Unified View of Damage leaves Planimetry & Analysis Using Digital Images Processing Techniques

• URL: http://arxiv.org/abs/2306.16734v1
• Date: Thu, 29 Jun 2023 07:15:45 GMT
• Title: Unified View of Damage leaves Planimetry & Analysis Using Digital Images Processing Techniques
• Authors: Pijush Kanti Kumar, DeepKiran Munjal, Sunita Rani, Anurag Dutta, Liton Chandra Voumik and A. Ramamoorthy
• Abstract summary: This paper attempts to identify plant leaf diseases using image processing techniques. The focus of this study is on the detection of citrus leaf canker disease.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The detection of leaf diseases in plants generally involves visual observation of patterns appearing on the leaf surface. However, there are many diseases that are distinguished based on very subtle changes in these visually observable patterns. This paper attempts to identify plant leaf diseases using image processing techniques. The focus of this study is on the detection of citrus leaf canker disease. Canker is a bacterial infection of leaves. Symptoms of citrus cankers include brown spots on the leaves, often with a watery or oily appearance. The spots (called lesions in botany) are usually yellow. It is surrounded by a halo of the leaves and is found on both the top and bottom of the leaf. This paper describes various methods that have been used to detect citrus leaf canker disease. The methods used are histogram comparison and k-means clustering. Using these methods, citrus canker development was detected based on histograms generated based on leaf patterns. The results thus obtained can be used, after consultation with experts in the field of agriculture, to identify suitable treatments for the processes used.

Related papers

• A Comprehensive Literature Review on Sweet Orange Leaf Diseases [3.0177210416625115]
  Leaf diseases impact fruit quality in the citrus industry. Early detection and diagnosis are necessary for leaf management. This comprehensive review study related to leaf disease compares the performance of the models.
  arXiv  Detail & Related papers  (2023-12-04T09:35:21Z)
• Detection of Late Blight Disease in Tomato Leaf Using Image Processing Techniques [0.0]
  Late blight is the most prevalent tomato disease in the world, and often causes a significant reduction in the production of tomato crops. The importance of tomatoes as an agricultural product necessitates early detection of late blight. Using picture segmentation and the Multi-class SVM technique, late blight disorder is discovered in this work.
  arXiv  Detail & Related papers  (2023-05-31T06:16:40Z)
• Cross-modulated Few-shot Image Generation for Colorectal Tissue Classification [58.147396879490124]
  Our few-shot generation method, named XM-GAN, takes one base and a pair of reference tissue images as input and generates high-quality yet diverse images. To the best of our knowledge, we are the first to investigate few-shot generation in colorectal tissue images.
  arXiv  Detail & Related papers  (2023-04-04T17:50:30Z)
• Digital staining in optical microscopy using deep learning -- a review [47.86254766044832]
  Digital staining has emerged as a promising concept to use modern deep learning for the translation from optical contrast to established biochemical contrast of actual stainings. We provide an in-depth analysis of the current state-of-the-art in this field, suggest methods of good practice, identify pitfalls and challenges and postulate promising advances towards potential future implementations and applications.
  arXiv  Detail & Related papers  (2023-03-14T15:23:48Z)
• Unsupervised Deep Digital Staining For Microscopic Cell Images Via Knowledge Distillation [46.006296303296544]
  It is difficult to obtain large-scale stained/unstained cell image pairs in practice. We propose a novel unsupervised deep learning framework for the digital staining of cell images. We show that the proposed unsupervised deep staining method can generate stained images with more accurate positions and shapes of the cell targets.
  arXiv  Detail & Related papers  (2023-03-03T16:26:38Z)
• Semantic Image Segmentation with Deep Learning for Vine Leaf Phenotyping [59.0626764544669]
  In this study, we use Deep Learning methods to semantically segment grapevine leaves images in order to develop an automated object detection system for leaf phenotyping. Our work contributes to plant lifecycle monitoring through which dynamic traits such as growth and development can be captured and quantified.
  arXiv  Detail & Related papers  (2022-10-24T14:37:09Z)
• Leaf Tar Spot Detection Using RGB Images [16.38252968880992]
  Tar spot disease is a fungal disease that appears as a series of black circular spots on corn leaves. Deep neural networks could provide quick, automated tar spot detection with sufficient ground truth. We show that a Mask R-CNN can be used effectively to detect tar spots in close-up images of leaf surfaces.
  arXiv  Detail & Related papers  (2022-05-02T14:56:06Z)
• Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform [82.53597363161228]
  This paper proposes a method for characterizing texture across histopathologic images with a considerable success rate. It is possible to quantify the intrinsic properties of such images with promising accuracy on two HI datasets.
  arXiv  Detail & Related papers  (2022-02-27T02:19:09Z)
• Assessing glaucoma in retinal fundus photographs using Deep Feature Consistent Variational Autoencoders [63.391402501241195]
  glaucoma is challenging to detect since it remains asymptomatic until the symptoms are severe. Early identification of glaucoma is generally made based on functional, structural, and clinical assessments. Deep learning methods have partially solved this dilemma by bypassing the marker identification stage and analyzing high-level information directly to classify the data.
  arXiv  Detail & Related papers  (2021-10-04T16:06:49Z)
• A Multi-Plant Disease Diagnosis Method using Convolutional Neural Network [0.0]
  This chapter investigates an optimal plant disease identification model combining the diagnosis of multiple plants. We implement numerous popular convolutional neural network (CNN) architectures. The experimental results validate that the Xception and DenseNet architectures perform better in multi-label plant disease classification tasks.
  arXiv  Detail & Related papers  (2020-11-10T15:18:52Z)
• A Review of Visual Descriptors and Classification Techniques Used in Leaf Species Identification [0.0]
  Key research areas in plant science include plant species identification, weed classification using hyper spectral images, monitoring plant health and tracing leaf growth, and the semantic interpretation of leaf information. Botanists easily identify plant species by discriminating between the shape of the leaf, tip, base, leaf margin and leaf vein, as well as the texture of the leaf and the arrangement of leaflets of compound leaves. There is a need for intelligent systems that recognize and characterize leaves so as to scrutinize a particular species, the diseases that affect them, the pattern of leaf growth, and so on.
  arXiv  Detail & Related papers  (2020-09-13T14:11:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.