Self-Supervised Leaf Segmentation under Complex Lighting Conditions
Deakin University（迪肯大学）；Lancaster University（兰卡斯特大学）；The University of Warwick（华威大学）；South China University of Technology（华南理工大学）；Medigrowth Australia Pty Ltd（澳大利亚Medigrowth私人有限公司）
Xufeng Lin, Chang-Tsun Li, Scott Adams, Abbas Kouzani, Richard Jiang, Ligang He, Yongjian Hu, Michael Vernon, Egan Doeven, Lawrence Webb, Todd Mcclellan, Adam Guskic
As an essential prerequisite task in image-based plant phenotyping, leaf segmentation has garnered increasing attention in recent years. While self-supervised learning is emerging as an effective alternative to various computer vision tasks, its adaptation for image-based plant phenotyping remains rather unexplored. In this work, we present a self-supervised leaf segmentation framework consisting of a self-supervised semantic segmentation model, a color-based leaf segmentation algorithm, and a self-supervised color correction model. The self-supervised semantic segmentation model groups the semantically similar pixels by iteratively referring to the self-contained information, allowing the pixels of the same semantic object to be jointly considered by the color-based leaf segmentation algorithm for identifying the leaf regions. Additionally, we propose to use a self-supervised color correction model for images taken under complex illumination conditions. Experimental results on datasets of different plant species demonstrate the potential of the proposed self-supervised framework in achieving effective and generalizable leaf segmentation.
India is an agriculture-dependent country. As we all know that farming is the backbone of our country it is our responsibility to preserve the crops. However, we cannot stop the destruction of crops by natural calamities at least we have to try to protect our crops from diseases. To, detect a plant disease we need a fast automatic way. So, this paper presents a model to identify the particular disease of plant leaves at early stages so that we can prevent or take a remedy to stop spreading of the disease. This proposed model is made into five sessions. Image preprocessing includes the enhancem…
AgentPolyp: Accurate Polyp Segmentation via Image Enhancement Agent
School of Mathematics, Shandong University（山东大学数学学院）；School of Information Science and Engineering, Shandong Normal University（山东师范大学信息科学与工程学院）；Yeshiva University（叶史瓦大学）；School of cyber science and technology, Sun Yat-sen University（中山大学网络空间安全学院）
Robotics and Autonomous Systems The Commonwealth Scientific and Industrial Research Organisation (CSIRO), Data61 Brisbane, Australia（澳大利亚联邦科学与工业研究组织（CSIRO）数据61部布里斯班机器人与自主系统部（Robotics and Autonomous Systems））
Automated segmentation of individual leaves of a plant in an image is a prerequisite to measure more complex phenotypic traits in high-throughput phenotyping. Applying state-of-the-art machine learning approaches to tackle leaf instance segmentation requires a large amount of manually annotated training data. Currently, the benchmark datasets for leaf segmentation contain only a few hundred labeled training images. In this paper, we propose a framework for leaf instance segmentation by augmenting real plant datasets with generated synthetic images of plants inspired by domain randomisation. We…
China Agricultural University（中国农业大学）；University of California, Berkeley（加州大学伯克利分校）；LuDong University（鲁东大学）
Leaf segmentation is the most direct and effective way for high-throughput plant phenotype data analysis and quantitative researches of complex traits. Currently, the primary goal of plant phenotyping is to raise the accuracy of the autonomous phenotypic measurement. In this work, we present the LeafMask neural network, a new end-to-end model to delineate each leaf region and count the number of leaves, with two main components: 1) the mask assembly module merging position-sensitive bases of each predicted box after non-maximum suppression (NMS) and corresponding coefficients to generate origi…
We present an approach to leaf level segmentation of images of Arabidopsis thaliana plants based upon detected edges. We introduce a novel approach to edge classification, which forms an important part of a method to both count the leaves and establish the leaf area of a growing plant from images obtained in a high-throughput phenotyping system. Our technique uses a relatively shallow convolutional neural network to classify image edges as background, plant edge, leaf-on-leaf edge or internal leaf noise. The edges themselves were found using the Canny edge detector and the classified edges can…
This paper proposes a novel framework for fluorescence plant video processing. The plant research community is interested in the leaf-level photosynthetic analysis within a plant. A prerequisite for such analysis is to segment all leaves, estimate their structures, and track them over time. We identify this as a joint multi-leaf segmentation, alignment, and tracking problem. First, leaf segmentation and alignment are applied on the last frame of a plant video to find a number of well-aligned leaf candidates. Second, leaf tracking is applied on the remaining frames with leaf candidate transform…
Leaf vein forms the basis of leaf characterization and classification. Different species have different leaf vein patterns. It is seen that leaf vein segmentation will help in maintaining a record of all the leaves according to their specific pattern of veins thus provide an effective way to retrieve and store information regarding various plant species in database as well as provide an effective means to characterize plants on the basis of leaf vein structure which is unique for every species. The algorithm proposes a new way of segmentation of leaf veins with the use of Odd Gabor filters and…
LeafInst - Unified Instance Segmentation Network for Fine-Grained Forestry Leaf Phenotype Analysis: A New UAV based Benchmark
organization= College of Information Science；Technology \& Artificial Intelligence, Nanjing Forestry University , city= Nanjing , postcode= 210037 , state= Jiangsu , country= China；organization= Department of Geography, College of Natural Resources；organization= Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University , city= Nanjing , postcode= 210037 , state= Jiangsu , country= China；organization= School of Architecture \& Urban Planning, Shenzhen University , city= Shenzhen , postcode= 518060 , state= Guangdong , country= China；organization= School of Software, Hefei University of Technology , city= Hefei , postcode= 230601 , state= Anhui , country= China
Plant phenotyping tasks such as leaf segmentation and counting are fundamental to the study of phenotypic traits. Since it is well-suited for these tasks, deep supervised learning has been prevalent in recent works proposing better performing models at segmenting and counting leaves. Despite good efforts from research groups, one of the main challenges for proposing better methods is still the limitation of labelled data availability. The main efforts of the field seem to be augmenting existing limited data sets, and some aspects of the modelling process have been under-discussed. This paper e…
College of Information Sciences and Technology, Donghua University（东华大学信息科学与技术学院）；Engineering Research Center of Digitized Textile & Fashion Technology, Ministry of Education, Donghua University（东华大学数字化纺织服装技术教育部工程研究中心）
Automatic leaf segmentation, as well as identification and classification methods that built upon it, are able to provide immediate monitoring for plant growth status to guarantee the output. Although 3D plant point clouds contain abundant phenotypic features, plant leaves are usually distributed in clusters and are sometimes seriously overlapped in the canopy. Therefore, it is still a big challenge to automatically segment each individual leaf from a highly crowded plant canopy in 3D for plant phenotyping purposes. In this work, we propose an overlapping-free individual leaf segmentation meth…
College of Information and Electrical Engineering, Shenyang Agricultural University（沈阳农业大学信息与电气工程学院）；School of Information and Intelligence Engineering, University of Sanya（三亚学院信息与智能工程学院）
Nowadays, there are many approaches to acquire three-dimensional (3D) point clouds of maize plants. However, automatic stem-leaf segmentation of maize shoots from three-dimensional (3D) point clouds remains challenging, especially for new emerging leaves that are very close and wrapped together during the seedling stage. To address this issue, we propose an automatic segmentation method consisting of three main steps: skeleton extraction, coarse segmentation based on the skeleton, fine segmentation based on stem-leaf classification. The segmentation method was tested on 30 maize seedlings and …
Fruit size and leaf area are important indicators for plant health and are of interest for plant nutrient management, plant protection and harvest. In this research, an image-based method for measuring the fruit volume as well as the leaf area for cabbage is presented. For this purpose, a mask region-based convolutional neural network (Mask R-CNN) was trained to segment the cabbage fruit from the leaves and assign it to the corresponding plant. The results indicated that even with a single camera, the developed method can provide a calculation accuracy of fruit size of 92.6% and an accuracy of…
LEAF: Latent Diffusion with Efficient Encoder Distillation for Aligned Features in Medical Image Segmentation
School of Computer Science and Engineering, Sun Yat-sen University, China（计算机科学与工程学院，中山大学）；Department of Biomedical Engineering, Johns Hopkins University, United States（生物医学工程系，约翰霍普金斯大学）
Hierarchical Approach for Joint Semantic, Plant Instance, and Leaf Instance Segmentation in the Agricultural Domain
University of Bonn（波恩大学）；University of Oxford（牛津大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）；La Sapienza University of Rome（罗马第一大学）
Deep learning techniques involving image processing and data analysis are constantly evolving. Many domains adapt these techniques for object segmentation, instantiation and classification. Recently, agricultural industries adopted those techniques in order to bring automation to farmers around the globe. One analysis procedure required for automatic visual inspection in this domain is leaf count and segmentation. Collecting labeled data from field crops and greenhouses is a complicated task due to the large variety of crops, growth seasons, climate changes, phenotype diversity, and more, espe…
Precision agriculture is area with lack of cheap technology. The refinement of the production system brings large advantages to the producer and the use of images makes the monitoring a more cheap methodology. Macronutrients monitoring can to determine the health and vulnerability of the plant in specific stages. In this paper is analyzed the method based on computational intelligence to work with image segmentation in the identification of symptoms of plant nutrient deficiency. Artificial neural networks are evaluated for image segmentation and filtering, several variations of parameters and …
Image segmentation is the most challenging issue in computer vision applications. And most difficulties for crops management in agriculture are the lack of appropriate methods for detecting the leaf damage for pests treatment. In this paper we proposed an automatic method for leaf damage detection and severity estimation of coffee leaf by avoiding defoliation. After enhancing the contrast of the original image using LUT based gamma correction, the image is processed to remove the background, and the output leaf is clustered using Fuzzy c-means segmentation in V channel of YUV color space to ma…
University Of Edinburgh（爱丁堡大学）；IMT Lucca（卢卡IMT高等研究院）
The number of leaves a plant has is one of the key traits (phenotypes) describing its development and growth. Here, we propose an automated, deep learning based approach for counting leaves in model rosette plants. While state-of-the-art results on leaf counting with deep learning methods have recently been reported, they obtain the count as a result of leaf segmentation and thus require per-leaf (instance) segmentation to train the models (a rather strong annotation). Instead, our method treats leaf counting as a direct regression problem and thus only requires as annotation the total leaf co…
UFES - Federal University of Espirito Santo（圣埃斯皮里图联邦大学）；Incaper（圣埃斯皮里图州农业研究所）
Generally, the identification and classification of plant diseases and/or pests are performed by an expert . One of the problems facing coffee farmers in Brazil is crop infestation, particularly by leaf rust Hemileia vastatrix and leaf miner Leucoptera coffeella. The progression of the diseases and or pests occurs spatially and temporarily. So, it is very important to automatically identify the degree of severity. The main goal of this article consists on the development of a method and its i implementation as an App that allow the detection of the foliar damages from images of coffee leaf tha…
We describe a new measure for the evaluation of region level segmentation of objects, as applied to evaluating the accuracy of leaf-level segmentation of plant images. The proposed approach enforces the rule that a region (e.g. a leaf) in either the image being evaluated or the ground truth image evaluated against can be mapped to no more than one region in the other image. We call this measure the subset-matched Jaccard index.
In Computer Vision, colour-based spatial techniquesoften assume a static skin colour model. However, skin colour perceived by a camera can change when lighting changes. In common real environment multiple light sources impinge on the skin. Moreover, detection techniques may vary when the image under study is taken under different lighting condition than the one that was earlier under consideration. Therefore, for robust skin pixel detection, a dynamic skin colour model that can cope with the changes must be employed. This paper shows that skin pixel detection in a digital colour image can be s…
Athena Research and Innovation Center（雅典娜研究与创新中心）；Institute of Mediterranean & Forest Ecosystems（地中海与森林生态系统研究所）
Plant phenotyping refers to a quantitative description of the plants properties, however in image-based phenotyping analysis, our focus is primarily on the plants anatomical, ontogenetical and physiological properties.This technique reinforced by the success of Deep Learning in the field of image based analysis is applicable to a wide range of research areas making high-throughput screens of plants possible, reducing the time and effort needed for phenotypic characterization.In this study, we use Deep Learning methods (supervised and unsupervised learning based approaches) to semantically segm…
We introduce a labeling tool and dataset aimed to facilitate computer vision research in agriculture. The annotation tool introduces novel methods for labeling with a variety of manual, semi-automatic, and fully-automatic tools. The dataset includes original images collected from commercial greenhouses, images from PlantVillage, and images from Google Images. Images were annotated with segmentations for foreground leaf, fruit, and stem instances, and diseased leaf area. Labels were in an extended COCO format. In total the dataset contained 10k tomatoes, 7k leaves, 2k stems, and 2k diseased lea…
Stony Brook University（石溪大学）；Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）
Automated analysis of optical colonoscopy (OC) video frames (to assist endoscopists during OC) is challenging due to variations in color, lighting, texture, and specular reflections. Previous methods either remove some of these variations via preprocessing (making pipelines cumbersome) or add diverse training data with annotations (but expensive and time-consuming). We present CLTS-GAN, a new deep learning model that gives fine control over color, lighting, texture, and specular reflection synthesis for OC video frames. We show that adding these colonoscopy-specific augmentations to the traini…
International Prevention Research Institute（国际预防研究所）；University of Strathclyde Institute of Global Public Health（思克莱德大学全球公共卫生研究所）；Croix-Rousse University Hospital（克鲁瓦鲁斯大学医院）；Hospices Civils de Lyon（里昂民用医院集团）；UMR-CNRS 5510（法国国家科学研究中心5510混合研究单位）
We introduce a novel method to extract the vessels in eye fun-dus images which is adaptive to lighting variations. In the Logarithmic Image Processing framework, a 3-segment probe detects the vessels by probing the topographic surface of an image from below. A map of contrasts between the probe and the image allows to detect the vessels by a threshold. In a lowly contrasted image, results show that our method better extract the vessels than another state-of the-art method. In a highly contrasted image database (DRIVE) with a reference , ours has an accuracy of 0.9454 which is similar or better…
Michigan State University（密歇根州立大学）；Dept. of Electrical and Computer Engineering（电气与计算机工程系（注：按要求机构粒度到大学/学院/公司/研究院级别，此处“Dept. of Electrical and Computer Engineering”为系级，按禁止提取规则不提取，仅提取大学级别“Michigan State University”）}]} 修正：严格按要求，仅提取大学级别。正确输出应为：{）
Automatic detection and segmentation of overlapping leaves in dense foliage can be a difficult task, particularly for leaves with strong textures and high occlusions. We present Dense-Leaves, an image dataset with ground truth segmentation labels that can be used to train and quantify algorithms for leaf segmentation in the wild. We also propose a pyramid convolutional neural network with multi-scale predictions that detects and discriminates leaf boundaries from interior textures. Using these detected boundaries, closed-contour boundaries around individual leaves are estimated with a watershe…
Reliable and automated 3D plant shoot segmentation is a core prerequisite for the extraction of plant phenotypic traits at the organ level. Combining deep learning and point clouds can provide effective ways to address the challenge. However, fully supervised deep learning methods require datasets to be point-wise annotated, which is extremely expensive and time-consuming. In our work, we proposed a novel weakly supervised framework, Eff-3DPSeg, for 3D plant shoot segmentation. First, high-resolution point clouds of soybean were reconstructed using a low-cost photogrammetry system, and the Mes…
Self-Supervised Pretraining Improves Cross-Site and Cross-Scale Robustness of Point Cloud Leaf-Wood Segmentation
Department of Landscape Architecture and Rural Systems Engineering, Seoul National University, Seoul, South Korea（景观建筑与乡村系统工程系，首尔国立大学，首尔，韩国）；Interdisciplinary Program in Landscape Architecture, Seoul National University, South Korea（景观建筑跨学科项目，首尔国立大学，韩国）；Integrated Major in Smart City Global Convergence, Seoul National University, Seoul, Republic of Korea（智能城市全球融合整合专业，首尔国立大学，首尔，韩国）；Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea（农业与生命科学研究院，首尔国立大学，首尔，韩国）
HyperTopo-Adapters: Geometry- and Topology-Aware Segmentation of Leaf Lesions on Frozen Encoders
Missouri Maize Computation and Vision Laboratory (MMCV)（密苏里州玉米计算与视觉实验室）；Department of Electrical Engineering and Computer Science（电气工程与计算机科学系）；University of Missouri（密苏里大学）
Accurate and reliable kiwifruit detection is one of the biggest challenges in developing a selective fruit harvesting robot. The vision system of an orchard robot faces difficulties such as dynamic lighting conditions and fruit occlusions. This paper presents a semantic segmentation approach with two novel image prepossessing techniques designed to detect kiwifruit under the harsh lighting conditions found in the canopy. The performance of the presented system is evaluated on a 3D real-world image set of kiwifruit under different lighting conditions (typical, glare, and overexposed). Alone the…
Contour-based instance segmentation methods include one-stage and multi-stage schemes. These approaches achieve remarkable performance. However, they have to define plenty of points to segment precise masks, which leads to high complexity. We follow this issue and present a single-shot method, called \textbf{VeinMask}, for achieving competitive performance in low design complexity. Concretely, we observe that the leaf locates coarse margins via major veins and grows minor veins to refine twisty parts, which makes it possible to cover any objects accurately. Meanwhile, major and minor veins sha…
Jalpaiguri Government Engineering College（贾尔派古里政府工程学院）
With increasing population the crisis of food is getting bigger day by day.In this time of crisis,the leaf disease of crops is the biggest problem in the food industry.In this paper, we have addressed that problem and proposed an efficient method to detect leaf disease.Leaf diseases can be detected from sample images of the leaf with the help of image processing and segmentation.Using k-means clustering and Otsu's method the faulty region in a leaf is detected which helps to determine proper course of action to be taken.Further the ratio of normal and faulty region if calculated would be able …
Color Recognition in Challenging Lighting Environments: CNN Approach
Sukkur IBA University（苏库尔IBA大学）；IBA Community College, Naushahro Feroze（瑙谢赫罗费罗兹IBA社区学院）；Saitama University（埼玉大学）；S.A.LU；Kathir College of Engineering（卡蒂尔工程学院）；Institute of Computer Science（计算机科学研究院）
Identification, classification, and quantification of crop defects are of paramount of interest to the farmers for preventive measures and decrease the yield loss through necessary remedial actions. Due to the vast agricultural field, manual inspection of crops is tedious and time-consuming. UAV based data collection, observation, identification, and quantification of defected leaves area are considered to be an effective solution. The present work attempts to estimate the percentage of affected groundnut leaves area across four regions of Andharapradesh using image processing techniques. The …
In this paper, we investigate the problem of counting rosette leaves from an RGB image, an important task in plant phenotyping. We propose a data-driven approach for this task generalized over different plant species and imaging setups. To accomplish this task, we use state-of-the-art deep learning architectures: a deconvolutional network for initial segmentation and a convolutional network for leaf counting. Evaluation is performed on the leaf counting challenge dataset at CVPPP-2017. Despite the small number of training samples in this dataset, as compared to typical deep learning image sets…
UC San Diego（加州大学圣地亚哥分校）；Carnegie Mellon University（卡内基梅隆大学）；Adobe Research（Adobe研究院）
We present a method to edit complex indoor lighting from a single image with its predicted depth and light source segmentation masks. This is an extremely challenging problem that requires modeling complex light transport, and disentangling HDR lighting from material and geometry with only a partial LDR observation of the scene. We tackle this problem using two novel components: 1) a holistic scene reconstruction method that estimates scene reflectance and parametric 3D lighting, and 2) a neural rendering framework that re-renders the scene from our predictions. We use physically-based indoor …
In order to identify and prevent tea leaf diseases effectively, convolution neural network (CNN) was used to realize the image recognition of tea disease leaves. Firstly, image segmentation and data enhancement are used to preprocess the images, and then these images were input into the network for training. Secondly, to reach a higher recognition accuracy of CNN, the learning rate and iteration numbers were adjusted frequently and the dropout was added properly in the case of over-fitting. Finally, the experimental results show that the recognition accuracy of CNN is 93.75%, while the accurac…
