CYPUR-NN: Crop Yield Prediction Using Regression and Neural Networks

• URL: http://arxiv.org/abs/2011.13265v1
• Date: Thu, 26 Nov 2020 12:50:58 GMT
• Title: CYPUR-NN: Crop Yield Prediction Using Regression and Neural Networks
• Authors: Sandesh Ramesh, Anirudh Hebbar, Varun Yadav, Thulasiram Gunta, and A Balachandra
• Abstract summary: Crop Yield Prediction Using Regression and Neural Networks (CYPUR-NN) is developed here as a system that will facilitate agriculturists and farmers to predict yield from a picture or by entering values via a web interface.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Our recent study using historic data of paddy yield and associated conditions include humidity, luminescence, and temperature. By incorporating regression models and neural networks (NN), one can produce highly satisfactory forecasting of paddy yield. Simulations indicate that our model can predict paddy yield with high accuracy while concurrently detecting diseases that may exist and are oblivious to the human eye. Crop Yield Prediction Using Regression and Neural Networks (CYPUR-NN) is developed here as a system that will facilitate agriculturists and farmers to predict yield from a picture or by entering values via a web interface. CYPUR-NN has been tested on stock images and the experimental results are promising.

Related papers

• Predicting Critical Heat Flux with Uncertainty Quantification and Domain Generalization Using Conditional Variational Autoencoders and Deep Neural Networks [2.517043342442487]
  We develop a conditional variational autoencoder model to augment the critical heat flux measurement data. A fine-tuned deep neural network (DNN) regression model was created and evaluated with the same dataset. The CVAE model was shown to have significantly less variability and a higher confidence after assessment of the prediction-wise relative standard deviations.
  arXiv  Detail & Related papers  (2024-09-09T16:50:41Z)
• PAODING: A High-fidelity Data-free Pruning Toolkit for Debloating Pre-trained Neural Networks [11.600305034972996]
  PAODING is a toolkit to debloat pretrained neural network models through the lens of data-free pruning. It can significantly reduce the model size and generalize on different datasets and models. It can also preserve the model fidelity in terms of test accuracy and adversarial robustness.
  arXiv  Detail & Related papers  (2024-04-30T07:24:41Z)
• Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit [56.801856519460465]
  Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations. We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
  arXiv  Detail & Related papers  (2023-04-14T09:39:06Z)
• Energy-based Out-of-Distribution Detection for Graph Neural Networks [76.0242218180483]
  We propose a simple, powerful and efficient OOD detection model for GNN-based learning on graphs, which we call GNNSafe. GNNSafe achieves up to $17.0%$ AUROC improvement over state-of-the-arts and it could serve as simple yet strong baselines in such an under-developed area.
  arXiv  Detail & Related papers  (2023-02-06T16:38:43Z)
• Forecasting West Nile Virus with Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data [0.0]
  We apply a spatially aware graph neural network model to forecast the presence of West Nile virus in Illinois. More generally, we show that graph neural networks applied to irregularly sampled geospatial data can exceed the performance of a range of baseline methods.
  arXiv  Detail & Related papers  (2022-12-21T21:08:45Z)
• NCTV: Neural Clamping Toolkit and Visualization for Neural Network Calibration [66.22668336495175]
  A lack of consideration for neural network calibration will not gain trust from humans. We introduce the Neural Clamping Toolkit, the first open-source framework designed to help developers employ state-of-the-art model-agnostic calibrated models.
  arXiv  Detail & Related papers  (2022-11-29T15:03:05Z)
• An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [73.4962254843935]
  We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
  arXiv  Detail & Related papers  (2022-04-18T23:42:18Z)
• Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future [73.03458424369657]
  In real-time forecasting in public health, data collection is a non-trivial and demanding task. 'Backfill' phenomenon and its effect on model performance has been barely studied in the prior literature. We formulate a novel problem and neural framework Back2Future that aims to refine a given model's predictions in real-time.
  arXiv  Detail & Related papers  (2021-06-08T14:48:20Z)
• The Neural Coding Framework for Learning Generative Models [91.0357317238509]
  We propose a novel neural generative model inspired by the theory of predictive processing in the brain. In a similar way, artificial neurons in our generative model predict what neighboring neurons will do, and adjust their parameters based on how well the predictions matched reality.
  arXiv  Detail & Related papers  (2020-12-07T01:20:38Z)
• Learning Curves for Drug Response Prediction in Cancer Cell Lines [29.107984441845673]
  We evaluate the data scaling properties of two neural networks (NNs) and two gradient boosting decision tree (GBDT) models trained on four drug screening datasets. The learning curves are accurately fitted to a power law model, providing a framework for assessing the data scaling behavior of these predictors.
  arXiv  Detail & Related papers  (2020-11-25T01:08:05Z)
• A Spatial-Temporal Graph Based Hybrid Infectious Disease Model with Application to COVID-19 [3.785123406103385]
  As the COVID-19 pandemic evolves, reliable prediction plays an important role for policy making. The data-driven machine learning models such as RNN can suffer in case of limited time series data such as COVID-19. We combine SEIR and RNN on a graph structure to develop a hybrid-temporal model to achieve both accuracy and efficiency in training and forecasting.
  arXiv  Detail & Related papers  (2020-10-18T19:34:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.