Smart Monitoring and Predictive Detection of Apple Diseases

Integrating IoT and Computer Vision

In partnership with Landseer Ltd, our research endeavours focus on the comprehensive exploration of a scalable smart monitoring platform tailored for apple disease monitoring from fruitlet stage through harvest and into apple storage.

This initiative encompasses a thorough investigation into the most effective strategies to monitor the plants using sensors and hyperspectral cameras to collect data and capture detailed images beyond human vision. Advanced algorithms process the sensors data and images to identify patterns associated with healthy and diseased apples. This includes identifying color changes, spots, deformations, etc. Additionally, machine/deep learning models are trained on vast datasets of images to recognise specific diseases. They become increasingly accurate over time due to their learning capabilities.

By constantly monitoring, the system can alert farmers as soon as signs of diseases are detected. This early warning enables prompt action, potentially preventing the spread of diseases. Moreover, the system accumulates a vast amount of data. This historical data can be used to predict disease outbreaks based on trends and patterns.