| Agritech - New Gene Drive System Offers Hope for Sustainable Agriculture |
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The latest agritech news from AZoLifeSciences |
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 Advancing AgriTech: Using PTR-MS Technology for Livestock and Crop Research
PTR-MS technology is a powerful tool in livestock and agricultural research, enabling precise analysis of VOCs from animals like sheep, chickens, pigs, and cattle, as well as agricultural field emissions.
Learn about its applications and how PTR-MS is advancing biological research, helping to develop more sophisticated solutions for the agricultural industry.
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| | | | |  Researchers have developed AI-based computer vision systems to identify growth-stunted salmon, with YoloV7 achieving the highest accuracy. This technology offers efficient and reliable monitoring, improving fish welfare and production in aquaculture. | | | | |  Post-doctoral researcher Joe Edwards and graduate student Sarah Love, both in the Department of Ecology and Evolutionary Biology, published findings this spring that can save fellow researchers a lot of time and energy when storing soil samples for later study of their microbial content. | | | | |  A review in Artificial Intelligence in Agriculture compared machine learning (ML) and deep learning (DL) for weed detection. The study found DL offers higher accuracy, while ML excels in real-time processing with smaller models, addressing challenges like visual similarity and early-stage weed control. | | | | |  Researchers applied multiple machine learning techniques to predict the unconfined compressive strength (UCS) of cohesive soil reconstituted with cement and lime. Gradient boosting and k-nearest neighbor models demonstrated the highest accuracy, revealing maximum dry density, consistency limits, and cement content as key factors influencing UCS, providing reliable predictions for engineering applications. | | | | |  Researchers introduced RMS-DETR, a multi-scale feature enhanced detection transformer, to identify weeds in rice fields using UAV imagery. This innovative approach, designed to detect small, occluded, and densely distributed weeds, outperforms existing methods, offering precision agriculture solutions for better weed management and optimized rice production. | | | | |  Using advanced machine learning algorithms, researchers successfully classified soils based on their parent materials, achieving up to 100% accuracy. The study highlights the potential of ML techniques like ESKNN and SVM in precise soil source determination across various analytical methods. | |
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