computer-vision-9

Crop Yield Prediction

3260 papers • 126 benchmarks • 313 datasets

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2018 Syngenta (2016 val)

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Most implemented papers

A CNN-RNN Framework for Crop Yield Prediction

S. Archontoulis, Saeed Khaki, Lizhi Wang•Tue Nov 19 2019

The proposed CNN-RNN model, along with other popular methods such as random forest, deep fully connected neural networks (DFNN), and LASSO, was used to forecast corn and soybean yield across the entire Corn Belt for years 2016, 2017, and 2018 using historical data.

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Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications

Xiaoxiao Du, Alina Zare•Sat Mar 10 2018

Novel classification and regression fusion models that can be trained given ambiguously and imprecisely labeled training data in which the training labels are associated with sets of data points instead of individual data points following a multiple-instance learning framework are proposed.

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EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task

M. Reichstein, Joachim Denzler, Christian Requena-Mesa, V. Benson, J. Runge•Thu Apr 15 2021

This work frames Earth surface forecasting as the task of predicting satellite imagery conditioned on future weather, which allows localized impacts from extreme weather to be predicted, thus supporting downstream applications such as crop yield prediction, forest health assessments or biodiversity monitoring.

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The CropAndWeed Dataset: a Multi-Modal Learning Approach for Efficient Crop and Weed Manipulation

Daniel Steininger, Verena Widhalm, Julia Simon, A. Trondl, Gerardus Croonen•Sat Dec 31 2022

Precision Agriculture and especially the application of automated weed intervention represents an increasingly essential research area, as sustainability and efficiency considerations are becoming more and more relevant. While the potentials of Convolutional Neural Networks for detection, classification and segmentation tasks have successfully been demonstrated in other application areas, this relatively new field currently lacks the required quantity and quality of training data for such a highly data-driven approach. Therefore, we propose a novel large-scale image dataset specializing in the fine-grained identification of 74 relevant crop and weed species with a strong emphasis on data variability. We provide annotations of labeled bounding boxes, semantic masks and stem positions for about 112k instances in more than 8k high-resolution images of both real-world agricultural sites and specifically cultivated outdoor plots of rare weed types. Additionally, each sample is enriched with an extensive set of meta-annotations regarding environmental conditions and recording parameters. We furthermore conduct benchmark experiments for multiple learning tasks on different variants of the dataset to demonstrate its versatility and provide examples of useful mapping schemes for tailoring the annotated data to the requirements of specific applications. In the course of the evaluation, we furthermore demonstrate how incorporating multiple species of weeds into the learning process increases the accuracy of crop detection. Overall, the evaluation clearly demonstrates that our dataset represents an essential step towards overcoming the data gap and promoting further research in the area of Precision Agriculture.

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Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data

Stefano Ermon, D. Lobell, Jiaxuan You, Xiaocheng Li, Melvin Low•Sat Feb 11 2017

This work introduces a scalable, accurate, and inexpensive method to predict crop yields using publicly available remote sensing data and incorporates a Gaussian Process component to explicitly model the spatio-temporal structure of the data and further improve accuracy.

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EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts

M. Reichstein, Joachim Denzler, Christian Requena-Mesa, V. Benson, Jakob Runge•Thu Dec 10 2020

The EarthNetScore is defined, a novel ranking criterion for models forecasting Earth surface reflectance, and EarthNet2021 is introduced, a new, curated dataset containing target spatio-temporal Sentinel 2 satellite imagery at 20 m resolution, matched with high-resolution topography and mesoscale weather variables.

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Crop Yield Prediction Using Deep Neural Networks

Saeed Khaki, Lizhi Wang•Wed Feb 06 2019

A deep neural network (DNN) approach that took advantage of state-of-the-art modeling and solution techniques was designed and significantly outperformed other popular methods such as Lasso, shallow neural networks (SNN), and regression tree (RT).

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Predicting crop yields with little ground truth: A simple statistical model for in-season forecasting

N. Semret•Tue Jun 15 2021

A fully automated model for in-season crop yield prediction, designed to work where there is a dearth of sub-national ”ground truth” information, is presented, characterized by careful feature engineering combined with a simple regression model.

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Multimodal Performers for Genomic Selection and Crop Yield Prediction

H. Måløy, Susanne S. Windju, Stein Bergersen, M. Alsheikh, K. Downing•Thu Sep 30 2021

A performer-based deep learning framework for crop yield prediction using single nucleotide polymorphisms and weather data and it is shown that visualizing the self-attention maps of a Multimodal Performer network indicates that the model makes meaningful connections between genotype andWeather data that can be used by the breeder to inform breeding decisions and shorten breeding cycle length.

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A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction

Joshua Fan, Junwen Bai, Zhiyun Li, Ariel Ortiz-Bobea, Carla P. Gomes•Tue Nov 16 2021

This paper introduces a novel graph-based recurrent neural network for crop yield prediction, to incorporate both geographical and temporal knowledge in the model, and further boost predictive power, and proves the effectiveness of geospatial and temporal information.

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