PSIV-3 Canine olfaction as a disease detection technology: A systematic review

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 295-296
Author(s):  
Aiden E Juge ◽  
Courtney L Daigle
Keyword(s):  
Systematic Review ◽  
Median Number ◽  
Fixed Number ◽  
Positive Sample ◽  
Plant Diseases ◽  
Disease Detection ◽  
Multiple Test ◽  
Promising Strategy ◽  
Detection Technology ◽  
Labrador Retrievers

Abstract Capitalizing on canine olfactory capacity is a promising strategy for detecting and diagnosing human, animal, and plant diseases. The purpose of this review was to assess the extent of current research in canine disease detection. In this systematic review, multiple databases were searched for studies in which dogs were trained to detect diseases or health conditions. Following PRISMA guidelines, 1689 studies were screened and 50 relevant studies identified. The majority of studies (n = 31, 66%) took place in Europe. Lung cancer (n = 11, 22%), prostate cancer (n = 8, 16%), and breast cancer (n = 7, 14%) were the most frequently-studied conditions. Urine (n = 17, 34%) and breath (n = 9, 18%) were the most common sample types. Across all studies, 166 unique detection dogs were tested. The most numerous breed was Labrador Retrievers (n = 24, 14.46%). The median number of dogs per study was 2 (range: 1–20). To analyze experimental design and results, studies including multiple test paradigms were divided into sub-studies (n = 90). In 84.4% of sub-studies (n = 76), dogs were presented with sets of samples and 74.4% (n = 67) reported a constant number of samples per trial. The median number of samples per trial was 7 (range: 2–100). Of the sub-studies reporting a fixed number of positive samples (range: 1–10; n = 55), 87% (n = 48) presented one positive sample per trial. A plurality of sub-studies (n = 44, 49%) presented samples in a lineup. Sensitivity (median: 0.90; range: 0.13 to 1.0; n = 77) and specificity (median: 0.96; range: 0.08 to 1.0; n = 69) were the predominant measures of detection success, although reporting strategies were inconsistent. Dogs appear to have the capacity to detect disease via olfaction; yet the nascent nature of this discipline yields little consistency across studies.

scholarly journals Tomato Leaf Disease Detection using Back Propagation Neural Network

2020 ◽  
Vol 9 (8) ◽  
pp. 529-538
Keyword(s):  
Neural Network ◽  
Traditional Approach ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Plant Diseases ◽  
Disease Detection ◽  
Large Drop ◽  
Tomato Crop ◽  
Leaf Disease ◽  
Area Of Interest

Most of the Indian economy rely on agriculture, so identifying any diseases crop in early stages is very crucial as these diseases in plants causes a large drop in the production and economy of the farmers and therefore, degradation of the crop which emphasize on the early detection of the plant disease. These days, detection of plant diseases has become a hot topic in the area of interest of the researchers. Farmers followed a traditional approach for identifying and detecting diseases in plants with naked eyes, which didn’t help much as the disease may have caused much damage to the plant. Tomato crop shares a huge portion of Indian cuisine and can be prone to various Air-Bourne and Soil-Bourne diseases. In this paper, we tried to automate the Tomato Plant Leaf disease detection by studying the various features of diseased and healthy leaves. The technique used is pattern recognition using Back-Propagation Neural network and comparing the results of this neural network on different features set. Several steps included are image acquisition, image pre-processing, features extraction, subset creation and BPNN classification.

scholarly journals A Review on Advances in Automated Plant Disease Detection

2021 ◽  
Vol 11 (4) ◽  
pp. 251-264
Author(s):  
Radhika Bhagwat ◽  
Yogesh Dandawate
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Plant Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Plant Diseases ◽  
Detection Methods ◽  
Economic Losses ◽  
Disease Detection ◽  
Range Images

Plant diseases cause major yield and economic losses. To detect plant disease at early stages, selecting appropriate techniques is imperative as it affects the cost, diagnosis time, and accuracy. This research gives a comprehensive review of various plant disease detection methods based on the images used and processing algorithms applied. It systematically analyzes various traditional machine learning and deep learning algorithms used for processing visible and spectral range images, and comparatively evaluates the work done in literature in terms of datasets used, various image processing techniques employed, models utilized, and efficiency achieved. The study discusses the benefits and restrictions of each method along with the challenges to be addressed for rapid and accurate plant disease detection. Results show that for plant disease detection, deep learning outperforms traditional machine learning algorithms while visible range images are more widely used compared to spectral images.

Plant Disease Detection Using Machine Learning Approaches

2021 ◽  
pp. 122-130
Author(s):  
Sukanta Ghosh ◽  
Shubhanshu Arya ◽  
Amar Singh
Keyword(s):  
Plant Disease ◽  
Crop Yields ◽  
Detection System ◽  
Visual Observation ◽  
Plant Diseases ◽  
Disease Detection ◽  
Learning Approaches ◽  
Factors Affecting ◽  
Plant Leaf ◽  
Market Situation

Agricultural production is one of the main factors affecting a country's domestic market situation. Many problems are the reasons for estimating crop yields, which vary in different parts of the world. Overuse of chemical fertilizers, uneven distribution of rainfall, and uneven soil fertility lead to plant diseases. This forces us to focus on effective methods for detecting plant diseases. It is important to find an effective plant disease detection technique. Plants need to be monitored from the beginning of their life cycle to avoid such diseases. Observation is a kind of visual observation, which is time-consuming, costly, and requires a lot of experience. For speeding up this process, it is necessary to automate the disease detection system. A lot of researchers have developed plant leaf detection systems based on various technologies. In this chapter, the authors discuss the potential of methods for detecting plant leaf diseases. It includes various steps such as image acquisition, image segmentation, feature extraction, and classification.

scholarly journals Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 939 ◽  
Author(s):  
Marko Arsenovic ◽  
Mirjana Karanovic ◽  
Srdjan Sladojevic ◽  
Andras Anderla ◽  
Darko Stefanovic
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Real Life ◽  
Weather Conditions ◽  
Plant Diseases ◽  
Disease Classification ◽  
Generative Adversarial Networks ◽  
Disease Detection ◽  
Single Leaf ◽  
The Impact

Plant diseases cause great damage in agriculture, resulting in significant yield losses. The recent expansion of deep learning methods has found its application in plant disease detection, offering a robust tool with highly accurate results. The current limitations and shortcomings of existing plant disease detection models are presented and discussed in this paper. Furthermore, a new dataset containing 79,265 images was introduced with the aim to become the largest dataset containing leaf images. Images were taken in various weather conditions, at different angles, and daylight hours with an inconsistent background mimicking practical situations. Two approaches were used to augment the number of images in the dataset: traditional augmentation methods and state-of-the-art style generative adversarial networks. Several experiments were conducted to test the impact of training in a controlled environment and usage in real-life situations to accurately identify plant diseases in a complex background and in various conditions including the detection of multiple diseases in a single leaf. Finally, a novel two-stage architecture of a neural network was proposed for plant disease classification focused on a real environment. The trained model achieved an accuracy of 93.67%.

scholarly journals Plant Disease Detection and Localization using GRADCAM

2020 ◽  
Vol 8 (6) ◽  
pp. 3069-3075
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Plant Diseases ◽  
Financial Resources ◽  
Disease Detection ◽  
The World ◽  
Detection And Localization ◽  
Better Than ◽  
Made In

Plant diseases are diseases that change or disrupt its important functions. The reduction in the age at which a plant dies is the main danger of plant diseases. And farmers around the world have to face the challenge of identifying and classifying these diseases and changing their treatments for each disease. This task becomes more difficult when they have to rely on naked eyes to identify diseases due to the lack of proper financial resources. But with the widespread use of smartphones by farmers and advances made in the field of deep learning, researchers around the world are trying to find a solution to this problem. Similarly, the purpose of this paper is to classify these diseases using deep learning and localize them on their respective leaves. We have considered two main models for classification called resnet and efficientnet and for localizing these diseases we have used GRADCAM and CAM. GRADCAM was able to localize diseases better than CAM

scholarly journals Diseases Detection of Occlusion and Overlapping Tomato Leaves Based on Deep Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Xuewei Wang ◽  
Jun Liu ◽  
Guoxu Liu
Keyword(s):  
Natural Environment ◽  
Network Architecture ◽  
Plant Diseases ◽  
Frame Rate ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Layer By Layer ◽  
Detection Technology ◽  
Tomato Diseases ◽  
Detection Speed

Background: In view of the existence of light shadow, branches occlusion, and leaves overlapping conditions in the real natural environment, problems such as slow detection speed, low detection accuracy, high missed detection rate, and poor robustness in plant diseases and pests detection technology arise.Results: Based on YOLOv3-tiny network architecture, to reduce layer-by-layer loss of information during network transmission, and to learn from the idea of inverse-residual block, this study proposes a YOLOv3-tiny-IRB algorithm to optimize its feature extraction network, improve the gradient disappearance phenomenon during network deepening, avoid feature information loss, and realize network multilayer feature multiplexing and fusion. The network is trained by the methods of expanding datasets and multiscale strategies to obtain the optimal weight model.Conclusion: The experimental results show that when the method is tested on the self-built tomato diseases and pests dataset, and while ensuring the detection speed (206 frame rate per second), the mean Average precision (mAP) under three conditions: (a) deep separation, (b) debris occlusion, and (c) leaves overlapping are 98.3, 92.1, and 90.2%, respectively. Compared with the current mainstream object detection methods, the proposed method improves the detection accuracy of tomato diseases and pests under conditions of occlusion and overlapping in real natural environment.

scholarly journals POMEGRANATE FRUIT DISEASES DETECTION USING IMAGE PROCESSING TECHNIQUES: A REVIEW

2021 ◽  
Vol 9 (2) ◽  
pp. 115-120
Author(s):  
Jayashri Patil, Et. al.
Keyword(s):  
Plant Root ◽  
Modern Technology ◽  
Plant Diseases ◽  
Economic Losses ◽  
Disease Detection ◽  
Traditional System ◽  
Detection Techniques ◽  
Lower Accuracy ◽  
Fruit Disease ◽  
Pomegranate Fruit

The Agriculture plant diseases are responsible for farmer economic losses. These diseases affect on plant root, fruit, leaf, and stem. Detection of disease at early stages helps the farmer to improve productivity. In the traditional system agriculture experts and experienced farmer can recognize the plant diseases at the lower accuracy which causes losses to farmers. Currently several researchers are proposing soft computing and expert systems to recognize plant diseases.  Plant disease identification by visual way is less accurate because some diseases do not have any visible symptoms or some of the diseases appear too late at the time of harvesting. The modern technology in agriculture sector can substantially improve the agriculture production & sustainability. This paper provides a review for fruit disease detection techniques for pomegranate plants. This study includes preprocessing, segmentation, feature extraction and classification techniques for pomegranate fruit diseases detection systems. This paper also states the comparison and limitations of existing fruit disease detection techniques.

scholarly journals Disease Detection in Plum Using Convolutional Neural Network under True Field Conditions

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5569
Author(s):  
Jamil Ahmad ◽  
Bilal Jan ◽  
Haleem Farman ◽  
Wakeel Ahmad ◽  
Atta Ullah
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Plant Diseases ◽  
Field Conditions ◽  
Disease Detection ◽  
Resource Constrained ◽  
Extensive Data ◽  
Resource Constrained Devices ◽  
Constrained Devices

The agriculture sector faces crop losses every year due to diseases around the globe, which adversely affect food productivity and quality. Detecting and identifying plant diseases at an early stage is still a challenge for farmers, particularly in developing countries. Widespread use of mobile computing devices and the advancements in artificial intelligence have created opportunities for developing technologies to assist farmers in plant disease detection and treatment. To this end, deep learning has been widely used for disease detection in plants with highly favorable outcomes. In this paper, we propose an efficient convolutional neural network-based disease detection framework in plum under true field conditions for resource-constrained devices. As opposed to the publicly available datasets, images used in this study were collected in the field by considering important parameters of image-capturing devices such as angle, scale, orientation, and environmental conditions. Furthermore, extensive data augmentation was used to expand the dataset and make it more challenging to enable robust training. Investigations of recent architectures revealed that transfer learning of scale-sensitive models like Inception yield results much better with such challenging datasets with extensive data augmentation. Through parameter quantization, we optimized the Inception-v3 model for deployment on resource-constrained devices. The optimized model successfully classified healthy and diseased fruits and leaves with more than 92% accuracy on mobile devices.

Analysis of Plant Disease Detection and Classification Models: A Computer Vision Perspective

2020 ◽  
Vol 17 (12) ◽  
pp. 5422-5428
Author(s):  
K. Jayaprakash ◽  
S. P. Balamurugan
Keyword(s):  
Computer Vision ◽  
Feature Extraction ◽  
Plant Disease ◽  
Weather Conditions ◽  
Vital Role ◽  
Plant Diseases ◽  
Disease Detection ◽  
Classification Models ◽  
Extraction Techniques ◽  
Identification Process

Presently, rapid and precise disease identification process plays a vital role to increase agricultural productivity in a sustainable manner. Conventionally, human experts identify the existence of anomaly in plants occurred due to disease, pest, nutrient deficient, weather conditions. Since manual diagnosis process is a tedious and time consuming task, computer vision approaches have begun to automatically detect and classify the plant diseases. The general image processing tasks involved in plant disease detection are preprocessing, segmentation, feature extraction and classification. This paper performs a review of computer vision based plant disease detection and classification techniques. The existing plant disease detection approaches including segmentation and feature extraction techniques have been reviewed. Additionally, a brief survey of machine learning (ML) and deep learning (DL) models to identify plant diseases also takes place. Furthermore, a set of recently developed DL based tomato plant leaf disease detection and classification models are surveyed under diverse aspects. To further understand the reviewed methodologies, a detailed comparative study also takes place to recognize the unique characteristics of the reviewed models.

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