Customized Weighted Ensemble of Modified Transfer Learning Models for the Detection of Sugarcane Leaf Diseases

Sugarcane is the primary crop in the global sugar industry, yet it remains highly susceptible to a wide range of diseases that significantly impact its yield and quality. An effective solution is required to address the issues caused by the manual identification of plant diseases, which is time-consuming and has low detection accuracy. This paper proposes the development of a robust Deep Ensemble Convolutional Neural Network (DECNN) model for the accurate detection of sugarcane leaf diseases. Initially, several transfer learning (TL) models, including EfficientNetB0, MobileNetV2, DenseNet121, NASNetMobile, and EfficientNetV2B0, were enhanced through the addition of specific layers. A comparative analysis was then conducted on the enlarged dataset of sugarcane leaf diseases, which was divided into six categories and 4800 images. The application of data augmentation, along with the addition of dense layers, batch normalization layers, and dropout layers, led to improved detection accuracy, precision, recall, and F1 scores for each model. Among the five enhanced transfer learning models, the modified EfficientNetB0 model demonstrated the highest detection accuracy, ranging from 97.08% to 98.54%. In conclusion, the DECNN model was developed by integrating the modified EfficientNetB0, MobileNetV2, and DenseNet121 models using a distinctive performance-based custom-weighted ensemble method, with weight optimization carried out using the Tree-structured Parzen Estimator (TPE) technique. This resulted in a model that achieved a detection accuracy of 99.17%, which outperformed the individual performance of the modified EfficientNetB0, MobileNetV2, and DenseNet121 models in detecting sugarcane leaf diseases.