Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks

By Peng Jiang, Yuehan Chen, Bin Liu, Dongjian He, Chunquan Liang. IEEE Access, 2019

654 citations recorded by the source corpus.

Alternaria leaf spot, Brown spot, Mosaic, Grey spot, and Rust are five common types of apple leaf diseases that severely affect apple yield. However, the existing research lacks an accurate and fast detector of apple diseases for ensuring the healthy development of the apple industry. This paper proposes a deep learning approach that is based on improved convolutional neural networks (CNNs) for the real-time detection of apple leaf diseases. In this paper, the apple leaf disease dataset (ALDD), which is composed of laboratory images and complex images under real field conditions, is first constructed via data augmentation and image annotation technologies. Based on this, a new apple leaf disease detection model that uses deep-CNNs is proposed by introducing the GoogLeNet Inception structure and Rainbow concatenation. Finally, under the hold-out testing dataset, using a dataset of 26,377 images of diseased apple leaves, the proposed INAR-SSD (SSD with Inception module and Rainbow concatenation) model is trained to detect these five common apple leaf diseases. The experimental results show that the INAR-SSD model realizes a detection performance of 78.80% mAP on ALDD, with a high-detection speed of 23.13 FPS. The results demonstrate that the novel INAR-SSD model provides a high-performance solution for the early diagnosis of apple leaf diseases that can perform real-time detection of these diseases with higher accuracy and faster detection speed than previous methods.

Subfields: Cell Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/6688c1771054113af6dbd18ea5f546788bc011dc

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Citations in the corpus

Tomato Diseases and Pests Detection Based on Improved Yolo V3 Convolutional Neural Network (2020, background): “Jiang et al. (2019) proposed an improved CNN-based deep learning method for real-time detection of apple leaf diseases and insect pests.”
Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies (2021, background): “[124] proposed an expert system to assist farmers in evaluating agricultural land for cultivation, based on the integration of data collected by the various sensors, with AI systems such as neural networks and multilayer perceptron (MLP), for the evaluation of the suitability of agricultural land.”
Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review (2020, neutral): “It should be noted that 19 studies (S1, S6, S7, S11, S12, S13, S22, S35, S43, S61, S68, S70, S75, S84, S86, S91, S100, S116, S120) propose new architectures and 15 (S9, S18, S19, S23, S32, S34, S39, S41, S42, S49, S50, S51, S53, S65, S103) investigations customize classic algorithms to solve the pro”
Plant Disease Detection and Classification by Deep Learning—A Review (2021, methodology): “Jiang et al. [76] proposed a novel method that is the SSD with inception module and rainbow concatenation (INAR-SSD).”