Mobile Machine Learning Application for Early Detection of Cassava Diseases

• Babatunde Oluwamayokun Soyoye

  Federal University of Technology Akure

• Abeebullah Bayonle Oguntayo

• Peter Adeniyi Adeduntan

• Timothy Oluwadamilare Adisa

Cassava is a major food crop in Nigeria, yet its production is severely threatened by diseases such as Cassava Bacterial Blight (CBB), Cassava Brown Streak Disease (CBSD), Cassava Green Mite (CGM), and Cassava Mosaic Disease (CMD). Timely and accurate detection of these diseases is crucial for minimizing crop losses and improving food security. Hence, this study evaluates the performance of a cassava disease detection mobile application developed using TensorFlow machine learning models. The app classifies cassava diseases based on leaf images and was tested on both young and mature leaf stages to assess its accuracy, precision, recall, and F1 score. A hybrid data collection approach combining onsite farm data from the Federal University of Technology, Akure, and online datasets was employed. Results showed an overall accuracy of 77.44% for mature leaves and 75.60% for young leaves, demonstrating strong reliability in identifying common cassava diseases. The app exhibited high precision and recall values across most disease categories, indicating its potential as an efficient, accessible, and cost-effective diagnostic tool that could be integrated and used by farmers. The study concludes that the integration of machine learning into mobile applications can significantly enhance early detection and management of cassava diseases, contributing to improved agricultural productivity and food security.

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