflicting a specific plant species. In this paper, we propose a new metrics-based few-shot learning SSM net architecture, which consists of stacked siamese and matching network components…
Plant diseases are considered one of the main factors influencing food production and minimize losses in production, and it is essential that crop diseases have fast detection and recognition. The recent expansion of deep learning methods has found its application in plant disease detection, offering a robust tool with highly accurate results. In this context, this work presents a systematic review of the literature that aims to identify the state of the art of the use of convolutional neural networks(CNN) in the process of identification and classification of plant diseases, delimiting trends…
Indian Institute of Information Technology Allahabad（阿拉哈巴德印度信息技术学院）
Plant leaf diseases pose a significant danger to food security and they cause depletion in quality and volume of production. Therefore accurate and timely detection of leaf disease is very important to check the loss of the crops and meet the growing food demand of the people. Conventional techniques depend on lab investigation and human skills which are generally costly and inaccessible. Recently, Deep Neural Networks have been exceptionally fruitful in image classification. In this research paper, plant leaf disease detection employing transfer learning is explored in the JPEG compressed dom…
University of Engineering and Technology Lahore（拉合尔工程技术大学）
Identification of plant disease is usually done through visual inspection or during laboratory examination which causes delays resulting in yield loss by the time identification is complete. On the other hand, complex deep learning models perform the task with reasonable performance but due to their large size and high computational requirements, they are not suited to mobile and handheld devices. Our proposed approach contributes automated identification of plant diseases which follows a sequence of steps involving pre-processing, segmentation of diseased leaf area, calculation of features ba…
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…
USTHB University（USTHB大学）；University of Mons（蒙斯大学）；Mohamed El Bachir El Ibrahimi University（穆罕默德·巴希尔·易卜拉希米大学）；Setif 1 University（塞蒂夫第一大学）
Recently, many works have been inspired by the success of deep learning in computer vision for plant diseases classification. Unfortunately, these end-to-end deep classifiers lack transparency which can limit their adoption in practice. In this paper, we propose a new trainable visualization method for plant diseases classification based on a Convolutional Neural Network (CNN) architecture composed of two deep classifiers. The first one is named Teacher and the second one Student. This architecture leverages the multitask learning to train the Teacher and the Student jointly. Then, the communi…
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…
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…
One of the important and tedious task in agricultural practices is the detection of the disease on crops. It requires huge time as well as skilled labor. This paper proposes a smart and efficient technique for detection of crop disease which uses computer vision and machine learning techniques. The proposed system is able to detect 20 different diseases of 5 common plants with 93% accuracy.
Fr. Conceicao Rodrigues Institute of Technology, Vashi（康塞桑·罗德里格斯神父理工学院，瓦希）
Crop failure owing to pests & diseases are inherent within Indian agriculture, leading to annual losses of 15 to 25% of productivity, resulting in a huge economic loss. This research analyzes the performance of various optimizers for predictive analysis of plant diseases with deep learning approach. The research uses Convolutional Neural Networks for classification of farm or plant leaf samples of 3 crops into 15 classes. The various optimizers used in this research include RMSprop, Adam and AMSgrad. Optimizers Performance is visualised by plotting the Training and Validation Accuracy and Loss…
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）
Chonbuk National University（韩国全北国立大学）；Mokpo National University（韩国木浦国立大学）
Acquisition of data in task-specific applications of machine learning like plant disease recognition is a costly endeavor owing to the requirements of professional human diligence and time constraints. In this paper, we present a simple pipeline that uses GANs in an unsupervised image translation environment to improve learning with respect to the data distribution in a plant disease dataset, reducing the partiality introduced by acute class imbalance and hence shifting the classification decision boundary towards better performance. The empirical analysis of our method is demonstrated on a li…
Our overarching goal is to develop an accurate and explainable model for plant disease identification using hyperspectral data. Charcoal rot is a soil borne fungal disease that affects the yield of soybean crops worldwide. Hyperspectral images were captured at 240 different wavelengths in the range of 383 - 1032 nm. We developed a 3D Convolutional Neural Network model for soybean charcoal rot disease identification. Our model has classification accuracy of 95.73\% and an infected class F1 score of 0.87. We infer the trained model using saliency map and visualize the most sensitive pixel locati…
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）
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…
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…
This paper presents a framework which uses computer vision algorithms to standardise images and analyse them for identifying crop diseases automatically. The tools are created to bridge the information gap between farmers, advisory call centres and agricultural experts using the images of diseased/infected crop captured by mobile-phones. These images are generally sensitive to a number of factors including camera type and lighting. We therefore propose a technique for standardising the colour of plant images within the context of the advisory system. Subsequently, to aid the advisory process, …
Harbin Institute of Technology（哈尔滨工业大学）；Peng Cheng Laboratory（鹏城实验室）；Peking University（北京大学）
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 performanc…
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）
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）
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…
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,…
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）
MobilePlantViT: A Mobile-friendly Hybrid ViT for Generalized Plant Disease Image Classification
University of Asia Pacific（亚太大学）；Bangladesh University of Business and Technology（孟加拉国商业与技术大学）；Techno International New Town（科技国际新城大学）；American International University-Bangladesh（孟加拉国美国国际大学）
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）
