SSM-Net for Plants Disease Identification in Low Data Regime

• URL: http://arxiv.org/abs/2005.13140v4
• Date: Mon, 7 Sep 2020 20:16:31 GMT
• Title: SSM-Net for Plants Disease Identification in Low Data Regime
• Authors: Shruti Jadon
• Abstract summary: Plant disease detection is an essential factor in increasing agricultural production. It is highly inconvenient to collect a large volume of data on all forms of the diseases afflicting a specific plant species. We propose a new metrics-based few-shot learning SSM net architecture to address the problem of disease detection in low data regimes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 to address the problem of disease detection in low data regimes. We demonstrated our experiments on two datasets: mini-leaves diseases and sugarcane diseases dataset. We have showcased that the SSM-Net approach can achieve better decision boundaries with an accuracy of 92.7% on the mini-leaves dataset and 94.3% on the sugarcane dataset. The accuracy increased by ~10% and ~5% respectively, compared to the widely used VGG16 transfer learning approach. Furthermore, we attained F1 score of 0.90 using SSM Net on the sugarcane dataset and 0.91 on the mini-leaves dataset. Our code implementation is available on Github: https://github.com/shruti-jadon/PlantsDiseaseDetection.

Related papers

• SMILE-UHURA Challenge -- Small Vessel Segmentation at Mesoscopic Scale from Ultra-High Resolution 7T Magnetic Resonance Angiograms [60.35639972035727]
  The lack of publicly available annotated datasets has impeded the development of robust, machine learning-driven segmentation algorithms. The SMILE-UHURA challenge addresses the gap in publicly available annotated datasets by providing an annotated dataset of Time-of-Flight angiography acquired with 7T MRI. Dice scores reached up to 0.838 $pm$ 0.066 and 0.716 $pm$ 0.125 on the respective datasets, with an average performance of up to 0.804 $pm$ 0.15.
  arXiv  Detail & Related papers  (2024-11-14T17:06:00Z)
• The PV-ALE Dataset: Enhancing Apple Leaf Disease Classification Through Transfer Learning with Convolutional Neural Networks [0.0]
  We extend the widely used PlantVillage dataset with additional apple leaf disease classes. Test F1 scores of 99.63% and 97.87% were obtained on the original and extended datasets.
  arXiv  Detail & Related papers  (2024-10-29T19:30:22Z)
• PND-Net: Plant Nutrition Deficiency and Disease Classification using Graph Convolutional Network [18.778641229886393]
  This article proposes a new deep learning method for plant nutrition deficiencies and disease classification using a graph convolutional network (GNN) The proposed method, called Plant Nutrition Deficiency and Disease Network (PND-Net), is evaluated on two public datasets for nutrition deficiency, and two for disease classification using four CNNs.
  arXiv  Detail & Related papers  (2024-10-16T17:01:28Z)
• Self-supervised transformer-based pre-training method with General Plant Infection dataset [3.969851116372513]
  This study proposes an advanced network architecture that combines Contrastive Learning and Masked Image Modeling (MIM) The proposed network architecture demonstrates effectiveness in addressing plant pest and disease recognition tasks, achieving notable detection accuracy. Our code and dataset will be publicly available to advance research in plant pest and disease recognition.
  arXiv  Detail & Related papers  (2024-07-20T15:48:35Z)
• Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
  Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
  arXiv  Detail & Related papers  (2024-04-26T16:39:50Z)
• HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing [50.4506590177605]
  HarvestNet is a dataset for mapping the presence of farms in the Ethiopian regions of Tigray and Amhara during 2020-2023. We introduce a new approach based on the detection of harvest piles characteristic of many smallholder systems. We conclude that remote sensing of harvest piles can contribute to more timely and accurate cropland assessments in food insecure regions.
  arXiv  Detail & Related papers  (2023-08-23T11:03:28Z)
• Agave crop segmentation and maturity classification with deep learning data-centric strategies using very high-resolution satellite imagery [101.18253437732933]
  We present an Agave tequilana Weber azul crop segmentation and maturity classification using very high resolution satellite imagery. We solve real-world deep learning problems in the very specific context of agave crop segmentation. With the resulting accurate models, agave production forecasting can be made available for large regions.
  arXiv  Detail & Related papers  (2023-03-21T03:15:29Z)
• Paddy Doctor: A Visual Image Dataset for Paddy Disease Classification [0.0]
  Paddy Doctor is a visual image dataset for identifying paddy diseases. Our dataset contains 13,876 annotated paddy leaf images across ten classes (nine diseases and normal leaf) We benchmarked the Paddy Doctor using a Convolutional Neural Network (CNN) and two transfer learning approaches, VGG16 and MobileNet.
  arXiv  Detail & Related papers  (2022-05-23T07:57:40Z)
• Deep neural networks approach to microbial colony detection -- a comparative analysis [52.77024349608834]
  This study investigates the performance of three deep learning approaches for object detection on the AGAR dataset. The achieved results may serve as a benchmark for future experiments.
  arXiv  Detail & Related papers  (2021-08-23T12:06:00Z)
• A CNN Approach to Simultaneously Count Plants and Detect Plantation-Rows from UAV Imagery [56.10033255997329]
  We propose a novel deep learning method based on a Convolutional Neural Network (CNN) It simultaneously detects and geolocates plantation-rows while counting its plants considering highly-dense plantation configurations. The proposed method achieved state-of-the-art performance for counting and geolocating plants and plant-rows in UAV images from different types of crops.
  arXiv  Detail & Related papers  (2020-12-31T18:51:17Z)
• The Plant Pathology 2020 challenge dataset to classify foliar disease of apples [0.0]
  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. We have manually captured 3,651 high-quality, real-life symptom images of multiple apple foliar diseases. A subset, expert-annotated to create a pilot dataset for apple scab, cedar apple rust, and healthy leaves, was made available to the Kaggle community for 'Plant Pathology Challenge'
  arXiv  Detail & Related papers  (2020-04-24T19:36:37Z)

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.