{"title":"利用物体检测进行木薯作物病害预测和定位","authors":"Josephat Kalezhi, Langtone Shumba","doi":"10.1016/j.cropro.2024.107001","DOIUrl":null,"url":null,"abstract":"<div><div>In agriculture, early detection and localization of plant diseases in time using deep learning techniques can help farmers contain the spread of plant diseases. In this work, we apply object detection models to identify and localize various categories of cassava plant leaf diseases. These include You Only Look Once (YOLO) as well as Generalized Efficient Layer Aggregation Network(GELAN) models. We applied YOLO v9-e, YOLO v9-c, as well as GELAN-e and GELAN-c models. The models were successfully trained using a custom cassava dataset. Several evaluation indicators that include precision, recall and mean average precision(mAP) were analysed result. The results have been compared with an earlier version of YOLO model and show an improvement in evaluation indicators reaching above 80% in the majority of diseases.</div></div>","PeriodicalId":10785,"journal":{"name":"Crop Protection","volume":"187 ","pages":"Article 107001"},"PeriodicalIF":2.5000,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cassava crop disease prediction and localization using object detection\",\"authors\":\"Josephat Kalezhi, Langtone Shumba\",\"doi\":\"10.1016/j.cropro.2024.107001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In agriculture, early detection and localization of plant diseases in time using deep learning techniques can help farmers contain the spread of plant diseases. In this work, we apply object detection models to identify and localize various categories of cassava plant leaf diseases. These include You Only Look Once (YOLO) as well as Generalized Efficient Layer Aggregation Network(GELAN) models. We applied YOLO v9-e, YOLO v9-c, as well as GELAN-e and GELAN-c models. The models were successfully trained using a custom cassava dataset. Several evaluation indicators that include precision, recall and mean average precision(mAP) were analysed result. The results have been compared with an earlier version of YOLO model and show an improvement in evaluation indicators reaching above 80% in the majority of diseases.</div></div>\",\"PeriodicalId\":10785,\"journal\":{\"name\":\"Crop Protection\",\"volume\":\"187 \",\"pages\":\"Article 107001\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2024-10-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Crop Protection\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0261219424004290\",\"RegionNum\":2,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRONOMY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Crop Protection","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0261219424004290","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRONOMY","Score":null,"Total":0}
Cassava crop disease prediction and localization using object detection
In agriculture, early detection and localization of plant diseases in time using deep learning techniques can help farmers contain the spread of plant diseases. In this work, we apply object detection models to identify and localize various categories of cassava plant leaf diseases. These include You Only Look Once (YOLO) as well as Generalized Efficient Layer Aggregation Network(GELAN) models. We applied YOLO v9-e, YOLO v9-c, as well as GELAN-e and GELAN-c models. The models were successfully trained using a custom cassava dataset. Several evaluation indicators that include precision, recall and mean average precision(mAP) were analysed result. The results have been compared with an earlier version of YOLO model and show an improvement in evaluation indicators reaching above 80% in the majority of diseases.
期刊介绍:
The Editors of Crop Protection especially welcome papers describing an interdisciplinary approach showing how different control strategies can be integrated into practical pest management programs, covering high and low input agricultural systems worldwide. Crop Protection particularly emphasizes the practical aspects of control in the field and for protected crops, and includes work which may lead in the near future to more effective control. The journal does not duplicate the many existing excellent biological science journals, which deal mainly with the more fundamental aspects of plant pathology, applied zoology and weed science. Crop Protection covers all practical aspects of pest, disease and weed control, including the following topics:
-Abiotic damage-
Agronomic control methods-
Assessment of pest and disease damage-
Molecular methods for the detection and assessment of pests and diseases-
Biological control-
Biorational pesticides-
Control of animal pests of world crops-
Control of diseases of crop plants caused by microorganisms-
Control of weeds and integrated management-
Economic considerations-
Effects of plant growth regulators-
Environmental benefits of reduced pesticide use-
Environmental effects of pesticides-
Epidemiology of pests and diseases in relation to control-
GM Crops, and genetic engineering applications-
Importance and control of postharvest crop losses-
Integrated control-
Interrelationships and compatibility among different control strategies-
Invasive species as they relate to implications for crop protection-
Pesticide application methods-
Pest management-
Phytobiomes for pest and disease control-
Resistance management-
Sampling and monitoring schemes for diseases, nematodes, pests and weeds.