Abstract

Accurately and efficiently classifying apple leaf diseases is crucial for early detection, management, and enhanced agricultural productivity. With advancements in deep learning, particularly Convolutional Neural Networks (CNNs), the potential for automated plant disease detection has greatly increased. The research comprehensively reviews deep learning models applied to apple leaf disease classification, focusing on state-of-the-art architectures such as MobileNet, Xception, NASNetLarge, and DenseNet-201. The existing studies on the application of these models in plant disease detection, comparing their strengths, limitations, and suitability for agricultural applications have been critically analyzed. Performance metrics like accuracy, precision, recall, and F1-score are examined to identify the most effective models for detecting various apple leaf diseases under real-world conditions. The research highlights key challenges and opportunities in optimizing deep learning models for practical deployment, contributing to sustainable farming practices and agricultural disease management advancements.

Introduction

I. INTRODUCTION

In the rapidly advancing field of agricultural technology, image classification has emerged as a cornerstone in the detection and management of plant diseases. Accurate identification of diseases from plant leaf images is particularly critical for economically important crops like apples, which play a significant role in global fruit production. Apple orchards are vulnerable to various diseases that can adversely impact both yield and fruit quality. Early detection and intervention are essential to prevent significant economic losses and to maintain the sustainability of apple farming [14].

The classification of apple leaf diseases using deep learning models has gained considerable attention due to its potential for precision and scalability. Current research emphasizes the need for evaluating and comparing advanced image classification architectures to identify models that are both accurate and practical for deployment in agricultural settings [3]. Such efforts are instrumental in addressing challenges posed by environmental variability, limited data availability, and the demand for computational efficiency in field conditions. The study aims to identify the most effective methods for deploying image classification models in diverse agricultural settings, contributing to the development of more sustainable and efficient farming practices. The findings from the comparative study will not only improve the accuracy and reliability of disease detection technologies but also help enhance the resilience of apple production in the face of challenges such as climate change, evolving pest pressures, and the increasing global demand for higher crop yields [23].

The research explores the capabilities of four state-of-the-art Convolutional Neural Network (CNN) architectures for classifying apple leaf diseases: DenseNet201, MobileNet, NASNetLarge, and Xception. Each of these models offers unique advantages in terms of feature extraction, computational efficiency, and accuracy:

DenseNet201: Employs dense connectivity to improve gradient flow and reduce parameter usage, making it efficient for complex image-based tasks. MobileNet: Optimized for real-time applications with limited computational resources through depth-wise separable convolutions. NASNetLarge: Designed through Neural Architecture Search, achieving state-of-the-art performance with minimal manual tuning. Xception: Uses depth-wise separable convolutions for highly flexible and accurate feature extraction in large-scale image classification tasks.

The comparative strengths of these models and their performance metrics, including accuracy, precision, recall, and F1-score have been highlighted. By synthesizing insights from recent studies, the aim is to provide a comprehensive understanding of the current advancements in image classification for apple leaf diseases and to identify future research opportunities that can enhance agricultural practices.

II. LITERATURE REVIEW

A literature review critically evaluates existing research on a specific topic by analyzing scholarly articles, books, and other sources. It synthesizes the current state of knowledge, identifies research gaps, and offers insights for future studies. The process involves organizing sources by themes or methodologies, assessing their strengths and weaknesses, and creating a cohesive understanding of the subject. Various types of reviews, such as narrative, systematic, scoping, and meta-analyses, provide different research approaches. Literature reviews ensure academic rigor, foster innovation, and lay the groundwork for advancing knowledge by highlighting unexplored areas for further study.

A. Denoising And Image Segmentation Techniques

Mehdipour Ghazi (2017) introduced advanced denoising techniques for low-light and sensor images, improving the Non-Local Means (NLM) algorithm to preserve image details while reducing noise and also significantly discussed improvements in image quality, particularly for applications requiring high-entropy random numbers [1]. Weber et al. (2017) proposed a segmentation technique based on quasi-flat zones, utilizing morphological tools for more accurate segmentation in complex environments, such as plant leaf recognition, which also aids in extracting random noise for secure applications [2]. Malathi (2018) developed an automated system for plant leaf disease detection, combining image segmentation with k-means clustering and artificial neural networks (ANNs), which can be adapted to enhance the extraction of random noise in low-light CMOS images [3].

B. Deep Learning Applications In Agriculture

Sladojevic et al. (2016) applied CNNs to classify plant diseases, achieving accuracy between 91% and 98%, for extracting random noise in low-light CMOS sensor images for secure random number generation [4]. Mohanty et al. (2016) demonstrated the scalability of CNNs in large-scale plant disease classification, proving their robustness in diverse environmental conditions, which is crucial for automated disease detection in agriculture [5]. Sadiyah Abdullahi (2017) focused on optimizing CNNs for maize image classification, achieving 99.58% accuracy, showing the potential of CNNs for analyzing noisy images in low-light conditions for improved random number generation [6]. Krizhevsky et al. (2017) advanced deep learning with AlexNet, achieving groundbreaking performance on ImageNet, and contributing to CNN innovations applicable in agriculture and other domains [7]. Fuentes et al. (2017) combined CNNs with region-based methods for real-time disease detection, emphasizing early intervention in crop management [8]. Yalcin et al. (2017) proposed a CNN for plant species classification, outpacing other methods with 97.47% accuracy, showcasing CNNs' ability to handle noisy images for random number generation [9]. Paymode and Malode (2022) utilized transfer learning with CNNs for multi-crop disease classification, achieving high accuracy and supporting sustainable agriculture through early disease detection [10]. Massi et al. (2020) explored hybrid deep-learning models for plant disease recognition, combining CNNs and SVMs to improve disease classification accuracy [11]. Xie et al. (2020) optimized CNNs for real-time grape leaf disease detection, achieving 94.29% accuracy, and offering a practical tool for field use in agriculture [12]. Zhou et al. (2021) proposed a Deep Residual Dense Network (RDN) for tomato leaf disease identification, achieving 95% accuracy, ideal for complex agricultural environments [13]. El Massi et al. (2020) combined multiple classifiers for automatic plant disease detection, achieving a recognition rate of 91.11%, and enhancing precision in agriculture [14].

C. Applications of Machine Learning in Agricultural Disease Detection

Rumpf et al. (2017) studied the detection and differentiation of diseases in sugar beet plants using Support Vector Machines (SVM) and vegetation spectral indices and achieved 97% accuracy for healthy leaves and 86% for diseased leaves, demonstrating the potential of SVMs for disease detection and noise analysis in low-light CMOS sensor images [15]. Padol and Yadav (2016) applied SVM and image processing for grape leaf disease detection, achieving 88.89% accuracy by using K-means clustering for image segmentation and feature extraction. The approach enhances disease management and agricultural productivity [16]. Es-Saady et al. (2016) developed a machine vision system with a serial combination of SVM classifiers for plant disease recognition. High recognition rates, outpacing existing methods, and demonstrated the potential for automated disease diagnosis & efficient crop management have been achieved by the developed system[17]. Arivazhagan S et al. (2013) proposed an SVM-based algorithm for automatic plant disease classification using texture features from leaf images, achieving 94% accuracy. The methods applied in the research could improve noise analysis in low-light CMOS sensor images for generating high-quality random numbers [18]. Dutot et al. (2013) developed a Decision Support System (DSS) for integrated pest management, combining expert rules with machine learning algorithms to predict pest outbreaks and optimize pest control decisions, leading to more sustainable farming practices and higher crop yields [19].

Islam et al. (2017) developed an image-processing system for automatic rice leaf disease detection, achieving reliable classification for diseases like bacterial leaf blight, demonstrating the importance of Artificial intelligence (AI) in agricultural disease management [20]. Sannakki et al. (2013) used neural networks for grape leaf disease detection, employing image segmentation and feature extraction to classify diseases with high accuracy, showing AI's potential for cost-effective, timely disease detection in agriculture [21].

D. Hyperspectral and Remote Sensing Techniques

Li et al. (2017) addressed challenges in remote sensing image classification, such as scale variance and image resolution, by introducing a Dilated Convolutional Neural Network (DCNN). The innovative approach allowed the model to maintain spatial resolution while capturing contextual information, outperforming traditional CNNs in large-scale environmental monitoring, urban planning, and disaster management applications [22]. Sankaran et al. (2010) evaluated optical sensors, including hyperspectral sensors, for detecting plant stress and diseases. The hyperspectral sensors were particularly effective at identifying subtle physiological changes, with machine learning algorithms enhancing detection accuracy, and contributing to advanced diagnostic tools for agriculture [23]. Ullah et al. (2023) introduced DeepPlantNet, a deep learning-based framework for efficient plant leaf disease classification, focusing on lightweight architecture and high accuracy. The model, with 28 layers, was trained on the PlantVillage dataset and tested on eight plant species, including apple, grape, and tomato. It achieved 98.49% accuracy for an eight-class classification and 99.85% for a three-class classification. DeepPlantNet outperformed existing models, providing a reliable and computationally efficient diagnostic tool for early disease detection, minimizing agricultural losses, and reducing chemical use.[24]. Ali et al. (2024) developed AppleLeafNet, a lightweight framework for diagnosing apple leaf diseases. The model has a 37-layer architecture with a two-stage framework: (1) identifying leaf condition (healthy or diseased) and (2) subclassifying diseases like rust and scab. Using transfer learning and the Plant Pathology 2021 dataset, AppleLeafNet achieved 98.25% accuracy for condition identification and 98.60% for disease classification. Its computational efficiency, with fewer learnable parameters, makes it suitable for resource-constrained agricultural applications [25].

Table 2.1: A Comprehensive Review of Deep Learning and Machine Learning Techniques for Plant Disease Detection and Agricultural Applications

E. Summary

Deep learning has emerged as a powerful tool in agriculture, particularly for diagnosing plant diseases through automatic learning of data representations using multi-layered neural networks. The approach is highly effective for image recognition tasks, exemplified by the study on apple leaf diseases, where deep learning models, such as CNNs, were used to classify leaf conditions like apple scab and black rot with high accuracy. The research highlighted deep learning's potential to enhance disease detection and support precision agriculture, enabling timely interventions and advancing automated plant health monitoring systems.

III. RESEARCH GAPS

The rapid advancements in deep learning and image processing techniques have revolutionized agricultural practices, particularly in the area of plant disease detection. However, despite the growing body of research, several gaps remain that hinder the widespread implementation of deep learning models for disease classification in real-world agricultural settings.

A. Limitations in Model Comparisons

While much research focuses on deep learning models for plant disease detection, most studies examine a single or a few models without direct comparison. This limits the ability to determine the best model for tasks like apple leaf disease classification. Although CNNs have shown success, their performance varies based on dataset, environmental conditions, and disease characteristics. Therefore, a thorough comparison of multiple models is needed to identify the most accurate and practical solution for apple leaf disease detection.

B. Data Diversity and Availability

A critical research gap lies in the availability of diverse and representative datasets for training deep learning models in agricultural applications. Many existing datasets used for apple leaf disease detection lack variations in important factors such as lighting conditions, background noise, and leaf orientation. These limitations hinder the generalizability of deep learning models and their performance in real-world scenarios. Furthermore, the imbalance in datasets, with some diseases being underrepresented, poses challenges in building robust models capable of accurately classifying all types of apple leaf diseases.

C. Resource-Intensive Models

While high-performing deep learning models like NASNetLarge and Xception achieve state-of-the-art results in controlled environments, they require substantial computational resources, limiting their suitability for field deployment. In contrast, lightweight models such as MobileNet and EfficientNet show promise for resource-constrained environments. However, their application in real-world agricultural practices, where computational resources are often limited, remains underexplored.

D. Real-World Deployment and Practical Impact

The transition of deep learning models from research to practical agricultural applications poses significant challenges. Although these models achieve high accuracy in controlled settings, their effectiveness under diverse environmental conditions, such as varying weather and lighting, remains underexplored. Moreover, integrating disease detection systems with farming practices, including pest management and crop monitoring, requires further development for seamless adoption. Addressing these gaps will enhance the accuracy and scalability of deep learning models for apple leaf disease detection, enabling real-world agricultural deployment. A systematic evaluation of models, improved datasets, and resource-efficient techniques will advance state-of-the-art solutions and make these technologies more accessible to farmers worldwide.

IV. FINDINGS

The review of deep learning models for plant disease detection reveals key findings on the field's advancements, challenges, and the need for model comparisons. These insights emphasize deep learning's importance in agriculture and highlight challenges in real-world applications.

A. Growing Importance of Deep Learning in Agriculture

Deep learning, particularly Convolutional Neural Networks (CNNs), has emerged as a powerful tool for image-based disease detection in agriculture. Models based on CNNs have shown remarkable accuracy in identifying plant diseases, including apple leaf diseases, by analyzing images of leaves. These models leverage the hierarchical nature of CNNs to extract features from raw image data, enabling them to detect subtle disease symptoms that are often invisible to the human eye. The growing importance of deep learning in agriculture stems from its ability to provide automated, scalable, and efficient disease detection, potentially reducing the reliance on manual labor and improving the timeliness of disease management.

B. Lack of Comprehensive Comparative Analysis

Many studies highlight the effectiveness of individual deep-learning models for plant disease detection, but few offer comparative analyses of multiple models. Most focus on single-model applications, limiting insight into which models are best suited for tasks like detecting apple leaf diseases. The need for a thorough comparative evaluation of CNN architectures and their suitability for different real-world conditions is a key finding from the literature review. Such comparisons can help identify the strengths and weaknesses of each model and provide guidelines for selecting the best model for specific applications.

C.Challenges in Data Availability and Diversity

A major challenge identified from the literature is the lack of diverse datasets that accurately represent the complexities of real-world agricultural environments. Many existing datasets used for apple leaf disease detection are limited in terms of variations in lighting, background noise, leaf orientation, and disease types. The lack of diversity can cause deep learning models to perform poorly when exposed to new, unseen data or real-world conditions. The issue of dataset imbalance, with certain disease types being underrepresented, exacerbates the problem, making it difficult for models to accurately classify all disease types.

D. Need for Practical and Scalable Deployment

Although deep learning models show impressive performance in controlled environments, the practical deployment of these models in large-scale agricultural settings presents several challenges. Many state-of-the-art models require significant computational power, making them unsuitable for real-time deployment in resource-constrained environments. Models optimized for mobile and edge devices, such as MobileNet and EfficientNet, have shown promise in addressing the issue. However, their performance in the field has not been adequately evaluated.

E. Potential for Real-World Impact

The accurate and early detection of apple leaf diseases has significant potential to improve crop management and sustainability in agriculture. Early disease identification can help farmers implement timely interventions, reducing the use of pesticides and preventing large-scale crop losses. Moreover, reliable disease detection systems can contribute to improved crop yield and quality, thereby enhancing farmers' financial stability. The findings from the literature review highlight the importance of developing scalable and reliable disease detection systems that can be seamlessly integrated into farming practices, offering a practical solution to the challenges posed by plant diseases. In conclusion, the findings from the literature review underscore the growing importance of deep learning techniques in plant disease detection, the need for comprehensive model comparisons, and the challenges related to data diversity and real-world deployment.

Conclusion

The research paper aims to provide a detailed comparative analysis of deep learning models, particularly Convolutional Neural Networks (CNNs), for apple leaf disease classification. It identifies key research gaps, including limited dataset diversity, the impact of environmental factors on model performance, and the computational challenges of deploying advanced models. Despite the progress in deep learning for agriculture, there is a need for more comprehensive evaluations of various CNN architectures across diverse conditions. The research highlights the importance of dataset diversity and the insufficient comparative analysis between different models. It emphasizes the potential of deep learning for early apple leaf disease detection, which can improve crop management, and promote sustainable farming. The future may be focused on testing these models with more diverse datasets and in real-world agricultural settings to ensure their effectiveness and scalability. The study provides a foundation for future advancements in apple leaf disease classification using deep learning.

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Copyright

Copyright © 2025 Ajay Kumar, Naveen Kumar, Ritesh Rana, Sandeep Kumar, Ashok Kumar Kashyap, Gagandeep Sharma. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.