Field-Level Crop Type Classification with k Nearest Neighbors: A Baseline for a New Kenya Smallholder Dataset

• URL: http://arxiv.org/abs/2004.03023v1
• Date: Mon, 6 Apr 2020 22:24:44 GMT
• Title: Field-Level Crop Type Classification with k Nearest Neighbors: A Baseline for a New Kenya Smallholder Dataset
• Authors: Hannah Kerner, Catherine Nakalembe, Inbal Becker-Reshef
• Abstract summary: We provide context for the new western Kenya dataset which was collected during an atypical 2019 main growing season. We demonstrate classification accuracy up to 64% for maize and 70% for cassava using k Nearest Neighbors--a fast, interpretable, and scalable method that can serve as a baseline for future work.
• Score: 4.83420384410068
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate crop type maps provide critical information for ensuring food security, yet there has been limited research on crop type classification for smallholder agriculture, particularly in sub-Saharan Africa where risk of food insecurity is highest. Publicly-available ground-truth data such as the newly-released training dataset of crop types in Kenya (Radiant MLHub) are catalyzing this research, but it is important to understand the context of when, where, and how these datasets were obtained when evaluating classification performance and using them as a benchmark across methods. In this paper, we provide context for the new western Kenya dataset which was collected during an atypical 2019 main growing season and demonstrate classification accuracy up to 64% for maize and 70% for cassava using k Nearest Neighbors--a fast, interpretable, and scalable method that can serve as a baseline for future work.

Related papers

• HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing [50.4506590177605]
  HarvestNet is a dataset for mapping the presence of farms in the Ethiopian regions of Tigray and Amhara during 2020-2023. We introduce a new approach based on the detection of harvest piles characteristic of many smallholder systems. We conclude that remote sensing of harvest piles can contribute to more timely and accurate cropland assessments in food insecure regions.
  arXiv  Detail & Related papers  (2023-08-23T11:03:28Z)
• How accurate are existing land cover maps for agriculture in Sub-Saharan Africa? [15.186674512627876]
  EO-based monitoring systems require accurate cropland maps to provide information about croplands. There is a lack of data to determine which of the many available land cover maps most accurately identify cropland in African countries.
  arXiv  Detail & Related papers  (2023-07-05T18:17:23Z)
• Boosting Crop Classification by Hierarchically Fusing Satellite, Rotational, and Contextual Data [0.0]
  We propose a novel approach to fuse multimodal information into a model for improved accuracy and robustness across multiple years and countries. To evaluate our approach, we release a new annotated dataset of 7.4 million agricultural parcels in France and Netherlands.
  arXiv  Detail & Related papers  (2023-05-19T21:42:53Z)
• Agave crop segmentation and maturity classification with deep learning data-centric strategies using very high-resolution satellite imagery [101.18253437732933]
  We present an Agave tequilana Weber azul crop segmentation and maturity classification using very high resolution satellite imagery. We solve real-world deep learning problems in the very specific context of agave crop segmentation. With the resulting accurate models, agave production forecasting can be made available for large regions.
  arXiv  Detail & Related papers  (2023-03-21T03:15:29Z)
• CvS: Classification via Segmentation For Small Datasets [52.821178654631254]
  This paper presents CvS, a cost-effective classifier for small datasets that derives the classification labels from predicting the segmentation maps. We evaluate the effectiveness of our framework on diverse problems showing that CvS is able to achieve much higher classification results compared to previous methods when given only a handful of examples.
  arXiv  Detail & Related papers  (2021-10-29T18:41:15Z)
• Early- and in-season crop type mapping without current-year ground truth: generating labels from historical information via a topology-based approach [6.8222552619003505]
  We propose a new approach that can effectively transfer knowledge about the topology of different crop types to generate labels. We tested this approach for mapping corn/soybeans in the US Midwest and paddy rice/corn/soybeans in Northeast China using Landsat-8 and Sentinel-2 data.
  arXiv  Detail & Related papers  (2021-10-19T21:44:02Z)
• CIM: Class-Irrelevant Mapping for Few-Shot Classification [58.02773394658623]
  Few-shot classification (FSC) is one of the most concerned hot issues in recent years. How to appraise the pre-trained FEM is the most crucial focus in the FSC community. We propose a simple, flexible method, dubbed as Class-Irrelevant Mapping (CIM)
  arXiv  Detail & Related papers  (2021-09-07T03:26:24Z)
• Two Shifts for Crop Mapping: Leveraging Aggregate Crop Statistics to Improve Satellite-based Maps in New Regions [11.371275175634413]
  In many regions crop type mapping with satellite data remains constrained by a scarcity of field-level crop labels for training supervised classification models. We present a methodology that uses aggregate-level crop statistics to correct the classifier by accounting for shifts in the crop type composition. We demonstrate that this methodology leads to substantial improvements in overall classification accuracy when using Linear Discriminant Analysis (LDA) to map crop types in Occitanie, France and in Western Province, Kenya.
  arXiv  Detail & Related papers  (2021-09-02T23:33:03Z)
• Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection [60.83360138070649]
  The paper presents an approach for analyzing aerial images of a potato crop using deep neural networks. The main objective is to demonstrate automated spatial recognition of a healthy versus stressed crop at a plant level. Experimental validation demonstrated the ability for distinguishing healthy and stressed plants in field images, achieving an average Dice coefficient of 0.74.
  arXiv  Detail & Related papers  (2021-06-14T21:57:40Z)
• Two-View Fine-grained Classification of Plant Species [66.75915278733197]
  We propose a novel method based on a two-view leaf image representation and a hierarchical classification strategy for fine-grained recognition of plant species. A deep metric based on Siamese convolutional neural networks is used to reduce the dependence on a large number of training samples and make the method scalable to new plant species.
  arXiv  Detail & Related papers  (2020-05-18T21:57:47Z)

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.