A Comprehensive Analysis on Severity Estimation of Plant Diseases by Deep Learning Models

The rapid spread of diseases and pest infestations has a detrimental effect on plant health. Methods for disease classification and detection have gained new insights from the developments in computer vision and deep learning approaches. However, there has been relatively limited work in the area of severity estimation and disease progression rate since it requires more detailed information such as the percentage of affected leaf area, symptoms intensity, and environmental factors that lead to the disease progression over time. Thus the modern agricultural sector faces a significant difficulty in the assessment of plant disease severity and tracking of disease development. The purpose of this study is to perform a thorough review of various deep learning methods for plant disease severity assessment, taking into account factors like accuracy, severity level measurement, and dataset employed. The article also suggests ways to address the shortcomings of the current approach.