scholarly journals Real-Time Prediction of Crop Yields From MODIS Relative Vegetation Health: A Continent-Wide Analysis of Africa

2018 ◽  
Vol 10 (11) ◽  
pp. 1726 ◽  
Author(s):  
Lillian Petersen
Keyword(s):  
Real Time ◽  
Crop Production ◽  
Crop Yields ◽  
Ground Truth ◽  
Laptop Computer ◽  
African Countries ◽  
Ground Truth Data ◽  
Production Values ◽  
Food Shortages ◽  
Vegetation Health

Developing countries often have poor monitoring and reporting of weather and crop health, leading to slow responses to droughts and food shortages. Here, I develop satellite analysis methods and software tools to predict crop yields two to four months before the harvest. This method measures relative vegetation health based on pixel-level monthly anomalies of NDVI, EVI and NDWI indices. Because no crop mask, tuning, or subnational ground truth data are required, this method can be applied to any location, crop, or climate, making it ideal for African countries with small fields and poor ground observations. Testing began in Illinois where there is reliable county-level crop data. Correlations were computed between corn, soybean, and sorghum yields and monthly vegetation health anomalies for every county and year. A multivariate regression using every index and month (up to 1600 values) produced a correlation of 0.86 with corn, 0.74 for soybeans, and 0.65 for sorghum, all with p-values less than 10 − 6 . The high correlations in Illinois show that this model has good forecasting skill for crop yields. Next, the method was applied to every country in Africa for each country’s main crops. Crop production was then predicted for the 2018 harvest and compared to actual production values. Twenty percent of the predictions had less than 2% error, and 40% had less than 5% error. This method is unique because of its simplicity and versatility: it shows that a single user on a laptop computer can produce reasonable real-time estimates of crop yields across an entire continent.

scholarly journals Methodology for Calculating Latency of GPS Probe Data

2017 ◽  
Vol 2645 (1) ◽  
pp. 76-85 ◽  
Author(s):  
Zhongxiang Wang ◽  
Masoud Hamedi ◽  
Stanley Young
Keyword(s):  
Real Time ◽  
Ground Truth ◽  
Transportation Systems ◽  
Traffic Information ◽  
Reference Source ◽  
Traveler Information ◽  
Ground Truth Data ◽  
Real Time Traffic ◽  
Message Signs ◽  
Road Segmentation

Crowdsourced GPS probe data, such as travel time on changeable-message signs and incident detection, have been gaining popularity in recent years as a source for real-time traffic information to driver operations and transportation systems management and operations. Efforts have been made to evaluate the quality of such data from different perspectives. Although such crowdsourced data are already in widespread use in many states, particularly the high traffic areas on the Eastern seaboard, concerns about latency—the time between traffic being perturbed as a result of an incident and reflection of the disturbance in the outsourced data feed—have escalated in importance. Latency is critical for the accuracy of real-time operations, emergency response, and traveler information systems. This paper offers a methodology for measuring probe data latency regarding a selected reference source. Although Bluetooth reidentification data are used as the reference source, the methodology can be applied to any other ground truth data source of choice. The core of the methodology is an algorithm for maximum pattern matching that works with three fitness objectives. To test the methodology, sample field reference data were collected on multiple freeway segments for a 2-week period by using portable Bluetooth sensors as ground truth. Equivalent GPS probe data were obtained from a private vendor, and their latency was evaluated. Latency at different times of the day, impact of road segmentation scheme on latency, and sensitivity of the latency to both speed-slowdown and recovery-from-slowdown episodes are also discussed.

scholarly journals UAV-Based Mapping of Banana Land Area for Village-Level Decision-Support in Rwanda

2021 ◽  
Vol 13 (24) ◽  
pp. 4985
Author(s):  
Regina Kilwenge ◽  
Julius Adewopo ◽  
Zhanli Sun ◽  
Marc Schut
Keyword(s):  
Decision Support ◽  
Crop Production ◽  
Vegetation Indices ◽  
Ground Truth ◽  
Support Vector ◽  
Ground Truth Data ◽  
Crop Monitoring ◽  
Landcover Classification ◽  
Optimal Productivity ◽  
Data Richness

Crop monitoring is crucial to understand crop production changes, agronomic practice decision-support, pests/diseases mitigation, and developing climate change adaptation strategies. Banana, an important staple food and cash crop in East Africa, is threatened by Banana Xanthomonas Wilt (BXW) disease. Yet, there is no up-to-date information about the spatial distribution and extent of banana lands, especially in Rwanda, where banana plays a key role in food security and livelihood. Therefore, delineation of banana-cultivated lands is important to prioritize resource allocation for optimal productivity. We mapped the spatial extent of smallholder banana farmlands by acquiring and processing high-resolution (25 cm/px) multispectral unmanned aerial vehicles (UAV) imageries, across four villages in Rwanda. Georeferenced ground-truth data on different land cover classes were combined with reflectance data and vegetation indices (NDVI, GNDVI, and EVI2) and compared using pixel-based supervised multi-classifiers (support vector models-SVM, classification and regression trees-CART, and random forest–RF), based on varying ground-truth data richness. Results show that RF consistently outperformed other classifiers regardless of data richness, with overall accuracy above 95%, producer’s/user’s accuracies above 92%, and kappa coefficient above 0.94. Estimated banana farmland areal coverage provides concrete baseline for extension-delivery efforts in terms of targeting banana farmers relative to their scale of production, and highlights opportunity to combine UAV-derived data with machine-learning methods for rapid landcover classification.

Measuring Flood Discharge

2017 ◽  
Author(s):  
Marian Muste ◽  
Ton Hoitink
Keyword(s):  
Real Time ◽  
Water Resource Management ◽  
Large Scale ◽  
Water Cycle ◽  
Ground Truth ◽  
Flood Frequency ◽  
Main Concern ◽  
Essential Difference ◽  
Discharge Data ◽  
Ground Truth Data

With a continuous global increase in flood frequency and intensity, there is an immediate need for new science-based solutions for flood mitigation, resilience, and adaptation that can be quickly deployed in any flood-prone area. An integral part of these solutions is the availability of river discharge measurements delivered in real time with high spatiotemporal density and over large-scale areas. Stream stages and the associated discharges are the most perceivable variables of the water cycle and the ones that eventually determine the levels of hazard during floods. Consequently, the availability of discharge records (a.k.a. streamflows) is paramount for flood-risk management because they provide actionable information for organizing the activities before, during, and after floods, and they supply the data for planning and designing floodplain infrastructure. Moreover, the discharge records represent the ground-truth data for developing and continuously improving the accuracy of the hydrologic models used for forecasting streamflows. Acquiring discharge data for streams is critically important not only for flood forecasting and monitoring but also for many other practical uses, such as monitoring water abstractions for supporting decisions in various socioeconomic activities (from agriculture to industry, transportation, and recreation) and for ensuring healthy ecological flows. All these activities require knowledge of past, current, and future flows in rivers and streams. Given its importance, an ability to measure the flow in channels has preoccupied water users for millennia. Starting with the simplest volumetric methods to estimate flows, the measurement of discharge has evolved through continued innovation to sophisticated methods so that today we can continuously acquire and communicate the data in real time. There is no essential difference between the instruments and methods used to acquire streamflow data during normal conditions versus during floods. The measurements during floods are, however, complex, hazardous, and of limited accuracy compared with those acquired during normal flows. The essential differences in the configuration and operation of the instruments and methods for discharge estimation stem from the type of measurements they acquire—that is, discrete and autonomous measurements (i.e., measurements that can be taken any time any place) and those acquired continuously (i.e., estimates based on indirect methods developed for fixed locations). Regardless of the measurement situation and approach, the main concern of the data providers for flooding (as well as for other areas of water resource management) is the timely delivery of accurate discharge data at flood-prone locations across river basins.

scholarly journals From Village to Globe: A Dynamic Real-Time Map of African Fields Through PlantVillage

2021 ◽  
Vol 5 ◽  
Author(s):  
Annalyse Kehs ◽  
Peter McCloskey ◽  
John Chelal ◽  
Derek Morr ◽  
Stellah Amakove ◽  
...  
Keyword(s):  
Real Time ◽  
Ground Truth ◽  
Cost Effective ◽  
Quality Data ◽  
Learning Tools ◽  
High Quality ◽  
Ground Truth Data ◽  
Cost Effective Method ◽  
High Throughput Method ◽  
Major Bottleneck

A major bottleneck to the application of machine learning tools to satellite data of African farms is the lack of high-quality ground truth data. Here we describe a high throughput method using youth in Kenya that results in a cost-effective method for high-quality data in near real-time. This data is presented to the global community, as a public good and is linked to other data sources that will inform our understanding of crop stress, particularly in the context of climate change.

scholarly journals Crop Productivity and Implications for Food Security and Rural Livelihood Development in Africa

2020 ◽  
Author(s):  
Mamudu A. Akudugu ◽  
Katherine K. Millar ◽  
Salifu Mubarik
Keyword(s):  
Food Security ◽  
Crop Production ◽  
Crop Productivity ◽  
Small Scale ◽  
Negative Consequences ◽  
African Countries ◽  
The Sustainable Development ◽  
Rural Livelihood ◽  
Food Shortages ◽  
Development Goals

Over the past few decades, there have been major advances in crop productivity across the world, which has been made possible through a combination of productivity enhancing technological innovations. Beyond this achievement however, most parts of Africa are still battling with low crop productivity resulting in food shortages and food insecurity. The yields of many staple crops are still far below their agronomic potentials with output increases being attributed largely to area expansion. This paper examines the implications of the current trends of crop/plant productivity for food security and rural livelihood development in Africa using Ghana as a case study. The paper argues that crop production in Africa is becoming a less viable and unattractive livelihood activity with farmers diversifying out of agriculture into non-agricultural activities such as illegal small-scale mining, which have negative consequences on the ability of African countries to attain the Sustainable Development Goals (SDGs).

African Smallholder Farmers on the Move: Farm and Non-Farm Trends for Six Sub-Saharan African Countries, 2002–15

2018 ◽  
Author(s):  
Magnus Jirström ◽  
Maria Archila Bustos ◽  
Sarah Alobo Loison
Keyword(s):  
Crop Production ◽  
Crop Yields ◽  
Smallholder Farmers ◽  
Crop Productivity ◽  
Sub Saharan Africa ◽  
African Countries ◽  
Production Area ◽  
Sub Saharan ◽  
The One ◽  
High Degree

This chapter provides a broad descriptive background of central aspects of smallholder agriculture in six countries in sub-Saharan Africa (SSA). It offers an up-to-date picture of the current trends of crop production, area productivity, levels of commercialization, and sources of cash incomes among 2,500 farming households. Structured around smallholder production, commercialization, and diversification in the period 2002–15, the chapter points on the one hand at persistent challenges such as low crop yields, low levels of output per farm, and a high degree of subsistence farming, and on the other hand at positive change over time in terms of growth in crop production and increasing levels of commercialization. It points at large variations not only between countries and time periods but also at the village levels, where gaps in crop productivity between farms remain large. Implicitly it points at the potential yet to be exploited in the SSA smallholder sector.

scholarly journals Data Processing Techniques for Real-Time Traveler Information: Use of Dedicated Short-Range Communications Probes on Suburban Arterial

2020 ◽  
Vol 14 (1) ◽  
pp. 99-108
Author(s):  
Jinhwan Jang
Keyword(s):  
Data Processing ◽  
Travel Time ◽  
Real Time ◽  
Sequence Data ◽  
Time Lag ◽  
Ground Truth ◽  
Prediction Errors ◽  
Travel Times ◽  
Travel Time Prediction ◽  
Ground Truth Data

Background: As wireless communication technologies evolve, probe-based travel-time collection systems are becoming popular around the globe. However, two problems generally arise in probe-based systems: one is the outlier and the other is time lag. To resolve the problems, methods for outlier removal and travel-time prediction need to be applied. Methods: In this study, data processing methods for addressing the two issues are proposed. After investigating the characteristic of the travel times on the test section, the modified z-score was suggested for censoring outliers contained in probe travel times. To mitigate the time-lag phenomenon, a recurrent neural network, a class of deep learning where temporal sequence data are normally treated, was applied to predict travel times. Results: As a result of evaluation with ground-truth data obtained through test-car runs, the proposed methods showed enhanced performances with prediction errors lower than 13% on average compared to current practices. Conclusion: The suggested methods can make drivers to better arrange their trip schedules with real-time travel-time information with improved accuracy.

scholarly journals Real-Time Queue Length Detection with Roadside LiDAR Data

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2342 ◽  
Author(s):  
Jianqing Wu ◽  
Hao Xu ◽  
Yongsheng Zhang ◽  
Yuan Tian ◽  
Xiuguang Song
Keyword(s):  
Real Time ◽  
Queue Length ◽  
Ground Truth ◽  
Lidar Data ◽  
Ground Truth Data ◽  
The Road ◽  
Detailed Method ◽  
Important Input ◽  
Novel Method ◽  
On The Road

Real-time queue length information is an important input for many traffic applications. This paper presents a novel method for real-time queue length detection with roadside LiDAR data. Vehicles on the road were continuously tracked with the LiDAR data processing procedures (including background filtering, point clustering, object classification, lane identification and object association). A detailed method to identify the vehicle at the end of the queue considering the occlusion issue and package loss issue was documented in this study. The proposed method can provide real-time queue length information. The performance of the proposed queue length detection method was evaluated with the ground-truth data collected from three sites in Reno, Nevada. Results show the proposed method can achieve an average of 98% accuracy at the six investigated sites. The errors in the queue length detection were also diagnosed.

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