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An enhanced classification system of various rice plant diseases based on multi-level handcrafted feature extraction technique.
Sci Rep
December 2024
Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt.
The rice plant is one of the most significant crops in the world, and it suffers from various diseases. The traditional methods for rice disease detection are complex and time-consuming, mainly depending on the expert's experience. The explosive growth in image processing, computer vision, and deep learning techniques provides effective and innovative agriculture solutions for automatically detecting and classifying these diseases.
View Article and Find Full Text PDFAn innovative artificial neural network model for smart crop prediction using sensory network based soil data.
PeerJ Comput Sci
November 2024
Department of Artificial Intelligence and Data Science, Sejong University, Seoul, Gwangjin-gu, Republic of Korea.
A thriving agricultural system is the cornerstone of an expanding economy of agricultural countries. Farmers' crop productivity is significantly reduced when they choose the crop without considering environmental factors and soil characteristics. Crop prediction enables farmers to select crops that maximize crop yield and earnings.
View Article and Find Full Text PDFMagnetic ordering and dynamics in monolayers and bilayers of chromium trihalides: atomistic simulations approach.
Sci Rep
October 2024
Faculty of Physics and Astronomy, Adam Mickiewicz University in Poznań, ul, Uniwersytetu Poznańskiego 2, 61-614, Poznań, Poland.
We analyze magnetic properties of monolayers and bilayers of chromium iodide, [Formula: see text], in two different stacking configurations: AA and rhombohedral ones. Our main focus is on the corresponding Curie temperatures, hysteresis curves, equilibrium spin structures, and spin wave excitations. To obtain all these magnetic characteristic, we employ the atomistic spin dynamics and Monte Carlo simulation techniques.
View Article and Find Full Text PDFLLM-driven multimodal target volume contouring in radiation oncology.
Nat Commun
October 2024
Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea.
Article Synopsis
- Target volume contouring for radiation therapy is more complex than typical organ segmentation, requiring integration of both imaging and clinical text information.
- The solution presented is LLMSeg, a multimodal AI that combines large language models with clinical data to enhance 3D target volume delineation specifically for breast cancer radiotherapy.
- LLMSeg shows significantly better performance and efficiency in data-limited scenarios compared to traditional unimodal AI models, showcasing its potential for real-world applications in radiation oncology.
Variation of soil organic carbon stability in restored mountain marsh wetlands.
Sci Rep
October 2024
College of Life Sciences, Key Laboratory of Plant Resource Conservation and Germplasm Innovation in Mountainous Region, Guizhou University, Guiyang, 550025, China.
Article Synopsis
- The study focuses on how converting rice paddies into native marsh wetlands affects soil organic carbon (SOC) levels in mountain marshes in China, particularly in the Duliu River Wetland.
- It examines various soil properties over periods of 0, 2, 10, and 20 years post-restoration, finding that SOC, soil moisture content, and other key components generally increased, while the mineral-associated organic carbon (MAOC) to SOC ratio decreased.
- The findings highlight the ecological benefits of restoring marshlands, suggesting that these environments enhance carbon sequestration, though further research is needed to understand the stabilization of SOC during the restoration process.
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