Two-dimensional Deep Regression for Early Yield Prediction of Winter Wheat

• URL: http://arxiv.org/abs/2111.08069v1
• Date: Mon, 15 Nov 2021 20:40:15 GMT
• Title: Two-dimensional Deep Regression for Early Yield Prediction of Winter Wheat
• Authors: Giorgio Morales, John W. Sheppard
• Abstract summary: We tackle the yield prediction problem using a Convolutional Neural Network (CNN) trained on data that combines radar satellite imagery and on-ground information. We utilize radar data acquired from the Sentinel-1 satellites, while the on-ground data correspond to a set of six features. We show that our proposed methodology yields better results than five compared methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Crop yield prediction is one of the tasks of Precision Agriculture that can be automated based on multi-source periodic observations of the fields. We tackle the yield prediction problem using a Convolutional Neural Network (CNN) trained on data that combines radar satellite imagery and on-ground information. We present a CNN architecture called Hyper3DNetReg that takes in a multi-channel input image and outputs a two-dimensional raster, where each pixel represents the predicted yield value of the corresponding input pixel. We utilize radar data acquired from the Sentinel-1 satellites, while the on-ground data correspond to a set of six raster features: nitrogen rate applied, precipitation, slope, elevation, topographic position index (TPI), and aspect. We use data collected during the early stage of the winter wheat growing season (March) to predict yield values during the harvest season (August). We present experiments over four fields of winter wheat and show that our proposed methodology yields better results than five compared methods, including multiple linear regression, an ensemble of feedforward networks using AdaBoost, a stacked autoencoder, and two other CNN architectures.

Related papers

• Rethinking Transformers Pre-training for Multi-Spectral Satellite Imagery [78.43828998065071]
  Recent advances in unsupervised learning have demonstrated the ability of large vision models to achieve promising results on downstream tasks. Such pre-training techniques have also been explored recently in the remote sensing domain due to the availability of large amount of unlabelled data. In this paper, we re-visit transformers pre-training and leverage multi-scale information that is effectively utilized with multiple modalities.
  arXiv  Detail & Related papers  (2024-03-08T16:18:04Z)
• Object Detection in Thermal Images Using Deep Learning for Unmanned Aerial Vehicles [0.9208007322096533]
  This work presents a neural network model capable of recognizing small and tiny objects in thermal images collected by unmanned aerial vehicles. The backbone is developed based on the structure of YOLOv5 combined with the use of a transformer encoder at the end. The neck includes a BI-FPN block combined with the use of a sliding window and a transformer to increase the information fed into the prediction head.
  arXiv  Detail & Related papers  (2024-02-13T06:40:55Z)
• Deep-Learning Framework for Optimal Selection of Soil Sampling Sites [0.0]
  This work leverages the recent advancements of deep learning in image processing to find optimal locations that present the important characteristics of a field. Our framework is constructed with an encoder-decoder architecture with the self-attention mechanism as the backbone. The model has achieved impressive results on the testing dataset, with a mean accuracy of 99.52%, a mean Intersection over Union (IoU) of 57.35%, and a mean Dice Coefficient of 71.47%.
  arXiv  Detail & Related papers  (2023-09-02T16:19:21Z)
• High-Resolution Satellite Imagery for Modeling the Impact of Aridification on Crop Production [2.5402662954395097]
  We introduce a first-of-its-kind dataset, SICKLE, having time-series images at different spatial resolutions from 3 different satellites. The dataset comprises of 2,398 season-wise samples from 388 unique plots distributed across 4 districts of the Delta. We benchmark the dataset on 3 separate tasks, namely crop type, phenology date (sowing, transplanting, harvesting) and yield prediction, and develop an end-to-end framework for predicting key crop parameters in a real-world setting.
  arXiv  Detail & Related papers  (2022-09-25T14:54:50Z)
• Toward Data-Driven STAP Radar [23.333816677794115]
  We characterize our data-driven approach to space-time adaptive processing (STAP) radar. We generate a rich example dataset of received radar signals by randomly placing targets of variable strengths in a predetermined region. For each data sample within this region, we generate heatmap tensors in range, azimuth, and elevation of the output power of a beamformer. In an airborne scenario, the moving radar creates a sequence of these time-indexed image stacks, resembling a video.
  arXiv  Detail & Related papers  (2022-01-26T02:28:13Z)
• MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking [72.65494220685525]
  We propose a new dynamic modality-aware filter generation module (named MFGNet) to boost the message communication between visible and thermal data. We generate dynamic modality-aware filters with two independent networks. The visible and thermal filters will be used to conduct a dynamic convolutional operation on their corresponding input feature maps respectively. To address issues caused by heavy occlusion, fast motion, and out-of-view, we propose to conduct a joint local and global search by exploiting a new direction-aware target-driven attention mechanism.
  arXiv  Detail & Related papers  (2021-07-22T03:10:51Z)
• Real-time Ionospheric Imaging of S4 Scintillation from Limited Data with Parallel Kalman Filters and Smoothness [91.3755431537592]
  We create two dimensional ionospheric images of S4 amplitude scintillation at 350 km over South America with temporal resolution of one minute. Our results show that in areas with a network of ground receivers with a relatively good coverage the produced images can provide reliable real-time results.
  arXiv  Detail & Related papers  (2021-05-11T23:09:14Z)
• WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting [12.735055892742647]
  We propose a novel deep learning framework to accurately and efficiently count wheat heads to aid in the gathering of real-time data for decision making. We call our model WheatNet and show that our approach is robust and accurate for a wide range of environmental conditions of the wheat field. Our proposed method achieves an MAE and RMSE of 3.85 and 5.19 in our wheat head counting task, respectively, while having significantly fewer parameters when compared to other state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-03-17T02:38:58Z)
• Semi-supervised deep learning based on label propagation in a 2D embedded space [117.9296191012968]
  Proposed solutions propagate labels from a small set of supervised images to a large set of unsupervised ones to train a deep neural network model. We present a loop in which a deep neural network (VGG-16) is trained from a set with more correctly labeled samples along iterations. As the labeled set improves along iterations, it improves the features of the neural network.
  arXiv  Detail & Related papers  (2020-08-02T20:08:54Z)
• Pre-Trained Models for Heterogeneous Information Networks [57.78194356302626]
  We propose a self-supervised pre-training and fine-tuning framework, PF-HIN, to capture the features of a heterogeneous information network. PF-HIN consistently and significantly outperforms state-of-the-art alternatives on each of these tasks, on four datasets.
  arXiv  Detail & Related papers  (2020-07-07T03:36:28Z)
• Towards Reading Beyond Faces for Sparsity-Aware 4D Affect Recognition [55.15661254072032]
  We present a sparsity-aware deep network for automatic 4D facial expression recognition (FER) We first propose a novel augmentation method to combat the data limitation problem for deep learning. We then present a sparsity-aware deep network to compute the sparse representations of convolutional features over multi-views.
  arXiv  Detail & Related papers  (2020-02-08T13:09:11Z)

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.