Machine Learning-Based Jamun Leaf Disease Detection: A Comprehensive Review

• URL: http://arxiv.org/abs/2311.15741v1
• Date: Mon, 27 Nov 2023 11:46:30 GMT
• Title: Machine Learning-Based Jamun Leaf Disease Detection: A Comprehensive Review
• Authors: Auvick Chandra Bhowmik, Dr. Md. Taimur Ahad, Yousuf Rayhan Emon
• Abstract summary: Jamun leaf diseases pose a significant threat to agricultural productivity. The advent of machine learning has opened up new avenues for tackling these diseases effectively. Various automated systems have been implemented for similar types of disease detection using image processing techniques.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Jamun leaf diseases pose a significant threat to agricultural productivity, negatively impacting both yield and quality in the jamun industry. The advent of machine learning has opened up new avenues for tackling these diseases effectively. Early detection and diagnosis are essential for successful crop management. While no automated systems have yet been developed specifically for jamun leaf disease detection, various automated systems have been implemented for similar types of disease detection using image processing techniques. This paper presents a comprehensive review of machine learning methodologies employed for diagnosing plant leaf diseases through image classification, which can be adapted for jamun leaf disease detection. It meticulously assesses the strengths and limitations of various Vision Transformer models, including Transfer learning model and vision transformer (TLMViT), SLViT, SE-ViT, IterationViT, Tiny-LeViT, IEM-ViT, GreenViT, and PMViT. Additionally, the paper reviews models such as Dense Convolutional Network (DenseNet), Residual Neural Network (ResNet)-50V2, EfficientNet, Ensemble model, Convolutional Neural Network (CNN), and Locally Reversible Transformer. These machine-learning models have been evaluated on various datasets, demonstrating their real-world applicability. This review not only sheds light on current advancements in the field but also provides valuable insights for future research directions in machine learning-based jamun leaf disease detection and classification.

Related papers

• Benchmarking In-the-wild Multimodal Disease Recognition and A Versatile Baseline [42.49727243388804]
  We propose an in-the-wild multimodal plant disease recognition dataset. It contains the largest number of disease classes but also text-based descriptions for each disease. Our proposed dataset can be regarded as an ideal testbed for evaluating disease recognition methods in the real world.
  arXiv  Detail & Related papers  (2024-08-06T11:49:13Z)
• 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)
• Machine Learning-Based Tea Leaf Disease Detection: A Comprehensive Review [3.3916160303055563]
  Tea leaf diseases are a major challenge to agricultural productivity, with far-reaching implications for yield and quality in the tea industry. The rise of machine learning has enabled the development of innovative approaches to combat these diseases. For predicting tea leaf disease, several automated systems have already been developed using different image processing techniques.
  arXiv  Detail & Related papers  (2023-11-06T16:30:40Z)
• SugarViT -- Multi-objective Regression of UAV Images with Vision Transformers and Deep Label Distribution Learning Demonstrated on Disease Severity Prediction in Sugar Beet [3.2925222641796554]
  This work will introduce a machine learning framework for automatized large-scale plant-specific trait annotation. We develop an efficient Vision Transformer based model for disease severity scoring called SugarViT. Although the model is evaluated on this special use case, it is held as generic as possible to also be applicable to various image-based classification and regression tasks.
  arXiv  Detail & Related papers  (2023-11-06T13:01:17Z)
• Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection [76.11864242047074]
  We propose a novel Affine-Consistent Transformer (AC-Former), which directly yields a sequence of nucleus positions. We introduce an Adaptive Affine Transformer (AAT) module, which can automatically learn the key spatial transformations to warp original images for local network training. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on various benchmarks.
  arXiv  Detail & Related papers  (2023-10-22T02:27:02Z)
• A comprehensive review on Plant Leaf Disease detection using Deep learning [0.0]
  Leaf disease is a common fatal disease for plants. Several automated systems have already been developed using different plant pathology imaging modalities. This paper provides a systematic review of the literature on leaf disease-based models for the diagnosis of various plant leaf diseases via deep learning.
  arXiv  Detail & Related papers  (2023-08-27T12:20:28Z)
• Defect Classification in Additive Manufacturing Using CNN-Based Vision Processing [76.72662577101988]
  This paper examines two scenarios: first, using convolutional neural networks (CNNs) to accurately classify defects in an image dataset from AM and second, applying active learning techniques to the developed classification model. This allows the construction of a human-in-the-loop mechanism to reduce the size of the data required to train and generate training data.
  arXiv  Detail & Related papers  (2023-07-14T14:36:58Z)
• Explainable vision transformer enabled convolutional neural network for plant disease identification: PlantXViT [11.623005206620498]
  Plant diseases are the primary cause of crop losses globally, with an impact on the world economy. In this study, a Vision Transformer enabled Convolutional Neural Network model called "PlantXViT" is proposed for plant disease identification. The proposed model has a lightweight structure with only 0.8 million trainable parameters, which makes it suitable for IoT-based smart agriculture services.
  arXiv  Detail & Related papers  (2022-07-16T12:05:06Z)
• In-Line Image Transformations for Imbalanced, Multiclass Computer Vision Classification of Lung Chest X-Rays [91.3755431537592]
  This study aims to leverage a body of literature in order to apply image transformations that would serve to balance the lack of COVID-19 LCXR data. Deep learning techniques such as convolutional neural networks (CNNs) are able to select features that distinguish between healthy and disease states. This study utilizes a simple CNN architecture for high-performance multiclass LCXR classification at 94 percent accuracy.
  arXiv  Detail & Related papers  (2021-04-06T02:01:43Z)
• Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning [91.3755431537592]
  We propose the use of Multi-Source Transfer Learning to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans. With our multi-source fine-tuning approach, our models outperformed baseline models fine-tuned with ImageNet. Our best performing model was able to achieve an accuracy of 0.893 and a Recall score of 0.897, outperforming its baseline Recall score by 9.3%.
  arXiv  Detail & Related papers  (2020-09-22T11:53:06Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)

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.