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

TLDR

Apple leaf diseases such as Alternaria, Brown spot, Mosaic, Grey spot, and Rust severely reduce yield, yet current studies lack accurate, rapid detection methods. This study aims to develop a real‑time apple leaf disease detector based on an improved convolutional neural network. The authors constructed the ALDD dataset with augmented laboratory and field images, then trained an INAR‑SSD model that integrates GoogLeNet Inception modules and Rainbow concatenation on 26,377 images. The INAR‑SSD achieves 78.80 % mAP at 23.13 FPS, outperforming prior approaches and enabling fast, accurate early diagnosis of the five apple leaf diseases.

Abstract

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.