EfficientNet-Based Robust Recognition of Peach Plant Diseases in Field Images

Abstract

Plant diseases are a major cause of degraded fruit quality and yield losses. These losses can be significantly reduced with early detection of diseases to ensure their timely treatment, particularly in developing countries. In this regard, an expert system based on deep learning model where the expert knowledge, particularly the one acquired by plant pathologist, is recursively learned by the system and is applied using a smart phone application for use in the target field environment, is being proposed. In this paper, a robust disease detection method is developed based on convolutional neural network (CNN), where its powerful features extraction capabilities are leveraged to detect diseases in images of fruits and leaves. The features extraction pipelines of several state-of-the-art pretrained networks are fine-tuned to achieve optimal detection performance. A novel dataset is collected from peach orchards and extensively augmented using both label-preserving and non-label-preserving transformations. The augmented dataset is used to study the effects of finetuning the pretrained networks' feature extraction pipeline as opposed to keeping the network parameters unchanged. The CNN models, particularly EfficientNet exhibited superior performance on the target dataset once their feature extraction pipelines are fine-tuned. The optimal model is able to achieve 96.6% average accuracy, 90% sensitivity and precision, and 98% specificity on the test set of images.

EFFNet-CA: An Efficient Driver Distraction Detection Based on Multiscale Features Extractions and Channel Attention Mechanism

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An Improved Deep Residual Convolutional Neural Network for Plant Leaf Disease Detection

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Diagnosing Diseases From Fingernail Images

“…EfficientNet (Tan & Le, 2019) is a novel convolutional neural network architecture for analyzing images and is preferred as a prediction model in various disease diagnostic research for humans (Venugopal, Joseph, Das, & Nath, 2022) (Nayak, Padhy, Mallick, Zymbler, & Kumar, 2022) (Wang, Liu, Xie, Yang, & Zhou, 2021) (Ravi, Acharya, & Alazab, 2022) (Zhu et al, 2022) (Marques, Ferreras, & de la Torre-Diez, 2022) and plants (Hanh, Van Manh, & Nguyen, 2022) (Farman et al, 2022) (Atila, Uçar, Akyol, & Uçar, 2021) (Li, Liu, Li, & Liu, 2022). EfficientNet's popularity among deep learning researchers is due to its high classification and prediction performances.…”