Multi-modal plant disease detection using a single-stream CNN

A review on automated plant disease detection: motivation, limitations, challenges, and recent advancements for future research

Agricultural development is a critical strategy for promoting prosperity and addressing the challenge of feeding nearly 10 billion people by 2050. Plant diseases can significantly impact food production, reducing both quantity and diversity. Therefore, early detection of plant diseases through automatic detection methods based on deep learning can improve food production quality and reduce economic losses. While previous models have been implemented for a single type of plant to ensure high accuracy, they require high-quality images for proper classification and are not effective with low-resolution images. To address these limitations, this paper proposes the use of pre-trained model based on convolutional neural networks (CNN) for plant disease detection. The focus is on fine-tuning the hyperparameters of popular pre-trained model such as EfficientNetV2S, to achieve higher accuracy in detecting plant diseases in lower resolution images, crowded and misleading backgrounds, shadows on leaves, different textures, and changes in brightness. The study utilized the Plant Diseases Dataset, which includes infected and uninfected crop leaves comprising 38 classes. In pursuit of improving the adaptability and robustness of our neural networks, we intentionally exposed them to a deliberately noisy training dataset. This strategic move followed the modification of the Plant Diseases Dataset, tailored to better suit the demands of our training process. Our objective was to enhance the network's ability to generalize effectively and perform robustly in real-world scenarios. This approach represents a critical step in our study's overarching goal of advancing plant disease detection, especially in challenging conditions, and underscores the importance of dataset optimization in deep learning applications.

Abstract: Plant diseases are a major threat to crop yield and food security. Early detection and diagnosis are crucial to prevent the spread of disease and minimize crop losses. Plant diseases can have devastating effects on crop yields and food security. Disease detection is crucial for effective disease management. In recent years, deep learning techniques, such as Convolutional Neural Networks (CNN), have shown promising results in disease detection. In this study, we propose a plant disease detection system using CNN and Arduino. The system involves capturing images of plant leaves using a camera module connected to an Arduino board. In recent years, deep learning techniques such as Convolutional Neural Networks (CNNs) have shown great promise in image-based disease detection. Additionally, the use of low-cost microcontrollers like Arduino can provide a costeffective solution for real-time disease detection in the field. This paper proposes a plant disease detection system that combines CNN and Arduino for early and accurate disease detection. The system utilizes a CNN model to classify plant disease images and an Arduino board to analyze the results and provide real-time feedback to the user. The proposed system is tested on a publicly available dataset of plant disease images, achieving high accuracy, and demonstrating its potential for practical use. The results show that the proposed system can provide an affordable and efficient solution for early plant disease detection, facilitating prompt action and reducing crop losses.

Abstract. Plant diseases are a critical barrier to agricultural sustainability, contributing to annual crop losses of 30–40% in some regions. Such diseases arise from diverse pathogens, including fungi, bacteria, and viruses, and can severely degrade crop yield and quality if undetected. Early diagnosis is crucial for timely intervention, reduced pesticide use, and long-term soil health. Traditional approaches, which rely on manual visual inspection of leaves, remain slow, subjective, and impractical for monitoring large or remote farms. In recent years, the convergence of artificial intelligence (AI), computer vision, and image-based analysis has enabled automated disease detection systems that are both scalable and real-time. Classical techniques first employed color, texture, and shape descriptors combined with machine learning models such as Support Vector Machines (SVM), k-Nearest Neighbor (KNN), and Random Forests. These methods achieved moderate success but struggled with variability in lighting, background, and leaf orientation. The introduction of deep learning, particularly Convolutional Neural Networks (CNNs), transformed plant disease detection by allowing end-to-end feature learning directly from raw images. Modern lightweight architectures like MobileNet and EfficientNet further enhance deployability on mobile and edge devices. In parallel, segmentation-assisted and hybrid models improve robustness under complex field conditions. This review consolidates these developments, evaluating methods in terms of accuracy, generalization, computational efficiency, and field-readiness. It also identifies persistent challenges—such as limited annotated datasets and the opaque decision-making of deep models—and highlights future directions in explainable AI, multi-modal sensing, and IoT-integrated precision agriculture.

<p class="Normal1">Medicinal plants have been found and utilized in traditional medical practices from ancient times. Many medicinal plants play a vital role in curating many life threatening diseases. Very few of the medicinal herbs are commercially cultivated. Many plant diseases are there which destroys these medicinal plants. Early detection of plant diseases can prevent the huge loss of these medicinal plants. Here, we presented a hybrid model that makes use of SVM along with the traditional convolutional neural network (CNN) for predicting <em>Basil</em> plants leaves diseases. We transformed the conventional CNN model by adding a classification layer Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) after feature extraction and this approach tends to perform better than traditional CNN as we make the dataset balanced by data augmentation and SVN and KNN tend to perform better in case of balanced samples. CNN is used for training, SVM/KNN is used for classification. The advantages of CNN and SVM are used in proposed the CNN and SVM and KNN model. It is assumed that such a combined model would incorporate the benefits of CNN and SVM. Here, we identified the four types of diseases that affect <em>basil</em> plant leaves as Leaf spot, Downy mildew, Fusarium wilt, Fungal, and Healthy. Since there isn’t a standard dataset for <em>basil</em> leaves, we created our own 803 picture data set and used various machine learning techniques to train and evaluate the model. However, over other existing algorithms, our hybrid model i.e., CNN+SVM has produced more accurate results. For five classes of <em>basil</em> plant leaves, the proposed model produced 95.02% accuracy of for leaf diseases.</p>

The agricultural sector contributes significantly to the gross domestic product of emerging nations. One of the primary factors that reduce agricultural productivity is plant diseases. Detecting plant diseases is a crucial step requiring attention for a productive economy and agriculture. Traditional techniques of crop disease detection are laborious and time-consuming, requiring a great deal of skill and effort. Academicians, researchers and practitioners have recently shown a great interest in the new area of automatic plant disease identification. As soon as they arise on plant leaves, early detection of plant diseases can be aided by machine learning (ML) and deep learning (DL). A comprehensive analysis has been conducted in this review paper to assess the viability to identify plant diseases applying machine learning models. This paper also considers the illnesses and viruses that affect different crops: black gram, chickpea, mungbean and potato. A detailed procedure has been done to recognise and classify plant diseases. The analysis of various ML and DL algorithms adopted by researchers to classify diseases has been explained. Finally, challenges are highlighted to recognise and classify plant diseases utilising ML and DL algorithms, particularly in legume crops. Developing and integrating real-time datasets on plant diseases using advanced models is the future for monitoring, detecting and managing plant ailments.

Artificial intelligence (AI) is transforming ophthalmology by leveraging machine learning (ML) and deep learning (DL) techniques, particularly artificial neural networks (ANN) and convolutional neural networks (CNN) to mimic human brain functions and enhance accuracy through data exposure. These AI systems are particularly effective in analyzing ophthalmic images for early disease detection, improving diagnostic precision, streamlining clinical workflows, and ultimately enhancing patient outcomes. This study aims to explore the specific applications and impact of AI in the fields of glaucoma, corneal diseases, and oculoplastics. This study reviews current AI technologies in ophthalmology, examining the implementation of ML and DL techniques. It evaluates AI's role in early disease detection, diagnostic accuracy, clinical workflow enhancement, and patient outcomes. AI has significantly advanced the early detection and management of various ocular conditions. In glaucoma, AI systems provide standardized, rapid identification of disease characteristics, reducing intra- and interobserver bias and workload. For corneal diseases, AI tools enhance diagnostic methods for conditions such as keratitis and keratoconus, improving early detection and treatment planning. In oculoplastics, AI assists in the diagnosis and monitoring of eyelid and orbital diseases, facilitating precise surgical planning and postoperative management. The integration of AI in ophthalmology has revolutionized eye care by enhancing diagnostic precision, streamlining clinical workflows, and improving patient outcomes. As AI technologies continue to evolve, their applications in ophthalmology are expected to expand, offering innovative solutions for the diagnosis, monitoring, treatment, and surgical outcomes of various eye conditions.

Currently, artificial intelligence (AI) technology offers opportunities to address serious challenges in waste management in the fashion industry. This study systematically reviewed and analyzed literature on AI applications in textile waste management from 2014 to 2024. We aimed to review the intervention effects of AI technologies on textile waste across stages of the fashion industry lifecycle and explore AI’s role in promoting circular supply chains (CSCs). The results show that AI plays a prominent role in four key areas: Enhanced intelligent analysis of market dynamics, optimizing consumer experience-oriented durability design, collaboratively optimizing production processes and inventory management, and advancement of intelligent waste management. Convolutional neural networks are the most widely used AI technology due to their strength in image recognition. However, AI technologies also face limitations such as high data requirements. Future research should attempt to promote improvements in policies, enterprise, and technologies to unleash greater potential for AI in CSCs.

The application of artificial intelligence (AI) in medicine has garnered significant attention in recent years, offering new possibilities for improving patient care across various domains. For herpes zoster, a viral infection caused by the reactivation of the varicella-zoster virus, AI technologies have shown remarkable potential in enhancing disease diagnosis, treatment, and management. This study aims to investigate the current research status in the use of AI for herpes zoster, offering a comprehensive synthesis of existing advancements. A systematic literature review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Three databases of Web of Science Core Collection, PubMed, and IEEE were searched to identify relevant studies on AI applications in herpes zoster research on November 17, 2023. Inclusion criteria were as follows: (1) research articles, (2) published in English, (3) involving actual AI applications, and (4) focusing on herpes zoster. Exclusion criteria comprised nonresearch articles, non-English papers, and studies only mentioning AI without application. Two independent clinicians screened the studies, with a third senior clinician resolving disagreements. In total, 26 articles were included. Data were extracted on AI task types; algorithms; data sources; data types; and clinical applications in diagnosis, treatment, and management. Trend analysis revealed an increasing annual interest in AI applications for herpes zoster. Hospital-derived data were the primary source (15/26, 57.7%), followed by public databases (6/26, 23.1%) and internet data (5/26, 19.2%). Medical images (9/26, 34.6%) and electronic medical records (7/26, 26.9%) were the most commonly used data types. Classification tasks (85.2%) dominated AI applications, with neural networks, particularly multilayer perceptron and convolutional neural networks being the most frequently used algorithms. AI applications were analyzed across three domains: (1) diagnosis, where mobile deep neural networks, convolutional neural network ensemble models, and mixed-scale attention-based models have improved diagnostic accuracy and efficiency; (2) treatment, where machine learning models, such as deep autoencoders combined with functional magnetic resonance imaging, electroencephalography, and clinical data, have enhanced treatment outcome predictions; and (3) management, where AI has facilitated case identification, epidemiological research, health care burden assessment, and risk factor exploration for postherpetic neuralgia and other complications. Overall, this study provides a comprehensive overview of AI applications in herpes zoster from clinical, data, and algorithmic perspectives, offering valuable insights for future research in this rapidly evolving field. AI has significantly advanced herpes zoster research by enhancing diagnostic accuracy, predicting treatment outcomes, and optimizing disease management. However, several limitations exist, including potential omissions from excluding databases like Embase and Scopus, language bias due to the inclusion of only English publications, and the risk of subjective bias in study selection. Broader studies and continuous updates are needed to fully capture the scope of AI applications in herpes zoster in the future.

In agriculture, early detection of plant diseases is essential to avoid irretrievable damage to crops. However, accurate plant disease diagnosis is usually performed by professionals with high precision instruments which is both expensive and time consuming. To tackle this problem, an image recognition method based on multiple classifier integration is described. The method is composed of three parts. Firstly, a public dataset of diseased and healthy plant leaves is adopted. Secondly, three types of convolutional neural network (CNN) models are fine tuned to classify different diseases of plants and evaluated separately. Finally, the three models are integrated and evaluated for accurately diagnosing plant diseases. Experiment result shows the correctness and efficiency of the approach with a best accuracy of 99.92% on split test set.

Chapter 2 - Current status of plant diseases and food security

Plant diseases are one of the major causes of crop yield reduction worldwide. Early detection and effective management of plant diseases will ensure a sustainable food supply and reduce famine. In this project, we propose a Plant Disease Management System (PDMS) that uses Artificial Intelligence (AI) and Internet of Things (IoT) technologies to diagnose and manage plant diseases. The proposed system consists of two main components: a plant disease diagnosis module and a disease management module. The plant disease diagnosis module uses AI algorithms to analyze images of plant leaves and identify the type of disease affecting the plant. The AI model is trained on a large dataset of plant images using deep learning techniques, such as Convolution Neural Networks (CNNs) and Transfer Learning.

Modern reviews of challenges related to deep learning application in agriculture mention restricted access to open datasets with high-resolution natural images taken in field conditions. Therefore, artificial intelligence solutions trained on these datasets containing low-resolution images and disease symptoms in the advanced stage are not suitable for early detection of plant diseases. The study aims to train a convolutional neural network for apple scab detection in an early stage of disease development. In this study a dataset was collected and used to develop a convolutional neural network based on the sliding-window method. The convolutional neural network was trained using the transfer-learning approach and MobileNetV2 architecture tuned on for embedded devices. The quality analysis in laboratory conditions showed the following accuracy results: F 1 score 0.96 and Cohen’s kappa 0.94; and the occlusion maps — correct classification features.

BackgroundDespite the extensive hopes and expectations for value creation resulting from the implementation of artificial intelligence (AI) applications in healthcare, research has predominantly been technology-centric rather than focused on the many changes that are required in clinical practice for the technology to be successfully implemented. The importance of leaders in the successful implementation of innovations in healthcare is well recognised, yet their perspectives on the specific innovation characteristics of AI are still unknown. The aim of this study was therefore to explore the perceptions of leaders in healthcare concerning the innovation characteristics of AI intended to be implemented into their organisation.MethodsThe study had a deductive qualitative design, using constructs from the innovation domain in the Consolidated Framework for Implementation Research (CFIR). Interviews were conducted with 26 leaders in healthcare.ResultsParticipants perceived that AI could provide relative advantages when it came to care management, supporting clinical decisions, and the early detection of disease and risk of disease. The development of AI in the organisation itself was perceived as the main current innovation source. The evidence base behind AI technology was questioned, in relation to its transparency, potential quality improvement, and safety risks. Although the participants acknowledged AI to be superior to human action in terms of effectiveness and precision in some situations, they also expressed uncertainty about the adaptability and trialability of AI. Complexities such as the characteristics of the technology, the lack of conceptual consensus about AI, and the need for a variety of implementation strategies to accomplish transformative change in practice were identified, as were uncertainties about the costs involved in AI implementation.ConclusionHealthcare leaders not only saw potential in the technology and its use in practice, but also felt that AI’s opacity limits its evidence strength and that complexities in relation to AI itself and its implementation influence its current use in healthcare practice. More research is needed based on actual experiences using AI applications in real-world situations and their impact on clinical practice. New theories, models, and frameworks may need to be developed to meet challenges related to the implementation of AI in healthcare.

In the comparison paper we have compared the results of various papers implemented using machine learning algorithms. As compared to previous researcher’s results, this result has more accuracy and results in terms of percentage. Nowadays, Plant disease detection is important for ensuring agricultural productivity and quality in both traditional manual methods and machine learning approaches are used for this motive, however they fluctuate substantially in terms of process, accuracy, performance, and scalability. The detection of diseases in pomegranate plant leaves is a critical step in ensuring crop health and yield. Traditionally, manual checking methods have been employed for disease identification, relying heavily on the expertise of agricultural professionals and visual inspection. While these methods are low-cost and accessible, they are time-consuming, subjective, and prone to human error, particularly in large-scale farming operations. In contrast, machine learning-based methods offer a modern and efficient alternative. Leveraging advanced algorithms, these techniques can analyze large datasets of leaf images to detect and classify diseases with high accuracy. Popular approaches include convolutional neural networks (CNNs), transfer learning models, and hybrid systems that integrate feature extraction with classification. These methods not only enhance the precision and speed of disease detection but also reduce dependency on expert intervention. This comparative study explores the strengths and limitations of both traditional manual checking and machine learning-based approaches for pomegranate leaf disease detection. It examines critical factors such as accuracy, scalability, cost, and ease of implementation. The study highlights how machine learning has the ability to revolutionize agricultural practices by providing dependable, automated, and scalable solutions. But, challenges include the want for document training datasets, computational assets, and domain-precise information in machine studying stay.