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51 articles for “Crop Disease”
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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 · 898 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 · 7,497 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 · 7,547 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleAutomating Paddy Crop Disease Classification With Deep Learning ModelsShiva Shankar J, S. Palanivel, S. China VenkateswarluRice is a staple food crop for more than 10 countries. High consumption of rice demands better yield of crop. Timely disease diagnosis in paddy is fundamental to preventing yield losses and ensuring an adequate supply of rice for a rapidly rising worldwide population. Agriculture and modern farming is one of the fields where IoT and automation can have a … …Journal of Computer Science, Vol. 21, Issue 8 · 1 Sep 2025 · 5,696 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 · 3,101 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 · 1,599 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 · 535 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 · 578 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 · 922 accessesView PDFCite
Research ArticleOpen accessIn the abstractIntegrated Management and Fungicide Efficacy of Foliar Diseases in Spring Wheat in KazakhstanNadira Sultanova, Yerlan Dutbayev, Gulzada Abisheva et al.Spring wheat (Triticum aestivum L.) is a key economic crop in Kazakhstan. This study aimed to identify genetic sources conferring resistance to major foliar diseases and to quantify the contribution of fungicide application to yield stability. Field experiments conducted in 2024 assessed the resistance of 36 spring wheat genotypes, 17 common wheat and 19 durum wheat (T. durum Desf.), to … …OnLine Journal of Biological Sciences, Vol. 26, Issue 2 · 17 Jul 2026 · 961 accessesView PDFCite