Progressive Web Application for Plant Disease Detection using CNN

Convolutional neural networks have demonstrated state-of-the-art performance in image classification and various other computer vision tasks. Plant disease detection is an important area of deep learning which has been addressed by many recent methods. However, there is a dire need to optimize these solutions for resource-constrained portable devices such as smartphones. This is a challenging problem because deep learning models are resource extensive in nature. This paper proposes an efficient method to systematically classify plant disease symptoms using convolutional neural networks. These networks are memory efficient and when coupled with the proposed training configuration it enables rapid development of industrial applications by reducing the training times. Another critical problem arises with the improper distribution of samples among classes known as the class imbalance problem, which is addressed by employing a simple statistical methodology. Transfer learning is a known technique for training small datasets which transfers pre-trained weights learned on a large dataset. However, during transfer learning, negative transfer learning is a common problem. Therefore, a stepwise transfer learning approach is proposed which can help in fast convergence, while reducing overfitting and preventing negative transfer learning during knowledge transfer across domains. The system is trained and evaluated on two plant disease datasets i.e., PlantVillage (a publicly available dataset) and pepper disease dataset provided by the National Institute of Horticultural and Herbal Science, Republic of Korea. The pepper dataset is particularly challenging as it contains images from different parts of the plant such as the leaf, pulp, and stem. The proposed system has dominated the previous works on the PlantVillage dataset and achieved 99% and 99.69% accuracy on the Pepper dataset and PlantVillage datasets, respectively.

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.

Handwritten digit recognition, a task in computer vision, is critical for applications such as postal automation, banking, and digitization of forms. Traditional approaches have leveraged statistical models, but the rise of deep learning, particularly Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM), has revolutionized the field. However, a comprehensive performance comparison of CNN and LSTM architectures in the context of handwritten digit classification remains underexplored. This study aimed to address this gap by evaluating and comparing CNN and LSTM models for the classification of handwritten digits. Two machine learning models – Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) were trained with the preprocessed handwritten digit dataset. The CNN model was designed with multiple convolutional and pooling layers, along with dropout for regularization. The LSTM model was designed with LSTM layers to capture sequential patterns in the data, followed by a dense layer for classification. The models were implemented in python, evaluated and compared based on accuracy, precision, recall and F1-score. The evaluation and comparison results indicate that CNN achieved 99.31% accuracy, 99.0% precision, 99.0% recall, and a 99.0% F1-score, while LSTM achieved 98.90% accuracy, 99.0% precision, 99.0% recall, and a 99.0% F1-score. The results demonstrated that CNN outperformed LSTM in terms of accuracy and misclassification errors, making it the optimal choice for image-based handwritten digit recognition. This finding underscores the efficiency of CNN in addressing challenges related to digit recognition, contributing to the advancement of automated digit classification systems and improving the accuracy of image-based classification tasks.

Crops play a vital role in the global food supply. Various environmental factors can cause crop diseases and insect pests, resulting in severe production losses. However, manual detection of crop diseases and insect pests is a time-consuming and error-prone process, which often fails to identify and prevent the spread of plant diseases in a timely and effective manner. To overcome these challenges, machine learning (ML) and deep learning (DL) technologies have been introduced in recent years to improve early identification and effective response to plant pests and diseases. In this article, the principles of convolutional neural network (CNN), recurrent neural network (RNN) and adversarial generative neural network (GAN) are explained, as well as the latest progress in the improvement and application of the three algorithms in plant disease detection and detection. By applying DL technology, efficient and accurate detection of pests and diseases can be achieved, thereby providing reliable support for agricultural production. It is hoped that this work will become a valuable resource for researchers studying plant pest and disease detection and provide guidance to further advance research and practice in this field. At the same time, some current challenges and issues that need to be solved are also discussed, such as data quality, target occlusion, model generalization ability, and adaptability in practical applications. Deep learning holds great promise in revolutionizing precision agriculture by providing efficient and accurate solutions for pest and disease management.

The care and health of agricultural plants, which are the primary source for people to eat healthily, are essential. Disease detection in plants is one of the critical elements of smart agriculture. In parallel with the development of artificial intelligence, advancements in smart agriculture are also progressing. The development of deep learning techniques positively affects smart farming practices. Today, using deep learning and computer vision techniques, various plant diseases can be detected from images such as photographs. In this research, deep learning techniques were used to detect and diagnose bean leaf diseases. Healthy and diseased bean leaf images were used to train the convolutional neural network (CNN) model, which is one of the deep learning techniques. Transfer learning was applied to CNN models to detect plant diseases with the difference of related works. A transfer learning-based strategy to identify various diseases in plant varieties is demonstrated using leaf images of healthy and diseased plants from the Bean dataset. With the proposed method, 1295 images were studied. The results show that our technique successfully identified disease status in bean leaf images, achieving an accuracy of 98.33% with the ResNet50 model.

A country’s financial growth is prejudiced by its rate of agricultural output. Nevertheless, Plant Diseases (PD) pose a substantial obstacle to the cultivation and value of foodstuff. The timely detection of PDs is paramount for public wellness and Sustainable Agriculture (SA) promotion. The conventional diagnostic procedure entails a pathologist’s visual evaluation of a particular plant through in-person visits. Nevertheless, the manual inspection of crop diseases is limited due to its low level of accuracy and the limited availability of skilled workers. To address these concerns, there is a need to develop automated methodologies capable of effectively identifying and classifying a wide range of PDs. The precise detection and categorization of PDs pose a challenging task due to various factors. These include the presence of low-intensity data in both the image’s backdrop and the forefront, the significant similarity in color between normal and diseased plant regions, the presence of noise in the specimens, and the variations in the location, chrominance, framework, and dimensions of plant leaves. This paper presents a novel approach for identifying and categorizing PDs using a Deep Convolutional Neural Network - Transfer Learning (DCNN-TL) technique in the Agricultural Operation System (AOS). The proposed method aims to enhance the capabilities of SA in accurately identifying and categorizing PDs. The improved Deep Learning (DL) methodology incorporates a TL technique based on fine-tuned Visual Geometry Group 19 (VGG19) architecture. The revised system accurately detects and diagnoses five distinct PD categories. Among the evaluated methods, the proposed DCNN-TL in this study shows outstanding precision, recall, and accuracy values of 0.996, 0.9994, and 0.9998, respectively.

- The Plant Disease Detection and Classification project is designed to help farmers identify crop diseases at an early stage using images of plant leaves. By applying advanced machine learning (ML) and deep learning (DL) techniques, this system aims to automatically detect and classify various plant diseases with high accuracy. Early identification of plant diseases plays a vital role in improving crop yield, reducing unnecessary pesticide usage, and encouraging sustainable farming practices. Once a disease is detected, the system not only identifies it but also provides a brief description and recommends appropriate prevention and treatment methods. To enhance performance, the project uses data augmentation and normalization techniques—helping the model handle variations in lighting, angles, and environmental conditions. The system is trained on a diverse dataset containing images of both healthy and diseased leaves from different plant species, categorized into 38 different classes. The project incorporates various machine learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Decision Trees, and Random Forests, as well as a Convolutional Neural Network (CNN) for deep learning. Among these, CNNs have shown outstanding performance due to their ability to learn spatial features from images effectively. While existing research has shown promising results, real-world challenges such as varying image quality, lighting conditions, and background clutter still affect accuracy. This project aims to overcome these issues by training on large, diverse datasets and fine-tuning model parameters. Ultimately, this project hopes to empower farmers with an intelligent tool to monitor crop health, make informed decisions, and contribute to food security through precision agriculture. Keywords: Plant Disease Detection, Automated Disease Detection System, Machine Learning, Deep Learning, Data Augmentation, Data Normalization, Support Vector Machine, KNN Classifier, Convolutional Neural Network, Decision Trees, Random Forest

The performance of image classification technology based on deep network has been greatly improved, making computer vision enter the stage of industrialization and be gradually applied to many aspects of human work and life. As a typical classification task in computer vision, human behavior recognition has immeasurable potential value in medical, family, transportation, and other scenarios. At the same time, in the field of competitive sports, the integration of artificial intelligence technology and sports technical and tactical analysis is undoubtedly an important way to innovate and improve the technical and tactical level. Taking karate as an example, the study of athletes’ training and competition videos is an important means and method for technical and tactical analysis in competitive sports. Traditional tactical intelligence analysis methods have many shortcomings, such as high labor cost, serious data loss, long delay, and low accuracy. Therefore, based on the convolutional neural network, this paper establishes a new graph convolution model for automatic intelligent analysis of karate athletes’ technical action recognition, action frequency statistics, and trajectory tracking. The technology effectively makes up for the disadvantages of traditional tactical intelligence analysis methods. The research results show that the new topology map construction method has a significant effect on improving the accuracy of behavior recognition and also lays a foundation for technical and tactical analysis.

Image classification is the elementary and widely addressed task in computer vision. Classification of monument architecture is a real challenge due to its structural complexity. Analyzing architecture using the machine learning or data mining techniques can be useful. Various feature descriptors are used for large scale image classification. In this context, optimal feature descriptors as filters are used in Convolutional Neural Network. When analyzing architecture using the machine learning or data mining techniques, it can be useful to infer whether it is Cathedral or Indian Mughal Monuments in architecture involved. This paper focuses on architecture recognition, which has been studied extensively in the history literature, using Convolutional Neural Network (CNN) based on highly effective Tensorflow, an open source software library. The important task is to generalize the model to perform well on the new database. Ontology performs a significance role in learning to categorize given picture into metaphysical classes. A deep learning based approach is used to train the convolutional neural network. In the proposed work, images are classified into Cathedrals and Indian Mughal Monuments. Experimental results demonstrate that the method can successfully classify if the given image is Cathedral or Indian Mughal Monument which mainly includes the Taj Mahal and Char Minar. Proposed method substantially outperform state-of art approaches which is demonstrated by satisfactory result obtained on the dataset.

Rice is one of the most cultivated cereal in Asian countries and Vietnam in particular. Good seed germination is important for rice seed quality, that impacts the rice production and crop yield. Currently, seed germination evaluation is carried out manually by experienced persons. This is a tedious and time-consuming task. In this paper, we present a system for automatic evaluation of rice seed germination rate based on advanced techniques in computer vision and machine learning. We propose to use U-Net - a convolutional neural network - for segmentation and separation of rice seeds. Further processing such as computing distance transform and thresholding will be applied on the segmented regions for rice seed detection. Finally, ResNet is utilized to classify segmented rice seed regions into two classes: germinated and non- germinated seeds. Our contributions in this paper are three-fold. Firstly, we propose a framework which confirms that convolutional neural networks are better than traditional methods for both segmentation and classification tasks (with F1- scores of 93.38\% and 95.66\% respectively). Secondly, we deploy successfully the automatic tool in a real application for estimating rice germination rate. Finally, we introduce a new dataset of 1276 images of rice seeds from 7 to 8 seed varieties germinated during 6 to 10 days. This dataset is publicly available for research purpose.

Owing to the wide spread of the 3D objects technologies, learning 3D objects labeling and segmentation is becoming one of the most provocative tasks in computer vision and digital multimedia. Recently, convolutional neural network (CNN) has proved itself as a powerful model in segmentation and classification by giving excellent performances. The use of a graphical model such as conditional random field (CRF) contributes further in capturing contextual information and thus improving the segmentation performance. In this paper, we implement a deep architecture in order to segment and label 3D shape parts by considering both spectral and geometric features via a combined framework consisting of a CNN and CRF models. First, low-level features are used to learn deep features using a CNN model, and then formulate the deep CRF model with a CNN-based unary and pairwise potential functions to effectively extract the semantic correlations between adjacent triangles on the mesh. By comparing our results with those from several state-of-the-art, our method shows promising potentials.

Object classification has been one of the main tasks in computer vision. With the fast development of deep learning, its performance in image classification and object recognition has presented dramatic improvements. In this paper, we propose a new deep convolutional neural network (CNN) architecture for robust object classification. The proposed model is fused with three traditional CNN approaches, Densenet201, Resnet50, and our proposed residual CNN. The fused network architecture allows parallel processing of the multiple networks for keeping the system sped up. A single shot deep convolution network is trained as an object detector to generate all possible candidates of different object classes. The output of each neural network is representing a single vote that is used in the classification process. 3-to-1 voting criteria are applied in the final classification decision between the candidate object classes. Several experiments were conducted to evaluate the performance of the proposed network. The experimental results show that the proposed approach has better performance than the networks used in the fusion process when they act individually. It also has lower miss rates when compared to several states of art methodologies.

Background of study: Food image classification is a challenging task in computer vision, particularly when dealing with traditional food items that exhibit subtle visual variations. While Convolutional Neural Networks (CNNs) have long been the standard for image recognition, their limitation in capturing long-range dependencies has led to the emergence of Vision Transformers (ViTs). In this context, the classification of Indonesian traditional cakes offers a culturally rich yet complex problem for automated image recognition systems.Aims and scope of paper: This study aims to conduct a comparative analysis between EfficientNet-B0 (CNN-based) and ViT-B/16 (Transformer-based) architectures in classifying eight categories of Indonesian traditional cakes. The research evaluates not only classification accuracy but also the strengths and limitations of each model in handling fine-grained visual distinctions.Methods: Both models were fine-tuned using the “Kue Indonesia” dataset from Kaggle. The methodology includes image preprocessing, model training with consistent parameters, and evaluation using accuracy, precision, recall, and F1-score. A confusion matrix was also used to visualize misclassifications and analyze per-class performance.Result: ViT-B/16 achieved slightly higher accuracy (96.25%) compared to EfficientNet-B0 (95.62%). ViT performed better in classes with subtle variations, such as kue lapis and kue dadar gulung, while EfficientNet-B0 showed superior efficiency and high accuracy on visually distinct cakes.Conclusion: Both CNN and ViT models demonstrate strong performance in traditional food classification. ViT is more robust in fine-grained visual analysis, whereas EfficientNet-B0 is preferable for resource-constrained environments. This study highlights the role of AI in supporting digital preservation of culinary heritage.

– Domain: Artificial Intelligence Subdomain: Computer Vision Problem Statement: Plant diseases significantly reduce crop yield and quality. Traditional disease detection methods rely on manual observation, which is often slow, inaccurate, and not easily accessible to all farmers. There is a need for an automated system that can quickly and accurately detect plant diseases using technology. Solution: The Plant AI-Based Disease Detection System is a desktop web application that uses deep learning to identify plant leaf diseases. Users upload a leaf image, which is analyzed by a trained CNN model to detect the disease. The system then displays the disease name along with precautionary measures, helping users take timely action and improve crop productivity. Key Words: Plant Disease Detection, Artificial Intelligence, Computer Vision, Deep Learning, Convolutional Neural Network (CNN), Image Classification, Agriculture Technology, Machine Learning

Background: Plant diseases affect agricultural output, quality and profitability, making them serious obstacles for agriculture. It is essential to detect diseases early in order to reduce losses while retaining sustainable practices. Plant disease detection has benefited greatly from the use of computer vision and deep learning in recent years because of their outstanding precision and computing capability. Objective: In this paper, we intend to investigate the role of deep learning in computer vision for plant disease detection while looking into how these techniques address complex disease identification problems. A variety of deep learning architectures were reviewed, and the contribution of frameworks such as Tensorflow, Keras, Caffe and PyTorch to the researchers' model construction was studied as well. Additionally, the usage of open repositories such as PlantVillage and Kaggle along with the customized datasets were discussed. Methods: We gathered the most recent developments in deep learning techniques for leaf disease detection through a systematic literature review of research papers published over the past decade, using reputable academic databases like Scopus and Web of Science, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method for guidance. Results: This study finds that researchers consistently enhance existing deep learning architectures to improve prediction accuracy in plant disease detection, often by introducing novel architectures and employing transfer learning methods. Frameworks like TensorFlow, Keras, Caffe, and PyTorch are widely favored for their efficiency in development. Additionally, most studies opt for public datasets such as PlantVillage, Kaggle, and ImageNet, which offer an abundance of labelled data for training and testing deep learning models. Conclusion: While no singular ‘best' model emerges, the adaptability of deep learning and computer vision demonstrates the dynamic nature of plant disease recognition area, and this paper provides a comprehensive overview of deep learning's transformative impact on plant disease recognition by bringing together information from different studies. Keywords: Deep learning, Computer vision, Plant disease, Systematic literature review