# Mobile-Based Deep Learning Models for Banana Diseases Detection

arXiv: https://arxiv.org/abs/2004.03718 · HTML: https://mlsanity.com/paper/2004.03718/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 1 of 1)

- [Raw Produce Quality Detection with Shifted Window Self-Attention](https://mlsanity.com/paper/2112.13845/) (2021)

## Abstract (arXiv)

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.