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14 articles for “leaf images”
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Enhancing Crop Health: A Review of Image Processing Methods for Leaf Disease Identification
Abstract: This research presents an overview of different image processing techniques for the identification of leaf disease. Many algorithms can be used to identify and categorize leaf diseases in plants, and digital image processing provides a quick, dependable, and accurate method of disease detection. This paper presents various techniques used on multiple crops and the achieved accuracy for each model. Leaf disease detection is a critical task in agriculture to ensure …
Published in Current Trends in Signal Processing · Vol. 14, Issue 1, 2024 · pp. 10–14 Read article
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Classification of Plant Leaf Diseases Using Deep Learning Concepts
Abstract: Agriculture is vital to the economy of a country like India, where 70% of the workforce is employed in this sector. Plants suffering from illnesses experience a significant reduction in output. Delays in the identification of plant diseases lead to decreased yield and plant mortality. The cost of manufacturing is increased since it takes a big number of experts to manually detect plant diseases over several acres of land. The …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 3, 2025 · pp. 46–55 Read article
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An Efficient CNN Model for Automated Cotton Leaf
Abstract: Timely and accurate identification of cotton leaf diseases are essential for maintaining healthy crop production and minimizing agricultural losses. Early detection allows farmers to take preventive or corrective measures, reducing the risk of disease spread and improving overall yield. In this study, we propose a Convolutional Neural Network (CNN) based model for the automated classification of cotton leaf diseases using image-based detection techniques. The model is trained on a diverse …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 14, Issue 3, 2025 · pp. 01–10 Read article
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Automated Plant Disease Detection and Treatment Advisor Using Artificial Intelligence
Abstract: Automated plant disease detection and treatment advisors using artificial intelligence represent a significant advancement in modern agriculture. The identification of plant leaf diseases is essential to maintaining food security and agricultural output. Machine learning models, particularly deep learning algorithms like convolutional neural networks (CNNs), are trained on labeled datasets containing images of healthy and diseased plants. These models learn to classify images into different disease categories with high accuracy. Convolutional …
Published in International Journal of Cheminformatics · Vol. 1, Issue 2, 2023 · pp. 1–7 Read article
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Deep Learning based Solution for Leaf disease Detection in Crops and Fertilizer Recommendation
Abstract: The field of agriculture faces significant threats, including diseases that attack plant leaves. To address this issue, our system assists farmers in promptly detecting plant diseases using advanced technology. The user, typically a farmer, only needs to capture an image of the affected leaf and input it into our system. Our system then analyzes the uploaded image to accurately identify the specific disease afflicting the leaf. This analytical process is …
Published in Current Trends in Signal Processing · Vol. 14, Issue 3, 2024 · pp. 31–40 Read article
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Crop Disease Prediction by Machine Learning
Abstract: The classification of Crop can be classified into several methods. The data set of crop leaf illnesses, notably Bacterial Leaf Blight disease (BLB), a crop leaf disease with significant outbreaks throughout Thailand, and Brown Spot Crop disease (BSR), is classified employing image classification in this study. Additionally, image processing technology is used for identifying different types of crop leaf disease. These algorithms include the Random Forest, Decision Tree, Gradient Boost, …
Published in Trends in Machine design · Vol. 11, Issue 2, 2024 · pp. 21–25 Read article
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Plant Disease Detection Using Machine Learning
Abstract: Plant diseases significantly threaten global crop yields and affect both nutritional safety and farmer income. Accurate and early detection of plant diseases is essential for effective intervention and treatment. In this study, we used the CNN model (convolutional neural network) to explore a deep learning-based approach for plant disease classification. The model was trained and evaluated on a large dataset encompassing 38 different classes of plant disease, including healthy leaves. …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 12, Issue 2, 2025 · pp. 07–19 Read article
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Hybrid Techniques in Mango Leaf Disease Identification: Evaluating Neural Networks and Support Vector Machines
Abstract: Mango leaf diseases pose a significant threat to mango production, impacting both yield and fruit quality. Early and accurate detection of these diseases is crucial for effective management. This paper evaluates the use of hybrid techniques, specifically the integration of neural networks (NNs) and support vector machines (SVM), in the identification and classification of mango leaf diseases. NN excel in extracting complex features from images, while SVMs are robust classifiers, …
Published in Trends in Opto-electro & Optical Communication · Vol. 14, Issue 3, 2024 · pp. 19–27 Read article
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Aerodynamic Analysis of Foliage Stimulated Airfoil Design for Enhanced Strength with Carbon Fibre-reinforced Polymer
Abstract: Ever since the beginning of aviation, there has been an unwavering desire to improve airfoil design in order to improve aircraft performance. This insatiable desire for improvement has driven innumerable aerodynamicists to create a diverse range of revolutionary airfoils. This research investigates a revolutionary method to airfoil design, inspired by the complicated structures and aerodynamic qualities of natural leaves. Using biomimicry, the project seeks to increase the performance and efficiency …
Published in Journal of Polymer & Composites · Vol. 13, Issue 2, 2025 · pp. 560–616 Read article
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Agrobot: IoT Enabled Crop-care
Abstract: Agriculture serves as the foundation of global civilization, particularly in nations like India, where it accounts for roughly 70% of the GDP, underscoring its pivotal role in economic stability. Despite its significance, traditional agricultural practices have often overlooked critical elements such as disease identification and precise pesticide application, focusing primarily on conventional methods like harvesting and seedling techniques. To address these gaps and usher in a new era of efficiency …
Published in Journal of Experimental & Applied Mechanics · Vol. 15, Issue 1, 2024 · pp. 38–45 Read article
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Effect of Different Quantities of MnO Nanoparticles on Growth of Ladies Finger Abelmoschus Esculentus
Abstract: The present study deal with the effect of different quantities of Manganese Oxide Nanoparticles on the growth of Ladies finger Abelmoschus esculentus. Manganese oxide nanoparticles were synthesized by the co-precipitation method and characterized by using UV-V is spectroscopy, SEM, EDAX, FTIR, and XRD. Pot culture studies on different quantities of manganese oxide nanoparticles such as 100, 200,300, 400 and 500 mg for treatments T₀ (control) T₁, T₂, T₃, T₄, T₅ …
Published in Research and Reviews: A Journal of Toxicology · Vol. 12, Issue 2, 2022 · pp. 8–15 Read article
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GreenDiagnosis: Intelligent Crop Disease Detection Using Deep Learning Algorithm
Abstract: Agriculture in parts of India relies on labour-intensive traditions, maintaining disease-free crops is crucial. Manual methods can be inaccurate, driving farmers towards AI-based solutions. AI offers a proactive approach to address real-time farming challenges. Among these is the invasion of pests, which diminishes crop quality. Combating pest-related diseases poses a challenge, prompting innovation. Effective surveillance and early detection of crop diseases play a pivotal role in ensuring global food security …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 14, Issue 2, 2025 · pp. 8–18 Read article
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Plants Disease Detection Using TensorFlow and OpenCV
Abstract: Growing healthy and productive crops is crucial in the global battle for food security. To minimize crop losses and apply timely control measures, early and precise diagnosis of plant diseases is essential. Conventional illness detection techniques are subjective, labor-intensive, and complicated; they frequently rely on eye inspection. The TensorFlow and OpenCV libraries are used in this study to explore the use of Convolutional Neural Networks (CNNs) for plant disease discovery. …
Published in Journal of Electronic Design Technology · Vol. 15, Issue 1, 2024 · pp. 31–38 Read article
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Green Synthesis of Cobalt Oxide Nanoparticles Using Delonix regia Leaf
Abstract: Green synthesis of nanoparticles offers a sustainable and eco-friendly alternative to conventional methods. This study explores the biosynthesis of cobalt oxide nanoparticles (Co3O4 NPs) using Delonix regia leaf extract as a reducing and stabilizing agent. The synthesized Co3O4 NPs were characterized by UV-visible spectroscopy, Fourier transform infrared (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and transmission electron microscopy (TEM), confirming their uniform spherical morphology with an average size of …
Published in Journal of Modern Chemistry & Chemical Technology · Vol. 16, Issue 1, 2025 · pp. 30–38 Read article