scholarly journals Design and Evaluation of a Crowdsourcing Precision Agriculture Mobile Application for Lambsquarters, Mission LQ

Agronomy ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1951
Author(s):  
Brianna B. Posadas ◽  
Mamatha Hanumappa ◽  
Kim Niewolny ◽  
Juan E. Gilbert
Keyword(s):  
Precision Agriculture ◽  
Mobile Application ◽  
Ground Truth ◽  
High Quality ◽  
Classification Rate ◽  
Human Centered Design ◽  
Ground Truth Data ◽  
System Usability Scale ◽  
Final Design ◽  
Design Protocol

Precision agriculture is highly dependent on the collection of high quality ground truth data to validate the algorithms used in prescription maps. However, the process of collecting ground truth data is labor-intensive and costly. One solution to increasing the collection of ground truth data is by recruiting citizen scientists through a crowdsourcing platform. In this study, a crowdsourcing platform application was built using a human-centered design process. The primary goals were to gauge users’ perceptions of the platform, evaluate how well the system satisfies their needs, and observe whether the classification rate of lambsquarters by the users would match that of an expert. Previous work demonstrated a need for ground truth data on lambsquarters in the D.C., Maryland, Virginia (DMV) area. Previous social interviews revealed users who would want a citizen science platform to expand their skills and give them access to educational resources. Using a human-centered design protocol, design iterations of a mobile application were created in Kinvey Studio. The application, Mission LQ, taught people how to classify certain characteristics of lambsquarters in the DMV and allowed them to submit ground truth data. The final design of Mission LQ received a median system usability scale (SUS) score of 80.13, which indicates a good design. The classification rate of lambsquarters was 72%, which is comparable to expert classification. This demonstrates that a crowdsourcing mobile application can be used to collect high quality ground truth data for use in precision agriculture.

scholarly journals Early Development and Design of Crowdsourcing Agriculture Mobile Application for Lambsquarters

2020 ◽  
Vol 23 (2) ◽  
Author(s):  
Brianna Posadas ◽  
Mamatha Hanumappa ◽  
Juan Gilbert
Keyword(s):  
Mobile Application ◽  
Ground Truth ◽  
Research Process ◽  
Average Score ◽  
User Centered Design ◽  
Application Development ◽  
Ground Truth Data ◽  
Qualitative Feedback ◽  
System Usability Scale ◽  
Made In

As big data has become increasingly necessary in modern farming techniques, the dependence on high quality and quantity of ground truth data has risen. Collecting ground truth data is one of the most labor-intensive aspects of the research process. A crowdsourcing platform application to aid lay people in completing ground truth data can improve the quality and quantity of data for growers and agricultural researchers. In this study, a user-centered design process was used to develop a prototype of a mobile application which will teach people how to classify certain characteristics of lambsquarters in the District of Columbia. Focus group results demonstrated that the greatest motivation for the participants was having opportunities to develop their skills and access to educational resources. From the focus groups, design personas were created and wireframe prototypes were produced. The prototypes were evaluated by users using the System Usability Scale and qualitative feedback. The design received an average score of 75.95, which indicates an acceptable design. From the feedback of the users, improvements to the design were made in the mobile application development of the system.

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 Crowdsourcing Image Analysis for Plant Phenomics to Generate Ground Truth Data for Machine Learning

2018 ◽  
Author(s):  
Naihui Zhou ◽  
Zachary D Siegel ◽  
Scott Zarecor ◽  
Nigel Lee ◽  
Darwin A Campbell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Best Practices ◽  
Ground Truth ◽  
Training Data ◽  
Quality Data ◽  
High Quality ◽  
Ground Truth Data ◽  
Plant Phenomics

AbstractThe accuracy of machine learning tasks critically depends on high quality ground truth data. Therefore, in many cases, producing good ground truth data typically involves trained professionals; however, this can be costly in time, effort, and money. Here we explore the use of crowdsourcing to generate a large number of training data of good quality. We explore an image analysis task involving the segmentation of corn tassels from images taken in a field setting. We investigate the accuracy, speed and other quality metrics when this task is performed by students for academic credit, Amazon MTurk workers, and Master Amazon MTurk workers. We conclude that the Amazon MTurk and Master Mturk workers perform significantly better than the for-credit students, but with no significant difference between the two MTurk worker types. Furthermore, the quality of the segmentation produced by Amazon MTurk workers rivals that of an expert worker. We provide best practices to assess the quality of ground truth data, and to compare data quality produced by different sources. We conclude that properly managed crowdsourcing can be used to establish large volumes of viable ground truth data at a low cost and high quality, especially in the context of high throughput plant phenotyping. We also provide several metrics for assessing the quality of the generated datasets.Author SummaryFood security is a growing global concern. Farmers, plant breeders, and geneticists are hastening to address the challenges presented to agriculture by climate change, dwindling arable land, and population growth. Scientists in the field of plant phenomics are using satellite and drone images to understand how crops respond to a changing environment and to combine genetics and environmental measures to maximize crop growth efficiency. However, the terabytes of image data require new computational methods to extract useful information. Machine learning algorithms are effective in recognizing select parts of images, butthey require high quality data curated by people to train them, a process that can be laborious and costly. We examined how well crowdsourcing works in providing training data for plant phenomics, specifically, segmenting a corn tassel – the male flower of the corn plant – from the often-cluttered images of a cornfield. We provided images to students, and to Amazon MTurkers, the latter being an on-demand workforce brokered by Amazon.com and paid on a task-by-task basis. We report on best practices in crowdsourcing image labeling for phenomics, and compare the different groups on measures such as fatigue and accuracy over time. We find that crowdsourcing is a good way of generating quality labeled data, rivaling that of experts.

scholarly journals Scalable community detection via parallel correlation clustering

2021 ◽  
Vol 14 (11) ◽  
pp. 2305-2313
Author(s):  
Jessica Shi ◽  
Laxman Dhulipala ◽  
David Eisenstat ◽  
Jakub Łăcki ◽  
Vahab Mirrokni
Keyword(s):  
Community Detection ◽  
Clustering Algorithms ◽  
Empirical Evaluation ◽  
Large Data ◽  
Ground Truth ◽  
Scalable Algorithms ◽  
High Quality ◽  
Ground Truth Data ◽  
Correlation Clustering ◽  
Trade Offs

Graph clustering and community detection are central problems in modern data mining. The increasing need for analyzing billion-scale data calls for faster and more scalable algorithms for these problems. There are certain trade-offs between the quality and speed of such clustering algorithms. In this paper, we design scalable algorithms that achieve high quality when evaluated based on ground truth. We develop a generalized sequential and shared-memory parallel framework based on the LAMBDACC objective (introduced by Veldt et al.), which encompasses modularity and correlation clustering. Our framework consists of highly-optimized implementations that scale to large data sets of billions of edges and that obtain high-quality clusters compared to ground-truth data, on both unweighted and weighted graphs. Our empirical evaluation shows that this framework improves the state-of-the-art trade-offs between speed and quality of scalable community detection. For example, on a 30-core machine with two-way hyper-threading, our implementations achieve orders of magnitude speedups over other correlation clustering baselines, and up to 28.44× speedups over our own sequential baselines while maintaining or improving quality.

scholarly journals From village to globe: A dynamic real-time map of African fields through PlantVillage

2019 ◽  
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

AbstractA 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, on the day it is collected and is linked to other data sources that will inform our understanding of crop stress, particularly in the context of climate change.

scholarly journals Citizen science, cells and CNNs – deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations

2020 ◽  
Author(s):  
Helen Spiers ◽  
Harry Songhurst ◽  
Luke Nightingale ◽  
Joost de Folter ◽  
Roger Hutchings ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Machine Learning ◽  
Nuclear Envelope ◽  
Citizen Science ◽  
Learning Algorithm ◽  
Automatic Segmentation ◽  
Ground Truth ◽  
Learning Approaches ◽  
High Quality ◽  
Ground Truth Data

AbstractAdvancements in volume electron microscopy mean it is now possible to generate thousands of serial images at nanometre resolution overnight, yet the gold standard approach for data analysis remains manual segmentation by an expert microscopist, resulting in a critical research bottleneck. Although some machine learning approaches exist in this domain, we remain far from realising the aspiration of a highly accurate, yet generic, automated analysis approach, with a major obstacle being lack of sufficient high-quality ground-truth data. To address this, we developed a novel citizen science project, Etch a Cell, to enable volunteers to manually segment the nuclear envelope of HeLa cells imaged with Serial Blockface SEM. We present our approach for aggregating multiple volunteer annotations to generate a high quality consensus segmentation, and demonstrate that data produced exclusively by volunteers can be used to train a highly accurate machine learning algorithm for automatic segmentation of the nuclear envelope, which we share here, in addition to our archived benchmark data.

scholarly journals Evaluasi Usability dan Perbaikan Desain Aplikasi Mobile Menggunakan Usability Testing dengan Pendekatan Human-Centered Design (HCD)

Rekayasa ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 130-143
Author(s):  
Doni Abdul Fatah
Keyword(s):  
Locus Of Control ◽  
Early Warning ◽  
Mobile Application ◽  
Average Score ◽  
Human Centered Design ◽  
Internal Locus Of Control ◽  
Internal Support ◽  
System Usability Scale ◽  
System Usability ◽  
Grade Scale

Aplikasi mobile Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) merupakan aplikasi yang memberikan peringatan dini cuaca. Informasi peringatan dini tersebut skalanya tidak hanya sebatas provinsi, tetapi hingga tingkat kecamatan. Namun ketika informasi yang ditampilkan dalam aplikasi masih kurang jelas untuk dipahami pengguna serta kemudahan dan kenyaman pengguna belum sesuai tujuan pembuatan aplikasi maka perlu dilakukan redesign pada aplikasi mobile BMKG tersebut dengan mengacu pada 8 rule panduan (Eight Golden Rules) untuk desain interaksi, serta pengujian menggunakan Kuesioner Usability SUS (System Usability Scale) yang berisi 10 pertanyaan terkait aplikasi dengan melakukan wawancara kepada responden terhadap apikasi tersebut, dari hasil yang didapatkan pada pengujian menggunakan prinsip Eight Golden Rules terdapat tiga point yang belum maksimal mulai dari Design dialogue to yield closure, Support internal locus of control, dan Support internal locus of control, kemudian pengujian menggunakan SUS yang dilakukan secara 2 kali, untuk yang pertama terhadap desain asli dari aplikasi mobile BMKG dengan skor rata-rata 60 menunjukan tingkat penerimaan pengguna Marginal Low, sedangkan grade skala masuk kategori D dan Adjective rating kategori OK, dari hasil dilakukan usulan desain perbaikan sesuai dengan masukan yang telah didapatkan dari para responden kemudian pengujian yang ke dua dengan metode Perhitungan SUS mendapatkan skor rata-rata 80,25 dapat disimpulkan tingkat penerimaan pengguna kategori Acceptable, pada Tingkat grade skala kategori B, serta untuk Adjective rating kategori Excellent, sehingga usulan desain aplikasi mobile BMKG dapat digunakan dengan mudah dan pengguna tidak merasa kebingungan terhadap desain hasil perbaikan untuk mendapatkan layanan informasi cuaca yang diberikan. Usability Evaluation and Improvement of Mobile Application Design Using Usability Testing with a Human-Centered Design (HCD) ApproachMeteorology, Climatology, and Geophysics (BMKG) mobile application is an application that provides weather early warning. The early warning information is not only limited to the province, but also to the sub-district level. But when the information displayed in the application is still not clear enough to be understood by the user and the ease and comfort of the user is not in accordance with the purpose of making the application, it is necessary to redesign the BMKG mobile application by referring to the 8 rule guidelines (Eight Golden Rules) for interaction design, as well as testing using SUS Usability Questionnaire (System Usability Scale) which contains 10 questions related to the application by conducting interviews with respondents of the application, from the results obtained in testing using the principles of the Eight Golden Rules there are three points that have not been maximal starting from Design dialogue to yield closure, Internal Support locus of control, and internal support locus of control, then testing using SUS is done twice, for the first to the original design of the BMKG mobile application with an average score of 60 shows the level of acceptance of Marginal Low users, while the grade scale is in the D category d an Adjective rating category OK, from the results of the proposed improvement design in accordance with the input that has been obtained from respondents then the second test with the SUS Calculation method to get an average score of 80.25 can be concluded on the acceptability ranges included in Acceptable, on the grade scale get a value of B, and for Adjective Rating included in Excellent, so that the proposed BMKG mobile application design can be used easily and users do not feel confused about the design of the results of improvements to get weather information services provided.

Usability evaluation of a mHealth application to support patient journey at pediatric emergency department (Preprint)

2020 ◽  
Author(s):  
Jessica Rochat ◽  
Frédéric Ehrler ◽  
Arnaud Ricci ◽  
Victor Garretas Ruiz ◽  
Christian Lovis
Keyword(s):  
Emergency Department ◽  
Interaction Design ◽  
Mobile Application ◽  
Pediatric Emergency ◽  
Pediatric Emergency Department ◽  
Patient Centered ◽  
Patient Journey ◽  
Care Processes ◽  
System Usability Scale ◽  
Centered Care

BACKGROUND Patient experience at pediatric emergency department (PED) remain suboptimal. As an attempt to support the patients and their families before, during and after visit at PED, we have developed InfoKids, a mobile application guided by the patient centered care principle. OBJECTIVE The objective of this study is to assess the usability of the Infokids mobile application. METHODS The app was assessed through an in lab evaluation were participants had to execute 7 tasks of a scenario leading them from the installation of the app till the reception of a diagnostic sheet linked to the care episode. All interactions were recorded and usability flaws were analyzed in regards with usability criteria. A system usability scale questionnaire was also filled by the participant to compare our system with other. RESULTS A total of 17 parents, 15 women and 2 men (ages 26-53) participated in the study. Overall, they were mostly satisfied with the navigation, layout and interaction design of the app. Most of the problems encountered were related with navigation, especially difficulties for some participants to find the location of the action to perform. CONCLUSIONS empowering patient through mobile application supporting care processes has the potential to improve both care efficiency and to release pressure on healthcare system. The success of these applications is however linked to an optimal user experience that can be improved through usability testing.

scholarly journals Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network

2019 ◽  
Vol 11 (10) ◽  
pp. 1157 ◽  
Author(s):  
Jorge Fuentes-Pacheco ◽  
Juan Torres-Olivares ◽  
Edgar Roman-Rangel ◽  
Salvador Cervantes ◽  
Porfirio Juarez-Lopez ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Image Data ◽  
Ground Truth ◽  
Aerial Images ◽  
Aerial Image ◽  
Data Set ◽  
Visual Appearance ◽  
Aerial Robots ◽  
Lighting Conditions ◽  
Convolutional Encoder

Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.

scholarly journals Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

2021 ◽  
Vol 13 (10) ◽  
pp. 1966
Author(s):  
Christopher W Smith ◽  
Santosh K Panda ◽  
Uma S Bhatt ◽  
Franz J Meyer ◽  
Anushree Badola ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Ground Truth ◽  
Burn Severity ◽  
Classification Methods ◽  
Spectral Indices ◽  
Ground Truth Data ◽  
Burn Scar ◽  
Interior Alaska ◽  
Remote Sensing Methods ◽  
The Relationship

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.

Sign in / Sign up

Export Citation Format

Share Document