A Deep Learning-based Detector for Brown Spot Disease in Passion Fruit Plant Leaves
Pests and diseases pose a key challenge to passion fruit farmers across Uganda and East Africa in general. They lead to loss of investment as yields reduce and losses increases. As the majority of the farmers, including passion fruit farmers, in the country are smallholder farmers from low-income ho...
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creator | Katumba, Andrew Bomera, Moses Mwikirize, Cosmas Namulondo, Gorret Ajero, Mary Gorret Ramathani, Idd Nakayima, Olivia Nakabonge, Grace Okello, Dorothy Serugunda, Jonathan |
description | Pests and diseases pose a key challenge to passion fruit farmers across
Uganda and East Africa in general. They lead to loss of investment as yields
reduce and losses increases. As the majority of the farmers, including passion
fruit farmers, in the country are smallholder farmers from low-income
households, they do not have the sufficient information and means to combat
these challenges. While, passion fruits have the potential to improve the
well-being of these farmers as they have a short maturity period and high
market value , without the required knowledge about the health of their crops,
farmers cannot intervene promptly to turn the situation around.
For this work, we have partnered with the Uganda National Crop Research
Institute (NaCRRI) to develop a dataset of expertly labelled passion fruit
plant leaves and fruits, both diseased and healthy. We have made use of their
extension service to collect images from 5 districts in Uganda,
With the dataset in place, we are employing state-of-the-art techniques in
machine learning, and specifically deep learning, techniques at scale for
object detection and classification to correctly determine the health status of
passion fruit plants and provide an accurate diagnosis for positive
detections.This work focuses on two major diseases woodiness (viral) and brown
spot (fungal) diseases. |
doi_str_mv | 10.48550/arxiv.2007.14103 |
format | Article |
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Uganda and East Africa in general. They lead to loss of investment as yields
reduce and losses increases. As the majority of the farmers, including passion
fruit farmers, in the country are smallholder farmers from low-income
households, they do not have the sufficient information and means to combat
these challenges. While, passion fruits have the potential to improve the
well-being of these farmers as they have a short maturity period and high
market value , without the required knowledge about the health of their crops,
farmers cannot intervene promptly to turn the situation around.
For this work, we have partnered with the Uganda National Crop Research
Institute (NaCRRI) to develop a dataset of expertly labelled passion fruit
plant leaves and fruits, both diseased and healthy. We have made use of their
extension service to collect images from 5 districts in Uganda,
With the dataset in place, we are employing state-of-the-art techniques in
machine learning, and specifically deep learning, techniques at scale for
object detection and classification to correctly determine the health status of
passion fruit plants and provide an accurate diagnosis for positive
detections.This work focuses on two major diseases woodiness (viral) and brown
spot (fungal) diseases.</description><identifier>DOI: 10.48550/arxiv.2007.14103</identifier><language>eng</language><subject>Computer Science - Computer Vision and Pattern Recognition</subject><creationdate>2020-07</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2007.14103$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2007.14103$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Katumba, Andrew</creatorcontrib><creatorcontrib>Bomera, Moses</creatorcontrib><creatorcontrib>Mwikirize, Cosmas</creatorcontrib><creatorcontrib>Namulondo, Gorret</creatorcontrib><creatorcontrib>Ajero, Mary Gorret</creatorcontrib><creatorcontrib>Ramathani, Idd</creatorcontrib><creatorcontrib>Nakayima, Olivia</creatorcontrib><creatorcontrib>Nakabonge, Grace</creatorcontrib><creatorcontrib>Okello, Dorothy</creatorcontrib><creatorcontrib>Serugunda, Jonathan</creatorcontrib><title>A Deep Learning-based Detector for Brown Spot Disease in Passion Fruit Plant Leaves</title><description>Pests and diseases pose a key challenge to passion fruit farmers across
Uganda and East Africa in general. They lead to loss of investment as yields
reduce and losses increases. As the majority of the farmers, including passion
fruit farmers, in the country are smallholder farmers from low-income
households, they do not have the sufficient information and means to combat
these challenges. While, passion fruits have the potential to improve the
well-being of these farmers as they have a short maturity period and high
market value , without the required knowledge about the health of their crops,
farmers cannot intervene promptly to turn the situation around.
For this work, we have partnered with the Uganda National Crop Research
Institute (NaCRRI) to develop a dataset of expertly labelled passion fruit
plant leaves and fruits, both diseased and healthy. We have made use of their
extension service to collect images from 5 districts in Uganda,
With the dataset in place, we are employing state-of-the-art techniques in
machine learning, and specifically deep learning, techniques at scale for
object detection and classification to correctly determine the health status of
passion fruit plants and provide an accurate diagnosis for positive
detections.This work focuses on two major diseases woodiness (viral) and brown
spot (fungal) diseases.</description><subject>Computer Science - Computer Vision and Pattern Recognition</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj1FLwzAUhfPig0x_gE_mD7TetGnTPM7NqVBwsL2Xm-xWAjMtSZz6782mcA4HDocDH2N3AkrZNQ08YPh2p7ICUKWQAuprtlvyNdHMe8LgnX8vDEY65C6RTVPgY_ZjmL48381T4msXKQ-483yLMbrJ8034dIlvj-jT-eVE8YZdjXiMdPufC7bfPO1XL0X_9vy6WvYFtqouNAKgFmCaUSstEIykjgSZLGtMhZakaUQrVXsw1UiUSwsdWq1UpVVXL9j93-2FapiD-8DwM5zphgtd_QuipEp5</recordid><startdate>20200728</startdate><enddate>20200728</enddate><creator>Katumba, Andrew</creator><creator>Bomera, Moses</creator><creator>Mwikirize, Cosmas</creator><creator>Namulondo, Gorret</creator><creator>Ajero, Mary Gorret</creator><creator>Ramathani, Idd</creator><creator>Nakayima, Olivia</creator><creator>Nakabonge, Grace</creator><creator>Okello, Dorothy</creator><creator>Serugunda, Jonathan</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20200728</creationdate><title>A Deep Learning-based Detector for Brown Spot Disease in Passion Fruit Plant Leaves</title><author>Katumba, Andrew ; Bomera, Moses ; Mwikirize, Cosmas ; Namulondo, Gorret ; Ajero, Mary Gorret ; Ramathani, Idd ; Nakayima, Olivia ; Nakabonge, Grace ; Okello, Dorothy ; Serugunda, Jonathan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a673-9a00a910b5f9791a0b4e8e1eb1ebcbb2ace4b516476db2feecbbc08ac97729783</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Computer Science - Computer Vision and Pattern Recognition</topic><toplevel>online_resources</toplevel><creatorcontrib>Katumba, Andrew</creatorcontrib><creatorcontrib>Bomera, Moses</creatorcontrib><creatorcontrib>Mwikirize, Cosmas</creatorcontrib><creatorcontrib>Namulondo, Gorret</creatorcontrib><creatorcontrib>Ajero, Mary Gorret</creatorcontrib><creatorcontrib>Ramathani, Idd</creatorcontrib><creatorcontrib>Nakayima, Olivia</creatorcontrib><creatorcontrib>Nakabonge, Grace</creatorcontrib><creatorcontrib>Okello, Dorothy</creatorcontrib><creatorcontrib>Serugunda, Jonathan</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Katumba, Andrew</au><au>Bomera, Moses</au><au>Mwikirize, Cosmas</au><au>Namulondo, Gorret</au><au>Ajero, Mary Gorret</au><au>Ramathani, Idd</au><au>Nakayima, Olivia</au><au>Nakabonge, Grace</au><au>Okello, Dorothy</au><au>Serugunda, Jonathan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Deep Learning-based Detector for Brown Spot Disease in Passion Fruit Plant Leaves</atitle><date>2020-07-28</date><risdate>2020</risdate><abstract>Pests and diseases pose a key challenge to passion fruit farmers across
Uganda and East Africa in general. They lead to loss of investment as yields
reduce and losses increases. As the majority of the farmers, including passion
fruit farmers, in the country are smallholder farmers from low-income
households, they do not have the sufficient information and means to combat
these challenges. While, passion fruits have the potential to improve the
well-being of these farmers as they have a short maturity period and high
market value , without the required knowledge about the health of their crops,
farmers cannot intervene promptly to turn the situation around.
For this work, we have partnered with the Uganda National Crop Research
Institute (NaCRRI) to develop a dataset of expertly labelled passion fruit
plant leaves and fruits, both diseased and healthy. We have made use of their
extension service to collect images from 5 districts in Uganda,
With the dataset in place, we are employing state-of-the-art techniques in
machine learning, and specifically deep learning, techniques at scale for
object detection and classification to correctly determine the health status of
passion fruit plants and provide an accurate diagnosis for positive
detections.This work focuses on two major diseases woodiness (viral) and brown
spot (fungal) diseases.</abstract><doi>10.48550/arxiv.2007.14103</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Computer Vision and Pattern Recognition |
title | A Deep Learning-based Detector for Brown Spot Disease in Passion Fruit Plant Leaves |
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