Optimizing papaya yield: the evaluation of deep learning models for automated disease detection

Abstract

The current research will create a robust and successful deep learning (DL) system to recognize and classify papaya leaf diseases. The traditional disease detection techniques are both time-consuming and unreliable, and extensively rely on expert knowledge, therefore limiting them in terms of scalability in agricultural practice. To tackle this issue, the convolutional neural network (CNN)-based method is suggested and tested on the BDPapayaLeaf that includes 2,159 images of papaya leaf with four disease categories and healthy papaya leaves, i.e., anthracnose, bacterial spot, leaf curl (reversal), and ring spot. The data was split into training 80%, validation 10%, and testing 10% data. Pictures were downscaled to 224×224 and normalized before training. Six trained CNN structures VGG16, VGG19, InceptionV3, DenseNet121, MobileNetV2, and ResNet50 were examined. The top model in terms of classification accuracy, according to them, was InceptionV3 with 89% in terms of classification accuracy, showing a high level of performance on true positive and false negative. The findings indicate that DL is an effective and precise method of automated detection of papaya leaf disease and is useful in improving precision and reliability in agricultural diagnostics.