Date of Publication :17th January 2024

Abstract:Coffee plant can be damaged by a variety of pests and diseases, which are favoured or inhibited by specific climatic conditions like temperature and humidity. As a consequence of climate change, these pests could increase their distribution across a range of territories, infecting plantations at a global level.

Artificial Intelligence based systems can help in monitoring and prevent the rising of plagues in crops. Here it is presented a prototype called Coffee Crops Semaphore, which is a web application that estimates the probability of the presence of diseases in coffee bushes from data collected in situ regarding values and indicators of its phenology (v. gr. relative humidity, temperature and date of reading). It does offer a dashboard to visualise the estimations, presented as a traffic-light indicator for a better understanding of the potential risk, with other functionalities like uploading new data banks, and applying different Machine Learning algorithms or models to analyse them.

What is presented here belongs to a first phase of the Coffee Crops Semaphore, called Coffee Rust Semaphore, which is specifically oriented to the estimation of the presence of this fungus (Hemilea vastarix), based on data banks and images at mesosystem level (coffee plant).

Reference :