Small, Sparse, but Substantial: Techniques for Segmenting Small Agricultural Fields Using Sparse Ground Data

• URL: http://arxiv.org/abs/2005.01947v1
• Date: Tue, 5 May 2020 05:26:19 GMT
• Title: Small, Sparse, but Substantial: Techniques for Segmenting Small Agricultural Fields Using Sparse Ground Data
• Authors: Smit Marvaniya, Umamaheswari Devi, Jagabondhu Hazra, Shashank Mujumdar and Nitin Gupta
• Abstract summary: We present a multi-stage approach that uses a combination of machine learning and image processing techniques. In an evaluation using high-resolution imagery, we show that our approach has a high F-Score of 0.84 in areas with large fields and reasonable accuracy with an F-Score of 0.73 in areas with small fields.
• Score: 4.850320383827591
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The recent thrust on digital agriculture (DA) has renewed significant research interest in the automated delineation of agricultural fields. Most prior work addressing this problem have focused on detecting medium to large fields, while there is strong evidence that around 40\% of the fields world-wide and 70% of the fields in Asia and Africa are small. The lack of adequate labeled images for small fields, huge variations in their color, texture, and shape, and faint boundary lines separating them make it difficult to develop an end-to-end learning model for detecting such fields. Hence, in this paper, we present a multi-stage approach that uses a combination of machine learning and image processing techniques. In the first stage, we leverage state-of-the-art edge detection algorithms such as holistically-nested edge detection (HED) to extract first-level contours and polygons. In the second stage, we propose image-processing techniques to identify polygons that are non-fields, over-segmentations, or noise and eliminate them. The next stage tackles under-segmentations using a combination of a novel ``cut-point'' based technique and localized second-level edge detection to obtain individual parcels. Since a few small, non-cropped but vegetated or constructed pockets can be interspersed in areas that are predominantly croplands, in the final stage, we train a classifier for identifying each parcel from the previous stage as an agricultural field or not. In an evaluation using high-resolution imagery, we show that our approach has a high F-Score of 0.84 in areas with large fields and reasonable accuracy with an F-Score of 0.73 in areas with small fields, which is encouraging.

Related papers

• Agricultural Field Boundary Detection through Integration of "Simple Non-Iterative Clustering (SNIC) Super Pixels" and "Canny Edge Detection Method" [0.0]
  This article proposes a new approach to determine the suitability and green index of cultivated areas using satellite data obtained through the "Google Earth Engine" (GEE) platform. Two powerful algorithms, "SNIC (Simple Non-Iterative Clustering) Super Pixels" and "Canny Edge Detection Method", are combined. The proposed method is effective in accurately and reliably classifying randomly selected agricultural fields.
  arXiv  Detail & Related papers  (2025-02-06T22:00:41Z)
• Plant detection from ultra high resolution remote sensing images: A Semantic Segmentation approach based on fuzzy loss [2.6489824612123716]
  We tackle the challenge of identifying plant species from ultra high resolution (UHR) remote sensing images. Our approach involves introducing an RGB remote sensing dataset, characterized by millimeter-level spatial resolution. First experimental results obtained on both our UHR dataset and a public dataset are presented, showing the relevance of the proposed methodology.
  arXiv  Detail & Related papers  (2024-08-31T17:40:17Z)
• Multi-Region Transfer Learning for Segmentation of Crop Field Boundaries in Satellite Images with Limited Labels [6.79949280366368]
  We present an approach for segmentation of crop field boundaries in satellite images in regions lacking labeled data. We show that our approach outperforms existing methods and that multi-region transfer learning substantially boosts performance for multiple model architectures.
  arXiv  Detail & Related papers  (2024-03-29T22:24:12Z)
• 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)
• Multispectral Image Segmentation in Agriculture: A Comprehensive Study on Fusion Approaches [0.9790238684654974]
  This paper concentrates on the use of fusion approaches to enhance the segmentation process in agricultural applications. In this work, we compare different fusion approaches by combining RGB and NDVI as inputs for crop row detection. The experiments reveal that classical segmentation methods, utilizing techniques such as edge detection and thresholding, can effectively compete with DL-based algorithms.
  arXiv  Detail & Related papers  (2023-07-31T21:24:41Z)
• Extended Agriculture-Vision: An Extension of a Large Aerial Image Dataset for Agricultural Pattern Analysis [11.133807938044804]
  We release an improved version of the Agriculture-Vision dataset (Chiu et al., 2020b) We extend this dataset with the release of 3600 large, high-resolution (10cm/pixel), full-field, red-green-blue and near-infrared images for pre-training. We demonstrate the usefulness of this data by benchmarking different contrastive learning approaches on both downstream classification and semantic segmentation tasks.
  arXiv  Detail & Related papers  (2023-03-04T17:35:24Z)
• Unlocking large-scale crop field delineation in smallholder farming systems with transfer learning and weak supervision [14.98900824231117]
  Crop field boundaries aid in mapping crop types, predicting yields, and delivering field-scale analytics to farmers. Recent years have seen the successful application of deep learning to delineating field boundaries in industrial agricultural systems. We demonstrate the methods' success in India where we efficiently generated 10,000 new field labels.
  arXiv  Detail & Related papers  (2022-01-13T03:01:06Z)
• Grasp-Oriented Fine-grained Cloth Segmentation without Real Supervision [66.56535902642085]
  This paper tackles the problem of fine-grained region detection in deformed clothes using only a depth image. We define up to 6 semantic regions of varying extent, including edges on the neckline, sleeve cuffs, and hem, plus top and bottom grasping points. We introduce a U-net based network to segment and label these parts. We show that training our network solely with synthetic data and the proposed DA yields results competitive with models trained on real data.
  arXiv  Detail & Related papers  (2021-10-06T16:31:20Z)
• 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)
• Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study [82.02173199363571]
  We introduce a novel large-scale dataset for semi-supervised semantic segmentation in Earth Observation, the MiniFrance suite. MiniFrance has several unprecedented properties: it is large-scale, containing over 2000 very high resolution aerial images, accounting for more than 200 billions samples (pixels) We present tools for data representativeness analysis in terms of appearance similarity and a thorough study of MiniFrance data, demonstrating that it is suitable for learning and generalizes well in a semi-supervised setting.
  arXiv  Detail & Related papers  (2020-10-15T15:36:58Z)
• Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection [63.942632088208505]
  We propose a post-processing algorithm to align the segmented plane masks with edges detected in the image. This allows us to increase the accuracy of state-of-the-art approaches, while limiting ourselves to cuboid-shaped objects.
  arXiv  Detail & Related papers  (2020-03-28T18:51:43Z)
• Saliency Enhancement using Gradient Domain Edges Merging [65.90255950853674]
  We develop a method to merge the edges with the saliency maps to improve the performance of the saliency. This leads to our proposed saliency enhancement using edges (SEE) with an average improvement of at least 3.4 times higher on the DUT-OMRON dataset. The SEE algorithm is split into 2 parts, SEE-Pre for preprocessing and SEE-Post pour postprocessing.
  arXiv  Detail & Related papers  (2020-02-11T14:04:56Z)

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.