Innovations in Agricultural Forecasting: A Multivariate Regression Study on Global Crop Yield Prediction

• URL: http://arxiv.org/abs/2312.02254v2
• Date: Thu, 14 Mar 2024 07:42:52 GMT
• Title: Innovations in Agricultural Forecasting: A Multivariate Regression Study on Global Crop Yield Prediction
• Authors: Ishaan Gupta, Samyutha Ayalasomayajula, Yashas Shashidhara, Anish Kataria, Shreyas Shashidhara, Krishita Kataria, Aditya Undurti,
• Abstract summary: This study implements 6 regression models to predict crop yields in 37 developing countries over 27 years. Given 4 key training parameters, insecticides (tonnes), rainfall (mm), temperature (Celsius), and yield (hg/ha), it was found that our Random Forest Regression model achieved a determination coefficient (r2) of 0.94, with a margin of error (ME) of.03.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The prediction of crop yields internationally is a crucial objective in agricultural research. Thus, this study implements 6 regression models (Linear, Tree, Gradient Descent, Gradient Boosting, K Nearest Neighbors, and Random Forest) to predict crop yields in 37 developing countries over 27 years. Given 4 key training parameters, insecticides (tonnes), rainfall (mm), temperature (Celsius), and yield (hg/ha), it was found that our Random Forest Regression model achieved a determination coefficient (r2) of 0.94, with a margin of error (ME) of .03. The models were trained and tested using the Food and Agricultural Organization of the United Nations data, along with the World Bank Climate Change Data Catalog. Furthermore, each parameter was analyzed to understand how varying factors could impact overall yield. We used unconventional models, contrary to generally used Deep Learning (DL) and Machine Learning (ML) models, combined with recently collected data to implement a unique approach in our research. Existing scholarship would benefit from understanding the most optimal model for agricultural research, specifically using the United Nations data.

Related papers

• Learning Augmentation Policies from A Model Zoo for Time Series Forecasting [58.66211334969299]
  We introduce AutoTSAug, a learnable data augmentation method based on reinforcement learning. By augmenting the marginal samples with a learnable policy, AutoTSAug substantially improves forecasting performance.
  arXiv  Detail & Related papers  (2024-09-10T07:34:19Z)
• A Novel Fusion of Optical and Radar Satellite Data for Crop Phenology Estimation using Machine Learning and Cloud Computing [0.0]
  In the era of big Earth observation data ubiquity, attempts have been made to accurately predict crop phenology based on Remote Sensing data. Here, we estimate phenological developments for eight major crops and 13 phenological stages across Germany at 30m scale using a novel framework.
  arXiv  Detail & Related papers  (2024-08-16T13:44:35Z)
• Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
  Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
  arXiv  Detail & Related papers  (2024-04-26T16:39:50Z)
• Naïve Bayes and Random Forest for Crop Yield Prediction [0.0]
  This study analyzes crop yield prediction in India from 1997 to 2020, focusing on various crops and key environmental factors. It aims to predict agricultural yields by utilizing advanced machine learning techniques like Linear Regression, Decision Tree, KNN, Na"ive Bayes, K-Mean Clustering, and Random Forest.
  arXiv  Detail & Related papers  (2024-04-23T16:55:45Z)
• FengWu-4DVar: Coupling the Data-driven Weather Forecasting Model with 4D Variational Assimilation [67.20588721130623]
  We develop an AI-based cyclic weather forecasting system, FengWu-4DVar. FengWu-4DVar can incorporate observational data into the data-driven weather forecasting model. Experiments on the simulated observational dataset demonstrate that FengWu-4DVar is capable of generating reasonable analysis fields.
  arXiv  Detail & Related papers  (2023-12-16T02:07:56Z)
• A Hybrid Deep Learning-based Approach for Optimal Genotype by Environment Selection [8.084449311613517]
  We used a dataset comprising 93,028 training records to forecast yields for 10,337 test records, covering 159 locations over 13 years (2003-2015) This dataset included details on 5,838 distinct genotypes and daily weather data for a 214-day growing season, enabling comprehensive analysis. We developed two novel convolutional neural network (CNN) architectures: the CNN-DNN model, combining CNN and fully-connected networks, and the CNN-LSTM-DNN model, with an added LSTM layer for weather variables.
  arXiv  Detail & Related papers  (2023-09-22T17:31:47Z)
• Learning Bayesian Networks with Heterogeneous Agronomic Data Sets via Mixed-Effect Models and Hierarchical Clustering [0.0]
  maize occupies 197 million hectares as of 2021 in sub-Saharan Africa, Asia, and Latin America. Various statistical and machine learning models, including mixed-effect models, random coefficients models, random forests, and deep learning architectures, have been devised to predict maize yield. This study introduces an innovative approach integrating random effects into Bayesian networks (BNs), leveraging their capacity to model causal and probabilistic relationships through directed acyclic graphs.
  arXiv  Detail & Related papers  (2023-08-11T21:46:45Z)
• Strict baselines for Covid-19 forecasting and ML perspective for USA and Russia [105.54048699217668]
  Covid-19 allows researchers to gather datasets accumulated over 2 years and to use them in predictive analysis. We present the results of a consistent comparative study of different types of methods for predicting the dynamics of the spread of Covid-19 based on regional data for two countries: the United States and Russia.
  arXiv  Detail & Related papers  (2022-07-15T18:21:36Z)
• 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)
• Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations [58.17039841385472]
  We take advantage of all parallel developments in mechanistic modeling and satellite data availability for advanced monitoring of crop productivity. Our model successfully estimates gross primary productivity across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites. This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms.
  arXiv  Detail & Related papers  (2020-12-07T16:23:13Z)
• Crop Yield Prediction Integrating Genotype and Weather Variables Using Deep Learning [8.786816847837976]
  We use historical performance records from Uniform Soybean Tests (UST) in North America spanning 13 years of data to build a Long Short Term Memory - Recurrent Neural Network based model to dissect and predict genotype response in multiple environments. We deploy this deep learning framework as a 'hypotheses generation tool' to unravel GxExM relationships. We envision broad applicability of this approach (via conducting sensitivity analysis and "what-if" scenarios) for soybean and other crop species under different climatic conditions.
  arXiv  Detail & Related papers  (2020-06-24T16:20:12Z)

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.