Enhanced Classification of Turmeric Leaf Disease Severity Levels Using an Optimized Hybrid Deep Learning Model: LevelPrecisionRecallF1-scoreAccuracy

During a study that was conducted in India on turmeric fields, researchers found that various types of turmeric had a prevalence of turmeric leaf spot disease ranging from 6% to 28%. Traditional methods for disease prediction in turmeric crops have several limitations, including high costs for procuring the data input mandatory to run the model, a lack of regional data, and an insufficient dataset. In this study, we investigate the Turmeric problem while also using the hybrid convolutional neural networks (CNN) with SVM for Leaf spot severity level classification. This study looks at the infection that may be found on turmeric leaves. First, the unhealthy plant is captured on camera with the help of secondary sources. To extract and categorize the characteristics of the sickness, CNN –SVM deep learning algorithms are employed to their fullest potential. The results of the research revealed an accuracy of 97% for the Leaf spot Turmeric Disease severity level classification.