Transfer Learning-based Plant Disease Detection and Diagnosis System using Xception

Moid, Mohammed Abdul; Chaurasia, Mousmi Ajay

doi:10.1109/i-smac52330.2021.9640694

Cited by 10 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Likewise, the InceptionResNetV2 model we propose has an accuracy of 98.76%, higher than the model proposed by [22] of 98.64%. This positive result is followed by the Xception model we propose to get almost the same results as the Xception model proposed by [46], which are 97.58% and 97.60%, respectively. Unfortunately, our MobileNetV2 model did not follow these positive results.…”

Section: Research Analysis and Limitationssupporting

confidence: 58%

Convolutional Neural Network (CNN) Models for Crop Diseases Classification

Sutaji

Rosyid

2022

KINETIK

View full text Add to dashboard Cite

Crop diseases have a significant impact on agricultural production. As a result, early diagnosis of crop diseases is critical. Deep learning approaches are now promising to improve disease detection. Convolutional Neural Network (CNN) models can detect crop disease using images with automatic feature extraction. This study proposes crop disease classification considering ten pre-trained CNN models. Fine-tuning for each model was conducted in the Plant Village dataset. The experimental results show that fine-tuning improves the model’s performance with an average accuracy of 8.85%. The best CNN model was DenseNet121, with 94.48% and 98.97% accuracy for freezing all layers and unfreezing last block convolution layers. Moreover, fine-tuning produces less time-consuming with an average of 2.20 hours. VGG19 is the less time-consuming reduction by 8 hours. On the other hand, MobileNetV2 is the second-best performance model with less time-consuming than DenseNet121, and produces fewer parameters, which is affordable for embedding it to mobile devices.

show abstract

Section: Research Analysis and Limitationssupporting

confidence: 58%