Artificial intelligence in phytoplasmology: Image-based disease detection models for phytoplasma infection in flower crops

In floriculture, the economic products are the flowers; phytoplasma infection in domestic cultivations results in economic loss to the farmer. Farmers often lack the expertise to identify the infection due to the lack of awareness about the diseases. Similarly, in nursery plants, phytoplasma infections are often confused as novel plant types. Thus, an interactive expert system can help in appropriate identification of phytoplasma presence in flower crops. As a preliminary step towards it, an image-based disease prediction model for phytoplasma infection in China aster has been developed. The accuracy per class for healthy and infected was 91% and precision, recall and F1 score are observed to be 0.91 respectively. Such technologies are the need of the hour which can help in improving the accuracy and speed of disease deetection, allowing farmers to respond quickly and effectively to disease outbreaks.