Paddy Disease Detection and Classification Using Computer Vision Techniques: A Mobile Application to Detect Paddy Disease
Plant diseases significantly impact our food supply, causing problems for farmers, economies reliant on agriculture, and global food security. Accurate and timely plant disease diagnosis is crucial for effective treatment and minimizing yield losses. Despite advancements in agricultural technology,...
Gespeichert in:
Veröffentlicht in: | arXiv.org 2024-12 |
---|---|
Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | arXiv.org |
container_volume | |
creator | Khanal, Bimarsha Poudel, Paras Chapagai, Anish Regmi, Bijan Pokhrel, Sitaram Salik Ram Khanal |
description | Plant diseases significantly impact our food supply, causing problems for farmers, economies reliant on agriculture, and global food security. Accurate and timely plant disease diagnosis is crucial for effective treatment and minimizing yield losses. Despite advancements in agricultural technology, a precise and early diagnosis remains a challenge, especially in underdeveloped regions where agriculture is crucial and agricultural experts are scarce. However, adopting Deep Learning applications can assist in accurately identifying diseases without needing plant pathologists. In this study, the effectiveness of various computer vision models for detecting paddy diseases is evaluated and proposed the best deep learning-based disease detection system. Both classification and detection using the Paddy Doctor dataset, which contains over 20,000 annotated images of paddy leaves for disease diagnosis are tested and evaluated. For detection, we utilized the YOLOv8 model-based model were used for paddy disease detection and CNN models and the Vision Transformer were used for disease classification. The average mAP50 of 69% for detection tasks was achieved and the Vision Transformer classification accuracy was 99.38%. It was found that detection models are effective at identifying multiple diseases simultaneously with less computing power, whereas classification models, though computationally expensive, exhibit better performance for classifying single diseases. Additionally, a mobile application was developed to enable farmers to identify paddy diseases instantly. Experiments with the app showed encouraging results in utilizing the trained models for both disease classification and treatment guidance. |
format | Article |
fullrecord | <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_3142734260</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3142734260</sourcerecordid><originalsourceid>FETCH-proquest_journals_31427342603</originalsourceid><addsrcrecordid>eNqNjc0KgkAUhYcgSKp3uNBa0BmtaCdatAlaWFuZ9FYjNmPeceHbl2GLdq0OfOdvxBwuhO-uA84nbE5Uep7HlysehsJh3VEWRQeJIpSEkKDF3CqjQeoC4koSqavK5QedSOkbxOZRtxYbOCvqaYr5Xatni7SBCA7moiqEqK6rb82aYRZ-vmZsfJUV4XzQKVvstmm8d-vG9Gs2K03b6LeVCT_gKxHwpSf-S70AaIlNZQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3142734260</pqid></control><display><type>article</type><title>Paddy Disease Detection and Classification Using Computer Vision Techniques: A Mobile Application to Detect Paddy Disease</title><source>Free E- Journals</source><creator>Khanal, Bimarsha ; Poudel, Paras ; Chapagai, Anish ; Regmi, Bijan ; Pokhrel, Sitaram ; Salik Ram Khanal</creator><creatorcontrib>Khanal, Bimarsha ; Poudel, Paras ; Chapagai, Anish ; Regmi, Bijan ; Pokhrel, Sitaram ; Salik Ram Khanal</creatorcontrib><description>Plant diseases significantly impact our food supply, causing problems for farmers, economies reliant on agriculture, and global food security. Accurate and timely plant disease diagnosis is crucial for effective treatment and minimizing yield losses. Despite advancements in agricultural technology, a precise and early diagnosis remains a challenge, especially in underdeveloped regions where agriculture is crucial and agricultural experts are scarce. However, adopting Deep Learning applications can assist in accurately identifying diseases without needing plant pathologists. In this study, the effectiveness of various computer vision models for detecting paddy diseases is evaluated and proposed the best deep learning-based disease detection system. Both classification and detection using the Paddy Doctor dataset, which contains over 20,000 annotated images of paddy leaves for disease diagnosis are tested and evaluated. For detection, we utilized the YOLOv8 model-based model were used for paddy disease detection and CNN models and the Vision Transformer were used for disease classification. The average mAP50 of 69% for detection tasks was achieved and the Vision Transformer classification accuracy was 99.38%. It was found that detection models are effective at identifying multiple diseases simultaneously with less computing power, whereas classification models, though computationally expensive, exhibit better performance for classifying single diseases. Additionally, a mobile application was developed to enable farmers to identify paddy diseases instantly. Experiments with the app showed encouraging results in utilizing the trained models for both disease classification and treatment guidance.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Agriculture ; Applications programs ; Classification ; Computer vision ; Deep learning ; Diagnosis ; Effectiveness ; Food supply ; Health services ; Machine learning ; Medical diagnosis ; Mobile computing ; Plant diseases</subject><ispartof>arXiv.org, 2024-12</ispartof><rights>2024. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>780,784</link.rule.ids></links><search><creatorcontrib>Khanal, Bimarsha</creatorcontrib><creatorcontrib>Poudel, Paras</creatorcontrib><creatorcontrib>Chapagai, Anish</creatorcontrib><creatorcontrib>Regmi, Bijan</creatorcontrib><creatorcontrib>Pokhrel, Sitaram</creatorcontrib><creatorcontrib>Salik Ram Khanal</creatorcontrib><title>Paddy Disease Detection and Classification Using Computer Vision Techniques: A Mobile Application to Detect Paddy Disease</title><title>arXiv.org</title><description>Plant diseases significantly impact our food supply, causing problems for farmers, economies reliant on agriculture, and global food security. Accurate and timely plant disease diagnosis is crucial for effective treatment and minimizing yield losses. Despite advancements in agricultural technology, a precise and early diagnosis remains a challenge, especially in underdeveloped regions where agriculture is crucial and agricultural experts are scarce. However, adopting Deep Learning applications can assist in accurately identifying diseases without needing plant pathologists. In this study, the effectiveness of various computer vision models for detecting paddy diseases is evaluated and proposed the best deep learning-based disease detection system. Both classification and detection using the Paddy Doctor dataset, which contains over 20,000 annotated images of paddy leaves for disease diagnosis are tested and evaluated. For detection, we utilized the YOLOv8 model-based model were used for paddy disease detection and CNN models and the Vision Transformer were used for disease classification. The average mAP50 of 69% for detection tasks was achieved and the Vision Transformer classification accuracy was 99.38%. It was found that detection models are effective at identifying multiple diseases simultaneously with less computing power, whereas classification models, though computationally expensive, exhibit better performance for classifying single diseases. Additionally, a mobile application was developed to enable farmers to identify paddy diseases instantly. Experiments with the app showed encouraging results in utilizing the trained models for both disease classification and treatment guidance.</description><subject>Agriculture</subject><subject>Applications programs</subject><subject>Classification</subject><subject>Computer vision</subject><subject>Deep learning</subject><subject>Diagnosis</subject><subject>Effectiveness</subject><subject>Food supply</subject><subject>Health services</subject><subject>Machine learning</subject><subject>Medical diagnosis</subject><subject>Mobile computing</subject><subject>Plant diseases</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqNjc0KgkAUhYcgSKp3uNBa0BmtaCdatAlaWFuZ9FYjNmPeceHbl2GLdq0OfOdvxBwuhO-uA84nbE5Uep7HlysehsJh3VEWRQeJIpSEkKDF3CqjQeoC4koSqavK5QedSOkbxOZRtxYbOCvqaYr5Xatni7SBCA7moiqEqK6rb82aYRZ-vmZsfJUV4XzQKVvstmm8d-vG9Gs2K03b6LeVCT_gKxHwpSf-S70AaIlNZQ</recordid><startdate>20241208</startdate><enddate>20241208</enddate><creator>Khanal, Bimarsha</creator><creator>Poudel, Paras</creator><creator>Chapagai, Anish</creator><creator>Regmi, Bijan</creator><creator>Pokhrel, Sitaram</creator><creator>Salik Ram Khanal</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20241208</creationdate><title>Paddy Disease Detection and Classification Using Computer Vision Techniques: A Mobile Application to Detect Paddy Disease</title><author>Khanal, Bimarsha ; Poudel, Paras ; Chapagai, Anish ; Regmi, Bijan ; Pokhrel, Sitaram ; Salik Ram Khanal</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_31427342603</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Agriculture</topic><topic>Applications programs</topic><topic>Classification</topic><topic>Computer vision</topic><topic>Deep learning</topic><topic>Diagnosis</topic><topic>Effectiveness</topic><topic>Food supply</topic><topic>Health services</topic><topic>Machine learning</topic><topic>Medical diagnosis</topic><topic>Mobile computing</topic><topic>Plant diseases</topic><toplevel>online_resources</toplevel><creatorcontrib>Khanal, Bimarsha</creatorcontrib><creatorcontrib>Poudel, Paras</creatorcontrib><creatorcontrib>Chapagai, Anish</creatorcontrib><creatorcontrib>Regmi, Bijan</creatorcontrib><creatorcontrib>Pokhrel, Sitaram</creatorcontrib><creatorcontrib>Salik Ram Khanal</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Khanal, Bimarsha</au><au>Poudel, Paras</au><au>Chapagai, Anish</au><au>Regmi, Bijan</au><au>Pokhrel, Sitaram</au><au>Salik Ram Khanal</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Paddy Disease Detection and Classification Using Computer Vision Techniques: A Mobile Application to Detect Paddy Disease</atitle><jtitle>arXiv.org</jtitle><date>2024-12-08</date><risdate>2024</risdate><eissn>2331-8422</eissn><abstract>Plant diseases significantly impact our food supply, causing problems for farmers, economies reliant on agriculture, and global food security. Accurate and timely plant disease diagnosis is crucial for effective treatment and minimizing yield losses. Despite advancements in agricultural technology, a precise and early diagnosis remains a challenge, especially in underdeveloped regions where agriculture is crucial and agricultural experts are scarce. However, adopting Deep Learning applications can assist in accurately identifying diseases without needing plant pathologists. In this study, the effectiveness of various computer vision models for detecting paddy diseases is evaluated and proposed the best deep learning-based disease detection system. Both classification and detection using the Paddy Doctor dataset, which contains over 20,000 annotated images of paddy leaves for disease diagnosis are tested and evaluated. For detection, we utilized the YOLOv8 model-based model were used for paddy disease detection and CNN models and the Vision Transformer were used for disease classification. The average mAP50 of 69% for detection tasks was achieved and the Vision Transformer classification accuracy was 99.38%. It was found that detection models are effective at identifying multiple diseases simultaneously with less computing power, whereas classification models, though computationally expensive, exhibit better performance for classifying single diseases. Additionally, a mobile application was developed to enable farmers to identify paddy diseases instantly. Experiments with the app showed encouraging results in utilizing the trained models for both disease classification and treatment guidance.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | EISSN: 2331-8422 |
ispartof | arXiv.org, 2024-12 |
issn | 2331-8422 |
language | eng |
recordid | cdi_proquest_journals_3142734260 |
source | Free E- Journals |
subjects | Agriculture Applications programs Classification Computer vision Deep learning Diagnosis Effectiveness Food supply Health services Machine learning Medical diagnosis Mobile computing Plant diseases |
title | Paddy Disease Detection and Classification Using Computer Vision Techniques: A Mobile Application to Detect Paddy Disease |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T06%3A22%3A20IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Paddy%20Disease%20Detection%20and%20Classification%20Using%20Computer%20Vision%20Techniques:%20A%20Mobile%20Application%20to%20Detect%20Paddy%20Disease&rft.jtitle=arXiv.org&rft.au=Khanal,%20Bimarsha&rft.date=2024-12-08&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E3142734260%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3142734260&rft_id=info:pmid/&rfr_iscdi=true |