Hybrid Model for EffectivePapaya Leaf Disease Diagnosis

Papaya production faces significant challenges as a result of numerous leaf diseases that impair yield and quality. This study focuses on the automated identification of prevalent papaya leaf diseases using a model for classification constructed using neural networks using convolution, often known as CNNs and Random Forest algorithms. The research includes an in- depth assessment of illness classification efficacy, with a focus on recall, precision, F1-score, support, and metrics for accuracy across a variety of groups, including PRSV, Dusty Mildew, bacterial Leaves Spot, which is Anthracnose and Black Spot, Cercospora The leaves Spot, as well as Angular Leaf Spot. The results show that the algorithm performs well, with precision values consistently exceeding 93% for each disease class. The high recall values of the model, which reach 92%, reflect its ability to detect true positive occurrences. The F1-score, which assesses the balance of recall and accuracy, frequently exceeds 94%, proving the persistence of the categorization model. The backing numbers offer data on the occurrence distribution of every illness in the dataset. The recommended model's effectiveness in differentiating between various papaya leaf ailments is evidenced by an average weighted precision of 94.49%. The micro, as well as macro averages, demonstrate the model's stability in performance across various courses, resulting in the micro average yielding a value of 94.49%. These findings help to develop automated assessment and treatment in papaya growing, providing farmers with a useful tool for making early and precise choices regarding management. The proposed methodology generates positive results, paving the path for better disease management and papaya yields.