EfficientNet based Convolutional Neural Network for Visual Plant Disease Detection
Crops grown in tropical, subtropical, and temperate climates are subject to a variety of diseases and pests. Plant illnesses are complicated by interactions between the virus, the host plant, and the insect. In this paper, we present a robust transfer-learning-based detector for real-time plant dise...
| Published in: | 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2022 |
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| Main Author: | |
| Format: | Conference paper |
| Language: | English |
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Institute of Electrical and Electronics Engineers Inc.
2022
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133381132&doi=10.1109%2fECTI-CON54298.2022.9795496&partnerID=40&md5=52772a29c111bf47eb0d0b80cb66bf83 |
| Summary: | Crops grown in tropical, subtropical, and temperate climates are subject to a variety of diseases and pests. Plant illnesses are complicated by interactions between the virus, the host plant, and the insect. In this paper, we present a robust transfer-learning-based detector for real-time plant disease detection with PlantDoc datasets and plant village dataset. EfficientNetV2 architecture is more efficient, high speed and accurate in compared with EfficientNetV1. The EfficientNetV2 with the pretrained weights, Image net and plant village obtain the highest score in accuracy with 74%,F1-score=0.74 if compared to other benchmarked models. © 2022 IEEE. |
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| DOI: | 10.1109/ECTI-CON54298.2022.9795496 |