Utilizing TabPFN-Transformer on IoT Environmental Data for Early Prediction of Grapevines Diseases

Plant diseases caused by pathogens, that affect vineyards can cause serious damage and lead to reduced quantity and quality of fruits. Specifically for grapevines, diseases such as downy mildew and powdery mildew can cause yield loss, affect the size of the grapes, their ability to accumulate sugars affecting the flavor and aroma negatively and increase the need for fungicidal sprays to come up with these diseases and the pathogens that cause them. Clearly, it is important to early predict these diseases and timely apply treatment to mitigate the effects of diseases for the crop production. This study presents a workflow in which IoT environmental sensors and Machine Learning methods are leveraged for the early prediction of grapevine diseases, to accurately predict disease onset that allows for timely fungicide applications or other disease management strategies.