New and exciting development in the agricultural industry that makes use of AI

New and exciting development in the agricultural industry that makes use of AI

New and exciting development in the agricultural industry that makes use of AI

IT  
378
  • vegetation
  • plant
  • crop
  • cultivation
  • stress
  • agriculture
  • A characterization of biotic stress experienced by coffee leaves using few-shot learning
  • Using multivariate sensed data to develop a multi-label tiny ML machine learning model for active and optimal greenhouse microclimate management.
  • Analysis of land potential for maize cultivation using geospatial technology in the Didessa river basin, Ethiopia
  • A deep convolutional neural network based on an estimation of the virtual NIR channel was developed in order to segment damaged vegetation from RGB photographs.
  • Deep learning was used in Maize Crop to detect diseases, predict severity, and calculate crop loss.
  • A comprehensive analysis of machine learning approaches to livestock identification, including data sets, methodology, and potential future directions.
  • Genus classification of silkworm pupae based on X-ray images with the use of non-destructive ensemble learning
  • Prediction of Potassium Exchangeability in Soil Using Mid-Infrared Spectroscopy and Deep Learning: Moving from Prediction to Explainability
  • Analysis based on conditional learning of the generalization capabilities of the model for plant culture.
  • Optimizing nutrient levels for plant development in aquaponic systems by using machine learning on relatively limited training data sets
  • Comparative research on biotic stress categorization of rice crop using deep neural networks that have been pre-trained
  • Optimization strategies used within deep convolutional neural networks and applied to the categorization of olive diseases
  • Deep Convolutional Neural Network-Based Models for Weed Identification in Greenhouse-Grown Peppers
  • An investigation into how well deep learning algorithms can distinguish between weed and crop species when presented with a variety of image backgrounds.
  • Agricultural data analysis using explainable artificial intelligence and interpretable machine learning
  • Durum wheat yield estimation using machine learning
  • In central China, the effect and economic value of using precision seeding and laser field leveling for winter wheat
  • A discussion on the technology of the Internet of Things in agriculture
  • Deep Learning-Powered Computer Vision Approaches for Use in Smart Agriculture Applications
  • An investigation into the relationship between artificial intelligence and the agricultural and food processing industries
  • Single tree point cloud data can be automatically recorded without the need for markers via rotational projection.
  • Transfer learning for image classification of multi-crop leaf diseases using the VGG convolutional neural network
  • The use of near-infrared hyperspectral images in order to estimate the true density of commercial biomass pellets.