Mobile-based Deep Learning Models for Banana Disease Detection

  • S. L. Sanga Nelson Mandela African Institute of Science and Technology, Tanzania
  • D. Machuve Nelson Mandela African Institute of Science and Technology, Tanzania
  • K. Jomanga International Institute of Tropical Agriculture, Tanzania
Keywords: deep learning, banana diseases, smartphones, early detection, android based application


In Tanzania, smallholder farmers contribute significantly to banana production and Kagera, Mbeya, and Arusha are among the leading regions. However, pests and diseases are a threat to food security. Early detection of banana diseases is important to identify the diseases before too much damage is done on the plants. In this paper, a tool for early detection of banana diseases by using a deep learning approach is proposed. Five deep learning architectures, namely Vgg16, Resnet18, Resnet50, Resnet152 and InceptionV3 were used to develop models for banana disease detection, achieving all high accuracies, varying from 95.41% for InceptionV3 to 99.2% for Resnet152. InceptionV3 was selected for mobile deployment because it demands much less memory. The developed tool was capable of detecting diseases with a confidence of 99% of the captured leaves from the real environment. This tool will help smallholder farmers conduct early detection of banana diseases and improve their productivity.


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