Fungal Infection Diseases- Nightmare for Cannabis Industries: Artificial Intelligence Applications

Abstract

This review paper highlights the fungal diseases of both indoor and outdoor Cannabis cultivation environments and discusses the Artificial intelligence (AI) based crop disease detection and management. Some of the fungal pathogen that can attack Cannabis crops are Botrytis, Alternaria, Fusarium, Penicillium, Cladosporium, and Aspergillus. Fungal diseases are Powdery Mildew, Damping off, and Mildew. Of these fungal pathogens, the most common inflorescence disease is gray mold, caused by Botrytis cinerea. Botrytis cinerea and Erysiphe species complex are currently the most widespread pathogens of Cannabis worldwide. The greatest challenge facing Cannabis and hemp producers is the management of insect pests and pathogens that attack the roots, leaves and inflorescences. Pathogens are a pain in the neck of every Cannabis breeder. They affect the quality and quantity of yield, thus defeating the aim of cultivation. The common disease management strategies are-remove and destroy infected plants. Irradiate dried buds with gamma or electro-beam radiation. Another method is to apply biological control agents at rooting and vegetative stages of growth. Pesticides have been found in all Cannabis products, from flowers to edibles, vapes, and smokes. The pesticide pandemic in the Cannabis industry needs urgent attention. Cannabis can contain fungal pathogens and residues of pesticides, fungicides that cause serious and often fatal infections in persons with immunocompromised conditions, such as cancer, transplant, or infection with HIV. Contamination of Cannabis plants and products (i.e., recreational- and pharmaceutical-grades) with mycotoxigenic organisms, including species of Aspergillus, Penicillium, and Fusarium, pose serious challenges. The manual Cannabis disease identification process is time-consuming and tedious work. Instead, automated methods save both time and effort. The technology of Artificial Intelligence (AI) in the detection and management of disease has already been employed in many crops. The machine learning (ML)-based models were proposed for the identification and classification of plant diseases. The PlantVillage dataset is the largest and most studied plant disease dataset, which is used as a reference for the disease detection and management of plant diseases.