Association of meteorological variables with leaf spot and fruit rot disease incidence in eggplant and YOLOv8-based disease classification

• Published in: Ecological informatics 2024-11, Vol.83, p.102809, Article 102809
• Main Authors: Kaniyassery, Arya, Goyal, Ayush, Thorat, Sachin Ashok, Rao, Mattu Radhakrishna, Chandrashekar, Harsha K., Murali, Thokur Sreepathy, Muthusamy, Annamalai
• Format: Article
• Language: English
• Subjects: Disease incidence; Eggplant; Fruit rot; Leaf spot; Weather parameters; YOLOv8
• ISSN: 1574-9541
• DOI: 10.1016/j.ecoinf.2024.102809

Eggplant is one of the major vegetables consumed worldwide. Several fungal, bacterial, and viral diseases challenge the yield and quality of eggplant. The incidence of plant diseases is strongly influenced by weather factors such as temperature, humidity, rainfall, and wind speed. Mattu Gulla (MG) is a GI-tagged traditional variety of eggplant grown in Mattu village of the Udupi district in Karnataka state, India, with a cultural legacy of more than four centuries. In this study, we investigated the relationships between weather parameters and disease incidence in Mattu Gulla. Leaf spot (LS) and fruit rot (FR) are the major diseases affecting this plant variety. The influence of plant age and weather parameters on the modulation of the disease incidence (%) [DI (%)] of leaf spot and fruit rot was recorded and analyzed via correlation and regression. Prediction equations for disease incidence was derived via regression. A significant negative correlation was observed between the leaf spot DI (%) and minimum temperature (Min. temp), and a positive correlation was observed between the DI (%) and fruit rot. In the case of FR, the DI (%) is also significantly positively correlated with wind speed (WS), temperature, maximum relative humidity (RH I), rainfall (RF), and wind speed (WS). An RH I of 86–87 % was favorable for the incidence of fruit rot in the field. Regression analysis revealed a significant association between Min. temp and leaf spot DI (%), and in the case of fruit rot DI (%), the association was with Min. temp and WS. An android application, “Leaf Guard,” has been developed for AI-based disease detection in eggplant. During testing, the accuracy of the trained model reached 98.2 %. •The relationship between weather parameters on the leaf spot and fruit rot disease incidence in Mattu Gulla is studied.•Predictionequations for disease incidence was derived using regression.•Our YOLOv8x model showed the ability to isolate the region of interest with high precision and accuracy.•An android application, “Leaf Guard,” has been developed for disease classification.