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15 results for “Crop Disease Detection”
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Research ArticleOpen accessPhrase in the titleCrop Disease Detection via an Improved Residual NetworkGuolin Chen, Weifeng Liu, Xinyu Zou, Xiaoxia LiDeep learning has shown substantial potential across many domains, and crop disease detection is no exception. This study investigates the application of deep learning to crop disease detection, systematically evaluating the performance of convolutional neural networks of different depths. Building on the classical ResNet-50 as the base model, we introduce a Squeeze-and-Excitation (SE) attention mechanism to enhance feature extraction. The … …American Journal of Biochemistry and Biotechnology, Vol. 22, Issue 2 · 31 Jul 2026 · 905 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleCrop Disease Detection Using Deep Learning Techniques on ImagesKinjal Vijaybhai Deputy, Kalpdrum Passi, Chakresh Kumar JainAgriculture plays a crucial role in the economic development of many countries and sustains the global population despite facing various challenges like climate change, pollinator decline, and plant diseases. These threats to food security highlight the need for innovative solutions to prevent crop loss. Leveraging smartphone technology for automated image recognition-based disease diagnosis has emerged as a promising approach, thanks … …Journal of Computer Science, Vol. 19, Issue 12 · 25 Oct 2023 · 7,503 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleAgricultural Crop Disease Detection Using Convolutional Neural NetworksAtsu Alagah Komlavi, Harouna Naroua, Chaibou KadriMitigating food insecurity in Niger is of paramount importance. According to some researchers, agricultural yield is declining. The permanent annual food shortage was estimated to be between 25 and 50%. Loss of cereal agricultural production is caused by pests and plant diseases. Knowing that ordinary techniques became inefficient. Automatic detection of plant diseases using artificial intelligence becomes the best solution. … …Journal of Computer Science, Vol. 21, Issue 3 · 24 Jan 2025 · 7,562 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleEarly Plant Disease Detection Using Graph Isomorphic Networks: Enhancing Crop Yield Through Leaf AnalysisD. Sumathi, Sreejyothsna Ankam, Pravin Prakash Adivarekar et al.The economy of Tanzania is mostly driven by agriculture. Disease is one of the reasons that contributes to the low production of staple foods like cassava and maize, alongside climate change. Loss of income and food security are the results. In order to detect the diseases early, preventative measures are required. A potential option for farmers could be the use … …Journal of Computer Science, Vol. 21, Issue 9 · 15 Oct 2025 · 3,107 accessesView PDFCite
Research ArticleOpen accessIn the keywordsImplementation and Performance Evaluation of YOLOv8 for Wheat Fungal Disease DetectionShivani Sood, Shallu Duggal, Monika Sethi et al.Among the many difficulties in producing wheat, a vital food source for the world, there are biotic variables like fungal diseases, which drastically lower crop output. Around the world, biotrophic fungi particularly, leaf rust, powdery mildew, and yellow rusts-have become a constant threat to wheat production. Traditional methods of identification and mitigation are labor-intensive, slow, and imprecise, limiting therefore early … …Journal of Computer Science, Vol. 22, Issue 4 · 2 May 2026 · 1,609 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 abstractA Data Mining Driven Framework for Image-Based Crop Stress Analysis Using IPCA, FP-Growth, ConvLSTM, and Sequential ModelsA. Josephine, A. SubhashiniDetecting crop stress before it becomes serious has a major impact on the food supply. It helps in avoiding the loss of crops, increases productivity and helps farmers to practice precision farming. Most technologies today are designed specifically to either detect crop stress or to predict how well the crop will grow. Many of the technologies do not combine the … …Journal of Computer Science, Vol. 22, Issue 8 · 21 Aug 2026 · 933 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Intelligent Hybrid Machine Learning Model for Paddy Disease DetectionS. Hrushikesava Raju, S. Adinarayna, U. Sesadri et al.Most countries rely on paddy/rice as a preferred staple crop due to its desirable agronomic characteristics. However, during the growth cycle, undetected or delayed identification of diseases can significantly reduce crop yield. To address this, machine learning approaches are increasingly employed for early-stage disease detection. Traditional Recurrent Neural Networks (RNNs), while useful for sequential data, suffer from limitations such as … …Journal of Computer Science, Vol. 22, Issue 4 · 29 Apr 2026 · 1,754 accessesView PDFCite
Research ArticleOpen accessIn the abstractOptimized Fractal Fusion Driven Deep Neural Network: An Efficient Hybrid Methodology for Disease Detection and Classification in Plant LeavesDeepkiran Munjal, Mrinal Pandey, Laxman SinghPlant diseases and pest attacks are among the major issues in agriculture, which have a great impact on crop growth and yield. Early and correct detection of these diseases is vital for effective management and keeping plant health at the desired level. This research proposes a new method for detection and classification of plant diseases, the Optimized Fractional Fusion Deep … …Journal of Computer Science, Vol. 21, Issue 12 · 8 Jan 2026 · 2,228 accessesView PDFCite
Research ArticleOpen accessIn the abstractInternet of Things and Human Neocortex Inspired Algorithms for the Plant Disease Prediction-Sheath Blight for Paddy PlantThangaraj Ethilu, K. Parthiban, Arockia Jayadhas S et al.Sheath blight disease (Rhizoctonia solani) poses a critical threat to paddy (Oryza sativa) production, causing up to 50% yield losses under conducive environmental conditions. Existing computer vision-based disease detection systems identify infections only after symptom manifestation, while conventional machine learning prediction models suffer from high computational demands, data preprocessing requirements, and noise sensitivity. This research proposes a novel Hierarchical Temporal … …Journal of Computer Science, Vol. 21, Issue 11 · 18 Dec 2025 · 1,780 accessesView PDFCite