Sailfish Optimizer with EfficientNet Model for Apple Leaf Disease Detection

Recent developments in digital cameras and electronic gadgets cou-pled with Machine Learning (ML) and Deep Learning (DL)-based automated apple leaf disease detection models are commonly employed as reasonable alternatives to traditional visual inspection models. In this background, the current paper devises an Effective Sailfish Optimizer with EfficientNet-based Apple Leaf disease detection (ESFO-EALD) model. The goal of the proposed ESFO-EALD technique is to identify the occurrence of plant leaf diseases automatically. In this scenario, Median Filtering (MF) approach is utilized to boost the quality of apple plant leaf images. Moreover, SFO with Kapur???s entropy-based segmentation technique is also utilized for the identification of the affected plant region from test image. Furthermore, Adam optimizer with EfficientNet-based feature extraction and Spiking Neural Network (SNN)-based classification are employed to detect and classify the apple plant leaf images. A wide range of simulations was conducted to ensure the effective outcomes of ESFO-EALD technique on benchmark dataset. The results reported the supremacy of the proposed ESFO-EALD approach than the existing approaches.

Automated summary

Recent advancements in digital cameras and electronic gadgets have led to the development of automated apple leaf disease detection models based on Machine Learning and Deep Learning techniques, which are viable alternatives to traditional visual inspection models. This paper proposes an ESFO-EALD model, utilizing Effective Sailfish Optimizer and EfficientNet, for automatic detection of plant leaf diseases. Median Filtering is employed to enhance the quality of apple plant leaf images, while SFO with Kapur's entropy-based segmentation technique is used to identify the affected plant region. Adam optimizer, EfficientNet-based feature extraction, and Spiking Neural Network classification are employed for detecting and classifying apple plant leaf images. Extensive simulations validate the effectiveness of the ESFO-EALD technique on benchmark datasets, showing its superiority over existing approaches.