Plant Disease Classification

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.

Tomato leaf diseases significantly affect crop productivity, and manual inspection often leads to misclassification due to the visual similarity of symptoms. Recent studies have shown that Convolutional Neural Networks (CNN) provide high accuracy in leaf–based plant disease classification across various plant species, highlighting their potential for early disease detection. This study aims to develop an accurate tomato leaf disease classification system using a CNN model trained on the Kaggle tomato leaf dataset consisting of four classes: Leaf Blight, Bacterial Spot, Leaf Scab, and Healthy. The methodology includes literature review, dataset acquisition, preprocessing, augmentation, CNN architecture design, model training, and performance evaluation. Preprocessing techniques such as resizing and normalization were applied, followed by augmentation using random flipping and rotation to increase dataset variability. The proposed model was trained for 40 epochs with a batch size of 16. Results show consistent accuracy improvement, reaching 0.98 training accuracy with a loss of 0.07, while validation accuracy peaked at 0.94. Testing on both single and multiple images demonstrates strong prediction confidence, with minor misclassifications in visually similar cases. Overall, the system effectively identifies tomato leaf diseases and reinforces the suitability of CNN for supporting early plant disease detection in smart agriculture applications.

This composition bargains with the Tomato leaf infection discovery and classification utilizing different strategies like Convolutional Neural Network (CNN), Regions with CNN (R-CNN), Fast R-CNN and Faster R-CNN. The main issue in the agricultural sector is leaf diseases, which have an impact on crop yield and financial gain. Early detection of leaf diseases in plants is essential to prevent losses to the agricultural sector. There are several tomato leaf diseases that affect the crop's leaves, including the Mosaic virus, Early Blight, Healthy, Septoria leaf, and Bacterial spot. Using deep learning algorithms and image processing methods, we can identify the diseases in tomato leaves using developing deep learning approaches. The implementation procedures in our proposed work involves data collection, pre-processing, training, feature extraction, testing, and classification utilising the Visual Geometry Group (VGG 16) to identify damaged or healthy leaves. VGG 16 is incorporated to categorise the leaves as healthy or diseased based on the data and Regression's boundary box method is adopted. Therefore, using Faster RCNN, a model is created to identify and categorise diseases from every image of a tomato leaf that is used as an input, providing a forecast with a considerably greater degree of accuracy. We obtain an accuracy of approximately 98% after fitting the collected features into the neural network over 20 iterations.

Aquaculture plays a crucial role in global food security; however, disease outbreaks remain a major constraint to sustainable production. Rapid and reliable detection of fish diseases is essential to reduce mortality, economic losses, and the misuse of antimicrobials in aquaculture systems. Conventional diagnostic approaches, such as clinical observation and bacterial culturing, are time-consuming, costly, and require specialized expertise. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have shown promise in automating image-based disease detection. This study aimed to compare a lightweight three-layer CNN model with pre-trained deep learning architectures (VGG16, InceptionV3, and ResNet50) for multi-class classification of infected freshwater fish species using a balanced image dataset collected from aquaculture farms in Thailand. Images from clinically infected freshwater fish were collected during routine farm inspections across six provinces in Thailand. The dataset included 424 images of four species: Asian seabass (Lates calcarifer), red tilapia (Oreochromis sp.), snakeskin gourami (Trichopodus pectoralis), and snakeheads (Channa striata). After preprocessing, a balanced dataset of 56 images per class (totaling 224) was created. The dataset was divided into training (80%) and testing (20%) subsets. On-the-fly data augmentation techniques, such as rotation, brightness adjustment, flipping, shifting, shearing, and zooming, were applied to the training data to reduce overfitting. A lightweight, three-layer CNN model with stochastic gradient descent was used and compared with pre-trained architectures (VGG16, InceptionV3, and ResNet50). Model performance was assessed through accuracy, precision, recall, F1-score, confusion matrix analysis, and five-fold cross-validation. Among the evaluated models, InceptionV3 achieved the highest classification accuracy (56.82%), followed by VGG16 (43.18%) and the proposed CNN (38.64%), while ResNet50 performed poorly (25%). The InceptionV3 model also demonstrated higher average precision (63%), recall (57%), and F1-score (56.75%), indicating superior classification capabilities. Confusion matrix analysis revealed that InceptionV3 correctly classified 25 out of 44 test images, outperforming the proposed CNN (17 correct predictions) and VGG16 (19 correct predictions). Five-fold cross-validation further confirmed the stability and relatively better performance of the InceptionV3 model. The comparative evaluation shows that pre-trained CNN architectures, especially InceptionV3, outperform a lightweight three-layer CNN when trained on small, balanced datasets of infected fish images. Although the proposed lightweight CNN has limited accuracy, its low computational needs suggest it could be useful in resource-limited aquaculture settings. Incorporating deep learning-based image analysis into aquaculture health monitoring systems could enable quick disease triage, support timely management decisions, and promote better biosecurity and sustainable fish production. Future research should increase the dataset size, include more fish species and disease types, and test model performance across different farming environments to improve its generalizability and practical use.

Application of Artificial intelligence (AI) in agriculture sector plays an important role to enhance the yields by predicting and detecting the plant diseases and improving health of the crop. The early detection of plant diseases is crucial and helpful to the farmers to minimize the crop loss and ensure healthy crop. However, the traditional manual methods, are time consuming, error-prone, and inefficient for large-scale farming. Adopting recent technologies in Artificial Intelligence (AI) and Deep Learning, particularly Convolutional Neural Networks (CNNs) in the recent years have revolutionized the automation process of plant disease detection. However, singlemodal approaches depend on only RGB images often fail to capture critical physiological and biochemical changes in plants. To overcome these limitations, we propose a Single-Stream CNN in Multi-Modal Plant Disease Detection, integrating RGB, thermal, and hyperspectral imaging into a unified model. Unlike traditional multistream architectures that increase computational complexity, our model processes multi-modal data as a single 4-channel input tensor, optimizing feature fusion while maintaining computational efficiency. The proposed architecture, based on a Modified VGG-16 CNN model, which enhances disease detection accuracy by leveraging complementary information from different imaging modalities. Experimental evaluations demonstrate significant improvements in classification accuracy compared to RGB-only models. Furthermore, our model is optimized for real-time deployment on edge computing devices, making it scalable for precision agriculture applications, including automated greenhouse monitoring, drone-based crop surveillance, and IoTintegrated farming systems. This research work highlights the transformative potential of AI-driven multi-modal plant disease detection, flagging the way for more efficient, cost-effective, and scalable agricultural solutions.

- 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 diagnosis of the plants is carried out with a visual inspection by experts and a biological examination is the second choice if necessary. They are usually expensive and time consuming. This inspired several computer methodologies to detect plant blights based on their leaf images. We apply a computer methodology on Deep Learning systems based on artificial neural networks, this branch also allows for the early detection of plant diseases, by applying convolutional neural networks (CNNs) familiar with some of the famous architectures, notably the “ResNet” architecture, using an augmented dataset containing images of healthy and diseased leaves (each leaf is manually cut and placed on a uniform background) with acceptable accuracy rates in the research environment. This Deep Learning technique has shown very good performance for various object detection problems. The model fulfills its role by classifying images into two categories (disease-free) and (diseased). According to the results obtained, the developed system achieves better detection performances than those proposed in the state of the art. Finally, to compare their performances, we use the implementation under Anaconda 2019.10.

This study aims to develop an efficient and accurate deep learning-based model for the classification of plant leaf diseases using Convolutional Neural Networks (CNN). The objective is to automate disease detection in agricultural crops to assist farmers and agricultural experts in early and reliable diagnosis. The model is trained on the publicly available “Plant Village CLAHE Processed Data” dataset, which includes high-resolution RGB images of healthy and diseased plant leaves. Images are preprocessed through resizing (128×128), normalized, and split into training, validation, and test sets. Data augmentation techniques such as flipping, zooming, and rotation are used to improve generalization. A custom CNN architecture comprising convolutional, pooling, dense, and dropout layers is employed and trained using the Adam optimizer. Exploratory Data Analysis (EDA) ensures data quality and balance. The model achieves impressive results, with 93% test accuracy, 91% precision, 93% recall, and an F1-score of 92%, indicating robust performance in identifying diverse plant diseases. Training accuracy reached 94.64% with a validation accuracy of 92.95%, confirming minimal overfitting. These results validate the model’s reliability for practical use in smart farming solutions, especially in mobile or IoT-based applications for real-time disease monitoring and precision agriculture.

Deep learning models assist ophthalmologists in early detection of diseases from retinal images and timely treatment. Owing to robust and accurate results from deep learning models, we aim to use convolutional neural network (CNN) to provide a non-invasive method for early detection of eye diseases. We used a hybridized CNN with deep learning (DL) based on two separate CNN blocks, to identify multiple Optic Disc Cupping, Diabetic Retinopathy, Media Haze, and Healthy images. We used the RFMiD dataset, which contains various categories of fundus images representing different eye diseases. Data augmenting, resizing, coping, and one-hot encoding are used among other preprocessing techniques to improve the performance of the proposed model. Color fundus images have been analyzed by CNNs to extract relevant features. Two CCN models that extract deep features are trained in parallel. To obtain more noticeable features, the gathered features are further fused utilizing the Canonical Correlation Analysis fusion approach. To assess the effectiveness, we employed eight classification algorithms: Gradient boosting, support vector machines, voting ensemble, medium KNN, Naive Bayes, COARSE- KNN, random forest, and fine KNN. With the greatest accuracy of 93.39%, the ensemble learning performed better than the other algorithms. The accuracy rates suggest that the deep learning model has learned to distinguish between different eye disease categories and healthy images effectively. It contributes to the field of eye disease detection through the analysis of color fundus images by providing a reliable and efficient diagnostic system.

The early and accurate detection of diseases in tomato plants is crucial for ensuring high crop yields and quality. Traditional methods of disease identification are often labor-intensive, time-consuming, and subject to human error. This research presents an enhanced classification system for detecting tomato leaf diseases using Convolutional Neural Networks (CNNs). Leveraging the power of deep learning, our approach significantly improves the accuracy and efficiency of disease identification compared to conventional techniques. We developed a CNN-based model that was trained and validated using a comprehensive dataset of tomato leaf images, encompassing various disease classes and healthy samples. The model architecture was meticulously designed to optimize feature extraction and classification performance. Extensive data augmentation techniques were employed to enhance the robustness and generalization capability of the model. Our enhanced classification system achieved remarkable accuracy, outperforming existing models in the literature. The results demonstrate the model's ability to effectively differentiate between multiple disease types and healthy leaves, even under challenging conditions such as varying lighting and background noise. This advancement holds great potential for integration into automated agricultural systems, providing farmers with a reliable tool for early disease detection and management.The implementation of this CNN-based improved classification system can lead to significant advancements in precision agriculture, minimizing crop losses and promoting sustainable farming practices. Future work will focus on extending this approach to other crops and refining the system for real-time field applications.

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

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.

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.

However, orange orchards are frequently threatened by a plethora of diseases that can drastically reduce yield and quality. Early and accurate disease detection is paramount for effective management and mitigation, preventing widespread crop loss and ensuring sustainable agricultural practices. Traditional disease identification methods often rely on visual inspection by experts, which can be time-consuming, subjective, and prone to human error. Moreover, the rapid spread of certain diseases necessitates swift and reliable diagnostic tools. In this context, the application of advanced technologies like deep learning offers a promising avenue for automating and enhancing disease classification in orange fruits. The advent of deep learning has revolutionized various fields, including computer vision, enabling the development of highly accurate image recognition systems. Convolutional Neural Networks (CNNs), a class of deep learning models, have demonstrated exceptional performance in extracting intricate features from images, making them well-suited for tasks like disease classification. In the domain of agricultural disease detection, CNNs have shown remarkable potential in identifying and classifying various plant diseases based on visual symptoms captured in images. . This project will encompass the entire pipeline of developing a robust disease classification system, from dataset acquisition and preprocessing to model training, evaluation, and potential deployment considerations. A comprehensive dataset of orange fruit images, encompassing various disease types and healthy samples, will be assembled. Rigorous preprocessing techniques, including data augmentation, will be employed to enhance dataset diversity and model generalization. The hybrid model will be trained and fine-tuned using appropriate optimization algorithms and evaluated using relevant performance metrics. The ultimate goal is to create a practical and effective tool that can assist farmers and agricultural experts in the early detection and management of orange fruit diseases, contributing to improved crop health and productivity.

<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>