Deep Learning in Plant Diseases Detection for Agricultural Crops
| Autor: | M. T. Afify, Mohamed Loey, Ahmed El-Sawy |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
education.field_of_study
Multidisciplinary General Computer Science Scope (project management) business.industry Computer science Deep learning 010401 analytical chemistry Population General Engineering Image processing 02 engineering and technology 01 natural sciences General Business Management and Accounting Data science Plant disease Field (computer science) 0104 chemical sciences Agriculture 0202 electrical engineering electronic engineering information engineering Food processing 020201 artificial intelligence & image processing Artificial intelligence business education |
| Zdroj: | International Journal of Service Science, Management, Engineering, and Technology. 11:41-58 |
| ISSN: | 1947-9603 1947-959X |
| DOI: | 10.4018/ijssmet.2020040103 |
| Popis: | Deep learning has brought a huge improvement in the area of machine learning in general and most particularly in computer vision. The advancements of deep learning have been applied to various domains leading to tremendous achievements in the areas of machine learning and computer vision. Only recent works have introduced applying deep learning to the field of using computers in agriculture. The need for food production and food plants is of utmost importance for human society to meet the growing demands of an increased population. Automatic plant disease detection using plant images was originally tackled using traditional machine learning and image processing approaches resulting in limited accuracy results and a limited scope. Using deep learning in plant disease detection made it possible to produce higher prediction accuracies as well as broadened the scope of detected diseases and plant species considered. This article presents a survey of research papers that presented the use of deep learning in plant disease detection, and analyzes them in terms of the dataset used, models employed, and overall performance achieved. |
| Databáze: | OpenAIRE |
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