computer-vision-9

Crop Classification

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in crop-classification-19

Trend

Dataset

Best Model

Actions

CropHarvest - Kenya

CropHarvest - Togo

CropHarvest - Brazil

Libraries

i

Use these libraries to find crop-classification-19 models and implementations

Datasets

EuroCrops

Sen4AgriNet

SICKLE

Subtasks

No subtasks available.

Most implemented papers

End-to-end learned early classification of time series for in-season crop type mapping

D. Tuia, S. Lefèvre, N. Courty, Marc Rußwurm, R. Emonet, R. Tavenard•Tue Jan 29 2019

An End-to-End Learned Early Classification of Time Series (ELECTS) model that estimates a classification score and a probability of whether sufficient data has been observed to come to an early and still accurate decision and reduces the quantity of data massively to be downloaded, stored, and processed.

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Content

CropHarvest - Brazil

CropHarvest - Global

CropHarvest multicrop - Global

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Paper Graph

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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Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slides

A. Suriawinata, Bing Ren, Naofumi Tomita, S. Hassanpour, B. Abdollahi, Jason Wei•Mon Nov 19 2018

This diagnostic study describes a novel attention-based deep neural network framework for classifying microscopy images to identify Barrett esophagus and esophageal adenocarcinoma.

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Spatio-temporal crop classification of low-resolution satellite imagery with capsule layers and distributed attention

J. Brandt•Mon Apr 22 2019

Using a combination of capsule layers and long-short term memory layers with distributed attention, the present paper achieves state-of-the-art accuracy on temporal crop type classification at a 30x30m resolution with Sentinel 2 imagery.

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Crop Classification Under Varying Cloud Cover With Neural Ordinary Differential Equations

K. Schindler, Nando Metzger, J. D. Wegner, Mehmet Ozgur Turkoglu, Stefano D'aronco•Thu Dec 03 2020

This work proposes to use neural ordinary differential equations (NODEs) in combination with RNNs to classify crop types in irregularly spaced image sequences and shows that ODE-RNN improves classification accuracy over common baselines, such as LSTM, GRU, temporal convolutional network, and transformer.

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Crop mapping from image time series: deep learning with multi-scale label hierarchies

K. Schindler, J. D. Wegner, Mehmet Ozgur Turkoglu, Stefano D'aronco, Gregor Perich, F. Liebisch, Constantin Streit•Tue Feb 16 2021

An end-to-end trainable, hierarchical network architecture allows the model to learn joint feature representations of rare classes at a coarser level, thereby boosting classification performance at the fine-grained level.

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Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series

Loic Landrieu, F'elix Quinton•Wed Sep 29 2021

This paper takes advantage of the increasing quantity of annotated satellite data to model simultaneously the inter- and intra-annual agricultural dynamics of yearly parcel classification with a deep learning approach and releases the first large-scale multi-year agricultural dataset with over 300,000 annotated parcels.

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TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimation

S. Lefèvre, Charlotte Pelletier, Joachim Nyborg, I. Assent•Wed Nov 03 2021

TimeMatch is proposed, which explicitly accounts for the temporal shift of SITS for improved SITS-based domain adaptation and introduces an open-access dataset for cross-region adaptation from SITS in four different regions in Europe.

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Generalized Classification of Satellite Image Time Series with Thermal Positional Encoding

Charlotte Pelletier, Joachim Nyborg, I. Assent•Wed Mar 16 2022

This work proposes Thermal Positional Encoding (TPE) for attention-based crop classifiers, which addresses the temporal shifts between different regions to improve generalization and demonstrates the approach on a crop classification task across four different European regions.

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