Machine Learning in Cereal Crops Disease Detection: A Review
Cereals are an important and major source of the human diet. They constitute more than two-thirds of the world’s food source and cover more than 56% of the world’s cultivatable land. These important sources of food are affected by a variety of damaging diseases, causing significant loss in annual pr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/15/3/75 |
| _version_ | 1797473278730698752 |
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| author | Fraol Gelana Waldamichael Taye Girma Debelee Friedhelm Schwenker Yehualashet Megersa Ayano Samuel Rahimeto Kebede |
| author_facet | Fraol Gelana Waldamichael Taye Girma Debelee Friedhelm Schwenker Yehualashet Megersa Ayano Samuel Rahimeto Kebede |
| author_sort | Fraol Gelana Waldamichael |
| collection | DOAJ |
| description | Cereals are an important and major source of the human diet. They constitute more than two-thirds of the world’s food source and cover more than 56% of the world’s cultivatable land. These important sources of food are affected by a variety of damaging diseases, causing significant loss in annual production. In this regard, detection of diseases at an early stage and quantification of the severity has acquired the urgent attention of researchers worldwide. One emerging and popular approach for this task is the utilization of machine learning techniques. In this work, we have identified the most common and damaging diseases affecting cereal crop production, and we also reviewed 45 works performed on the detection and classification of various diseases that occur on six cereal crops within the past five years. In addition, we identified and summarised numerous publicly available datasets for each cereal crop, which the lack thereof we identified as the main challenges faced for researching the application of machine learning in cereal crop detection. In this survey, we identified deep convolutional neural networks trained on hyperspectral data as the most effective approach for early detection of diseases and transfer learning as the most commonly used and yielding the best result training method. |
| first_indexed | 2024-03-09T20:12:30Z |
| format | Article |
| id | doaj.art-46e013dc72134a3e97c3f43479b1a8f1 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-09T20:12:30Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-46e013dc72134a3e97c3f43479b1a8f12023-11-24T00:08:47ZengMDPI AGAlgorithms1999-48932022-02-011537510.3390/a15030075Machine Learning in Cereal Crops Disease Detection: A ReviewFraol Gelana Waldamichael0Taye Girma Debelee1Friedhelm Schwenker2Yehualashet Megersa Ayano3Samuel Rahimeto Kebede4Ethiopian Artificial Intelligence Institute, Addis Ababa 40782, EthiopiaEthiopian Artificial Intelligence Institute, Addis Ababa 40782, EthiopiaInstitute of Neural Information Processing, Ulm University, 89081 Ulm, GermanyEthiopian Artificial Intelligence Institute, Addis Ababa 40782, EthiopiaEthiopian Artificial Intelligence Institute, Addis Ababa 40782, EthiopiaCereals are an important and major source of the human diet. They constitute more than two-thirds of the world’s food source and cover more than 56% of the world’s cultivatable land. These important sources of food are affected by a variety of damaging diseases, causing significant loss in annual production. In this regard, detection of diseases at an early stage and quantification of the severity has acquired the urgent attention of researchers worldwide. One emerging and popular approach for this task is the utilization of machine learning techniques. In this work, we have identified the most common and damaging diseases affecting cereal crop production, and we also reviewed 45 works performed on the detection and classification of various diseases that occur on six cereal crops within the past five years. In addition, we identified and summarised numerous publicly available datasets for each cereal crop, which the lack thereof we identified as the main challenges faced for researching the application of machine learning in cereal crop detection. In this survey, we identified deep convolutional neural networks trained on hyperspectral data as the most effective approach for early detection of diseases and transfer learning as the most commonly used and yielding the best result training method.https://www.mdpi.com/1999-4893/15/3/75cereal cropplant diseasemachine learningdeep learning |
| spellingShingle | Fraol Gelana Waldamichael Taye Girma Debelee Friedhelm Schwenker Yehualashet Megersa Ayano Samuel Rahimeto Kebede Machine Learning in Cereal Crops Disease Detection: A Review Algorithms cereal crop plant disease machine learning deep learning |
| title | Machine Learning in Cereal Crops Disease Detection: A Review |
| title_full | Machine Learning in Cereal Crops Disease Detection: A Review |
| title_fullStr | Machine Learning in Cereal Crops Disease Detection: A Review |
| title_full_unstemmed | Machine Learning in Cereal Crops Disease Detection: A Review |
| title_short | Machine Learning in Cereal Crops Disease Detection: A Review |
| title_sort | machine learning in cereal crops disease detection a review |
| topic | cereal crop plant disease machine learning deep learning |
| url | https://www.mdpi.com/1999-4893/15/3/75 |
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