Hybrid CNN & Random Forest Model for Effective Rosemary Leaf Disease Diagnosis

As developments in image recognition and machine learning continue to alter the natural environment of plant pathology, this study investigates the classification of rosemary leaves according to various illnesses using deep neural networks (CNN) convolutions as well as Random Forest techniques. Dusty Mildew, Botrytis Blight, Root Decay, Leaf Spot Rosemary Beetle Aphid Infection, Rust, or Cercospora Leaf Spot is one of eight forms of rosemary disease of the leaves evaluated in this study. The classification model's efficacy has been thoroughly evaluated for each sickness class, including accuracy, recall, or F1-Score assessments. Precision ratings ranging from 93.88% to 94.62% demonstrate the system's ability to accurately recognize incidents within each sickness category. The accuracy values range from 93.92% to 94.59%, showing that the model is capable of recognizing genuine positive instances within each class. In addition, F1-Score results with a steady average of 94.26% support the model's harmonic balance of recall with accuracy. The support column shows the total amount of cases of every circumstance in the educational setting, which gives insight into the dataset's structure. This data is critical for estimating the prevalence of various ailments among the rosemary leaves. The model's resilience is supported by an overall precision of 98%, proving its ability to forecast accurately across all classes. The study employs macro, weighted, or micro average indicators, along with class-specific information, to conduct an in-depth assessment of the model's efficacy across all categories. The large-scale, weighted, and micro mean accuracy, memory, or total F1-Score values have stayed consistent at 94.26%, indicating that the model applies to others. This paper contributes to the field of agricultural pathology and also to the larger context of computerized crop illness recognition.