Large-scale crop yield estimation is, in part, made possible due to the availability of remote sensing data allowing for the continuous monitoring of crops throughout their growth cycle. Having this information allows stakeholders the ability to make real-time decisions to maximize yield potential. Although various models exist that predict yield from remote sensing data, there currently does not exist an approach that can estimate yield for multiple crops simultaneously, and thus leads to more accurate predictions. A model that predicts the yield of multiple crops and concurrently considers the interaction between multiple crop yields. We propose a new convolutional neural network model called YieldNet which utilizes a novel deep learning framework that uses transfer learning between corn and soybean yield predictions by sharing the weights of the backbone feature extractor. Additionally, to consider the multi-target response variable, we propose a new loss function. We conduct our experiment using data from 1,132 counties for corn and 1,076 counties for soybean across the United States. Numerical results demonstrate that our proposed method accurately predicts corn and soybean yield from one to four months before the harvest with a MAE being 8.74% and 8.70% of the average yield, respectively, and is competitive to other state-of-the-art approaches.
University of Minnesota Twin Cities（明尼苏达大学双城分校）；Ant Group（蚂蚁集团）；Texas Tech University（德克萨斯理工大学）；Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences（中国科学院地理科学与资源研究所）；Stanford University（斯坦福大学）
Land cover classification in remote sensing is often faced with the challenge of limited ground truth. Incorporating historical information has the potential to significantly lower the expensive cost associated with collecting ground truth and, more importantly, enable early- and in-season mapping that is helpful to many pre-harvest decisions. In this study, we propose a new approach that can effectively transfer knowledge about the topology (i.e. relative position) of different crop types in the spectral feature space (e.g. the histogram of SWIR1 vs RDEG1 bands) to generate labels, thereby su…
Weeds are a major threat to crops and are responsible for reducing crop yield worldwide. To mitigate their negative effect, it is advantageous to accurately identify them early in the season to prevent their spread throughout the field. Traditionally, farmers rely on manually scouting fields and applying herbicides for different weeds. However, it is easy to confuse between crops with weeds during the early growth stages. Recently, deep learning-based weed identification has become popular as deep learning relies on convolutional neural networks that are capable of learning important distingui…
In the U.S., corn is the most produced crop and has been an essential part of the American diet. To meet the demand for supply chain management and regional food security, accurate and timely large-scale corn yield prediction is attracting more attention in precision agriculture. Recently, remote sensing technology and machine learning methods have been widely explored for crop yield prediction. Currently, most county-level yield prediction models use county-level mean variables for prediction, ignoring much detailed information. Moreover, inconsistent spatial resolution between crop area and …
Multi-Year Vector Dynamic Time Warping Based Crop Mapping
Scientific and Technological Research Council of Turkey (TÜBİTAK) Space Technologies Research Institute (UZAY)（土耳其科学技术研究理事会（TÜBİTAK）空间技术研究所）；Informatics Institute, Middle East Technical University（中东技术大学信息学研究所）
Information on cultivated crops is relevant for a large number of food security studies. Different scientific efforts are dedicated to generating this information from remote sensing images by means of machine learning methods. Unfortunately, these methods do not take account of the spatial-temporal relationships inherent in remote sensing images. In our paper, we explore the capability of a 3D Fully Convolutional Neural Network (FCN) to map crop types from multi-temporal images. In addition, we propose the Intersection Over Union (IOU) loss function for increasing the overlap between the pred…
Texas A&M University（德克萨斯农工大学）；Mississippi State University（密西西比州立大学）；U.S.D.A. Agriculture Research Service（美国农业部农业研究局）；U.S.D.A.- APHIS PPQ S&T（美国农业部动植物卫生检验局植物保护与检疫科学技术处）；Utah State University（犹他州立大学）；Texas A&M AgriLife Research & Extension Center（德克萨斯农工大学生命科学研究与推广中心）
The cotton boll weevil, Anthonomus grandis Boheman is a serious pest to the U.S. cotton industry that has cost more than 16 billion USD in damages since it entered the United States from Mexico in the late 1800s. This pest has been nearly eradicated; however, southern part of Texas still faces this issue and is always prone to the pest reinfestation each year due to its sub-tropical climate where cotton plants can grow year-round. Volunteer cotton (VC) plants growing in the fields of inter-seasonal crops, like corn, can serve as hosts to these pests once they reach pin-head square stage (5-6 l…
University of Virginia, School of Data Science（弗吉尼亚大学数据科学学院）
Hyperspectral Imagining is a type of digital imaging in which each pixel contains typically hundreds of wavelengths of light providing spectroscopic information about the materials present in the pixel. In this paper we provide classification methods for determining crop type in the USGS GHISACONUS data, which contains around 7,000 pixel spectra from the five major U.S. agricultural crops (winter wheat, rice, corn, soybeans, and cotton) collected by the NASA Hyperion satellite, and includes the spectrum, geolocation, crop type, and stage of growth for each pixel. We apply standard LDA and QDA …
Beijing Institute of Technology（北京理工大学）；Mississippi State University（密西西比州立大学）
Multi-source image registration is challenging due to intensity, rotation, and scale differences among the images. Considering the characteristics and differences of multi-source remote sensing images, a feature-based registration algorithm named Multi-scale Histogram of Local Main Orientation (MS-HLMO) is proposed. Harris corner detection is first adopted to generate feature points. The HLMO feature of each Harris feature point is extracted on a Partial Main Orientation Map (PMOM) with a Generalized Gradient Location and Orientation Histogram-like (GGLOH) feature descriptor, which provides hi…
P2PFormer: A Primitive-to-polygon Method for Regular Building Contour Extraction from Remote Sensing Images
School of Remote Sensing and Information Engineering, Wuhan University（武汉大学遥感信息工程学院）；College of Oceanography and Space Informatics, China University of Petroleum (East China)（中国石油大学（华东）海洋与空间信息学院）；School of Surveying and Geoinformation Engineering, East China University of Technology（东华理工大学测绘与地理信息工程学院）
Adaptive Fusion of Multi-view Remote Sensing data for Optimal Sub-field Crop Yield Prediction
University of Kaiserslautern-Landau (RPTU)（凯撒斯劳滕-兰道大学（RPTU））；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Vision Impulse GmbH（Vision Impulse 有限公司）
Deep learning has become in recent years a cornerstone tool fueling key innovations in the industry, such as autonomous driving. To attain good performances, the neural network architecture used for a given application must be chosen with care. These architectures are often handcrafted and therefore prone to human biases and sub-optimal selection. Neural Architecture Search (NAS) is a framework introduced to mitigate such risks by jointly optimizing the network architectures and its weights. Albeit its novelty, it was applied on complex tasks with significant results - e.g. semantic image segm…
VectorLLM: Human-like Extraction of Structured Building Contours vis Multimodal LLMs
School of Remote Sensing and Information Engineering（遥感与信息工程学院）；College of Oceanography and Space Informatics（海洋学与空间信息学院）；School of Surveying and Geoinformation Engineering（测绘与地理信息工程学院）
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；Institute of Electronics, Chinese Academy of Sciences（中国科学院电子学研究所）；University of Chinese Academy of Science（中国科学院大学）
Arbitrary-oriented object detection is an important task in the field of remote sensing object detection. Existing studies have shown that the polar coordinate system has obvious advantages in dealing with the problem of rotating object modeling, that is, using fewer parameters to achieve more accurate rotating object detection. However, present state-of-the-art detectors based on deep learning are all modeled in Cartesian coordinates. In this article, we introduce the polar coordinate system to the deep learning detector for the first time, and propose an anchor free Polar Remote Sensing Obje…
Learning county from pixels: corn yield prediction with attention-weighted multiple instance learning
Biological Systems Engineering, University of Wisconsin-Madison（威斯康星大学麦迪逊分校生物系统工程系）；Department of Earth System Science and Center on Food Security and the Environment, Stanford University（斯坦福大学地球系统科学系和粮食安全与环境中心）；Department of Geography, University of Wisconsin-Madison（威斯康星大学麦迪逊分校地理系）；Research and Development Division, National Agricultural Statistics Service, United States Department of Agriculture（美国农业部国家农业统计服务处研发部）
College of Land Science and Technology, China Agricultural University（土地科学与技术学院，中国农业大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（航空信息研究所，中国科学院）；Faculty of Geosciences and Engineering, Southwest Jiaotong University（地质科学与工程学院，西南交通大学）；Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology（德累斯顿-罗斯托克亥姆霍尔茨中心，资源技术亥姆霍尔茨研究所）；Lancaster Environment Centre, Lancaster University（兰卡斯特大学环境研究中心）
MT-CYP-Net: Multi-Task Network for Pixel-Level Crop Yield Prediction Under Very Few Samples
State Key Laboratory of Water Resources Engineering and Management, Wuhan University（水利水电工程与管理国家重点实验室，武汉大学）；School of Computer Science, Wuhan University（武汉大学计算机学院）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（测绘遥感信息工程国家重点实验室，武汉大学）；College of Agricultural Science and Engineering, Hohai University（河海大学农业科学与工程学院）
Many commodity crops have growth stages during which they are particularly vulnerable to stress-induced yield loss. In-season crop progress information is useful for quantifying crop risk, and satellite remote sensing (RS) can be used to track progress at regional scales. At present, all existing RS-based crop progress estimation (CPE) methods which target crop-specific stages rely on ground truth data for training/calibration. This reliance on ground survey data confines CPE methods to surveyed regions, limiting their utility. In this study, a new method is developed for conducting RS-based i…
