# Using Transfer Learning for Image-Based Cassava Disease Detection

arXiv: https://arxiv.org/abs/1707.03717 · HTML: https://mlsanity.com/paper/1707.03717/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2017.

## Built on by (most cited first, 12 of 12)

- [Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection](https://mlsanity.com/paper/2106.07770/) (2021)
- [The Power of Transfer Learning in Agricultural Applications: AgriNet](https://mlsanity.com/paper/2207.03881/) (2022)
- [Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications](https://mlsanity.com/paper/2301.01200/) (2022)
- [Image Quality Assessment for Foliar Disease Identification (AgroPath)](https://mlsanity.com/paper/2209.12443/) (2022)
- [Hierarchical Object Detection and Recognition Framework for Practical Plant Disease Diagnosis](https://mlsanity.com/paper/2407.17906/) (2024)
- [Application of Machine Learning in understanding plant virus pathogenesis: Trends and perspectives on emergence, diagnosis, host-virus interplay and management](https://mlsanity.com/paper/2112.01998/) (2021)
- [Unveiling the frontiers of deep learning: innovations shaping diverse domains](https://mlsanity.com/paper/2309.02712/) (2023)
- [OpenAg: Democratizing Agricultural Intelligence](https://mlsanity.com/paper/2506.04571/) (2025)
- [Crop Pest Classification Using Deep Learning Techniques: A Review](https://mlsanity.com/paper/2507.01494/) (2025)
- [DenseSwinV2: Channel Attentive Dual Branch CNN Transformer Learning for Cassava Leaf Disease Classification](https://mlsanity.com/paper/2603.25935/) (2026)
- [A Light Weight Multi-Features-View Convolution Neural Network For Plant Disease Identification](https://mlsanity.com/paper/2605.00903/) (2026)
- [Evaluating Contextual Bias in CNN Image Classification: Evidence from Agricultural Benchmark Datasets](https://mlsanity.com/paper/2609.14654/) (2026)

## Abstract (arXiv)

Cassava is the third largest source of carbohydrates for human food in the world but is vulnerable to virus diseases, which threaten to destabilize food security in sub-Saharan Africa. Novel methods of cassava disease detection are needed to support improved control which will prevent this crisis. Image recognition offers both a cost effective and scalable technology for disease detection. New transfer learning methods offer an avenue for this technology to be easily deployed on mobile devices. Using a dataset of cassava disease images taken in the field in Tanzania, we applied transfer learning to train a deep convolutional neural network to identify three diseases and two types of pest damage (or lack thereof). The best trained model accuracies were 98% for brown leaf spot (BLS), 96% for red mite damage (RMD), 95% for green mite damage (GMD), 98% for cassava brown streak disease (CBSD), and 96% for cassava mosaic disease (CMD). The best model achieved an overall accuracy of 93% for data not used in the training process. Our results show that the transfer learning approach for image recognition of field images offers a fast, affordable, and easily deployable strategy for digital plant disease detection.