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21 articles for “Leaf Features”
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Research ArticleOpen accessPhrase in the titleLEAF FEATURES EXTRACTION AND RECOGNITION APPROACHES TO CLASSIFY PLANTMohamad Faizal Ab Jabal, Suhardi Hamid, Salehuddin Shuib, Illiasaak AhmadPlant classification based on leaf identification is becoming a popular trend. Each leaf carries substantial information that can be used to identify and classify the origin or the type of plant. In medical perspective, images have been used by doctors to diagnose diseases and this method has been proven reliable for years. Using the same method as doctors, researchers try … …Journal of Computer Science, Vol. 9, Issue 10 · 5 Sep 2013 · 7,367 accessesView PDFCite
Research ArticleOpen accessIn the abstractBenchmarking Lightweight Deep Learning Models for Field Based Sorghum Leaf Disease DetectionPrince Daughin Ngqabutho NcubeSorghum is a climate-resilient cereal crop central to food and livelihood security across Southern Africa, yet its productivity is frequently reduced by foliar diseases that smallholder farmers struggle to diagnose reliably in the field. While deep learning has demonstrated strong potential for automated plant disease identification, sorghum remains underrepresented in the literature, and no prior study has provided a comprehensive, … …Journal of Computer Science, Vol. 22, Issue 10 · 29 Sep 2026 · 548 accessesView PDFCite
Research ArticleOpen accessIn the abstractEpsilon-Insensitive Primal Loss With Support Vector Machine for Robust Cotton Leaf Disease ClassificationPavana Kumar Eramachetty Dhanajayalu, Malathi Sivasamy, Chitveli Siva Balaji YadavCotton diseases significantly reduce both yield and quality of crops, making early and accurate identification of the disease crucial for effective prevention and control. However, achieving a balance between lightweight models and precise classification of cotton diseases remains challenging. This difficulty arises primarily from the dispersed nature of disease symptoms and interference from noisy backgrounds, which complicate the detection process. … …Journal of Computer Science, Vol. 22, Issue 6 · 5 Aug 2026 · 764 accessesView PDFCite
Research ArticleOpen accessIn the abstractTomato Leaf Disease Detection by Hybrid Ai and Ml TechnologyP. Loganathan, M. John Britto, Vinston RajaDiseases that affect plants contribute to productivity decline, but they can be managed with ongoing monitoring. It is time-consuming and prone to error to track plant diseases manually. Early disease detection using Artificial Intelligence (AI) and machine vision can lessen negative impacts while also overcoming some of the limitations of continuous human monitoring. Therefore, this study uses both Deep Learning … …Journal of Computer Science, Vol. 21, Issue 12 · 12 Feb 2026 · 1,889 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnhancing Plant Disease Identification Through Hyperspectral Imaging and AC-FNet FrameworkDhason Lita Pansy, Malaichamy MuraliThe precise identification of plant diseases is essential for optimizing agricultural practices. The impact of these diseases on food production often leads to significant revenue loss. Detecting diseases that primarily manifest symptoms in plant leaves has conventionally depended on visual inspection by plant pathologists. However, modern methodologies use machine learning and computer vision techniques to facilitate disease identification, thus addressing … …Journal of Computer Science, Vol. 21, Issue 5 · 21 May 2025 · 8,581 accessesView PDFCite
Research ArticleOpen accessIn the abstractDeep Learning-Based Approach for Identification of Potato Leaf Diseases Using Wrapper Feature Selection and Feature ConcatenationMuhammad Ahtsam Naeem, Muhammad Asim Saleem, Jarrar Amjad et al.The potato is a widely grown crop in many regions of the world. In recent decades, potato farming has gained incredible traction in the world. Potatoes are susceptible to several illnesses that stunt their development. This plant seems to have significant leaf disease. Early Blight and Late Blight are two prevalent leaf diseases that affect potato plants. The early detection … …Journal of Computer Science, Vol. 21, Issue 5 · 12 May 2025 · 5,178 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Accurate Mango Pest Identification Employing the Gaussian Mixture Model and Expectation-Maximization (EM) AlgorithmD. Lita Pansy, M. MuraliMangoes are fruits that originated in South and Southeast Asia. Mango fruits are consumed in high volume worldwide. However, fruit, stem, root, and mango leaves are damaged by pests, and it has a significant negative impact on mango production. Many works were developed for the pest detection of mango; but, none of the works concentrated on the practical applicability and … …Journal of Computer Science, Vol. 21, Issue 1 · 3 Dec 2024 · 5,069 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Hybrid Metaheuristic Algorithm for Diseases Classification Using UAV ImagesYagnasree Sirivella, Anuj JainRecent advances in technology are very astounding since they have made it possible to manage and monitor systems remotely. Traditional farming is undergoing a transition towards "smart farming" with the assistance of technological breakthroughs, which include the implementation of intelligent irrigation systems and the remote monitoring of the development of crops. In particular, the Unmanned Aerial Vehicle plays a significant … …Journal of Computer Science, Vol. 19, Issue 10 · 11 Sep 2023 · 5,253 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Edge Efficient and Lightweight Hybrid CNN Vision Transformer Network With CBAM for IoT Based Plant Disease DetectionNyabeye Pangop Doris-Khöler, Kengni Ngangmo OlgaDetecting crop disease early enough to act on it remains a bottleneck for smallholder and precision farming alike, particularly when diagnosis depends on manual inspection or lab analysis that cannot keep pace with an outbreak. We present AgriViTNet, a compact CNN-Vision Transformer hybrid built for exactly this constraint: It extracts local texture cues through convolution, relates them across the whole … …Journal of Computer Science, Vol. 22, Issue 9 · 20 Sep 2026 · 586 accessesView PDFCite
Research ArticleOpen accessIn the abstractApplication of Data Enhancement Method Based on Generative Adversarial Networks for Soybean Leaf Disease IdentificationXiao Yu, Cong Chen, Qi Gong, Lina LuSoybean leaf disease data collection is an expensive and time-consuming task. Convolutional neural network training requires a large amount of data, but traditional data enhancement methods (such as rotation, flipping, translation) are restricted by fixed rules and cannot generate images with diversity and variability. Aiming at the problem of the lack of soybean leaf disease data set, this study proposes … …American Journal of Biochemistry and Biotechnology, Vol. 18, Issue 4 · 26 Nov 2022 · 5,208 accessesView PDFCite