Mobile-Based Deep Learning Models for Banana Diseases Detection

2020/04/07 by Sophia Sanga, Victor Mero, Sanga, Sophia +5
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #cs.CV

paper · pdf · doi:10.48550/arxiv.2004.03718

Paper presented at the ICLR 2020 Workshop on Computer Vision for Agriculture (CV4A)

arxiv created 2020/04/07 · arxiv updated 2020/04/09

Abstract

Smallholder farmers in Tanzania are challenged on the lack of tools for early detection of banana diseases. This study aimed at developing a mobile application for early detection of Fusarium wilt race 1 and black Sigatoka banana diseases using deep learning. We used a dataset of 3000 banana leaves images. We pre-trained our model on Resnet152 and Inceptionv3 Convolution Neural Network architectures. The Resnet152 achieved an accuracy of 99.2% and Inceptionv3 an accuracy of 95.41%. On deployment using Android mobile phones, we chose Inceptionv3 since it has lower memory requirements compared to Resnet152. The mobile application on real environment detected the two diseases with a confidence level of 99% of the captured leaf area. This result indicates the potential in improving the yield of bananas by smallholder farmers using a tool for early detection of diseases.

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