| Autor: |
Adedoja, Adedamola O., Owolawi, Pius A., Mapayi, Temitope, Chunling Tu |
| Předmět: |
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| Zdroj: |
IAENG International Journal of Computer Science; Mar2022, Vol. 49 Issue 1, p216-231, 16p |
| Abstrakt: |
Plant diseases remain a threat to global food supply, as it causes unimaginable loss of food and revenue. Intelligent mobile plant disease diagnostic system has however become valuable due to its usefulness for the early diagnosis and detection of plant diseases using leave images even when there are no availability of competent and adequate experts in such locations. The objective of this paper is to develop an intelligent mobile plant disease diagnostic system that runs on a smartphone. The diagnostic system is based on NASNet-Mobile, a lightweight convolutional neural network (CNN) architecture using the images of the plant leaves for plant disease diagnosis. A mobile application is developed for both android and iOS smartphones to capture the plant leaf images. The system runs on a web service that gets diagnosis from the CNN model. The plant leave images captured using the developed mobile application are sent through the web service and the recognition of the plant disease is achieved using NASNet-Mobile CNN model. The proposed NASNet-Mobile CNN model plant disease diagnostic system achieved an accuracy rate of 99.31%. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Supplemental Index |
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