  Sugarcane, a key crop for the world's sugar industry, is prone to several
diseases that have a substantial negative influence on both its yield and
quality. To effectively manage and implement preventative initiatives, diseases
must be detected promptly and accurately. In this study, we present a unique
model called sugarcaneNet2024 that outperforms previous methods for
automatically and quickly detecting sugarcane disease through leaf image
processing. Our proposed model consolidates an optimized weighted average
ensemble of seven customized and LASSO-regularized pre-trained models,
particularly InceptionV3, InceptionResNetV2, DenseNet201, DenseNet169,
Xception, and ResNet152V2. Initially, we added three more dense layers with
0.0001 LASSO regularization, three 30% dropout layers, and three batch
normalizations with renorm enabled at the bottom of these pre-trained models to
improve the performance. The accuracy of sugarcane leaf disease classification
was greatly increased by this addition. Following this, several comparative
studies between the average ensemble and individual models were carried out,
indicating that the ensemble technique performed better. The average ensemble
of all modified pre-trained models produced outstanding outcomes: 100%, 99%,
99%, and 99.45% for f1 score, precision, recall, and accuracy, respectively.
Performance was further enhanced by the implementation of an optimized weighted
average ensemble technique incorporated with grid search. This optimized
sugarcaneNet2024 model performed the best for detecting sugarcane diseases,
having achieved accuracy, precision, recall, and F1 score of 99.67%, 100%,
100%, and 100% , respectively.
