Deep Learning Enabled Efficient Net with KLM for Pragmatic Plant Disease Diagnosis

Abstract

Research is still being done on the automated identification and classification of plant diseases. A quick and precise methodology for identifying plant diseases can improve both small-scale commercial crop protection and large-scale food security. Deep learning (DL) methods also enable the monitoring of plant health and the early diagnosis of illnesses. This study's DLEN-KLM model, which is built on EfficientNet and Kernel extreme Learning Machine (KLM) and is designed to diagnose plant diseases intelligently, addresses this issue. In contrast to limited adaptive histogram equalisation as a preprocessing method, the suggested DLEN-KLM model uses median filtering in its construction. A feature extractor built on EfficientNet B0 is also part of the DLEN-KLM model, which is used to create the best feature vectors before categorising them with the KLM model. Utilising a benchmark dataset, the effectiveness of the DLEN-KLM technique is validated. The experimental results showed that the technique outperformed more contemporary methods in terms of disease diagnosis.