Soil color sensor data collection using a GPS-enabled smartphone application

Stiglitz, Roxanne; Mikhailova, Elena A.; Post, Christopher J.; Schlautman, Mark A.; Sharp, Julia L.; Pargas, Roy P.; Glover, Benjamin J.; Mooney, Jack

doi:10.1016/j.geoderma.2017.02.018

Cited by 36 publications

(21 citation statements)

References 24 publications

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“…Recently, there has been renewed interest in creation of soil test kits optimized for agronomical field use as a result of increasing access to technology such as portable sensors (Piikki et al., 2016). Key to this is the rising ubiquity of smartphones, which are being increasingly used in environmental management applications (Aitkenhead, Donnelly, Coull, & Hastings, 2014) and soil science (Aitkenhead et al., 2015; Delgado, Kowalski, & Tebbe, 2013; Stiglitz et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

Novel procedure for testing of soil field test kits involving paper strips

Golicz

Hallett

Sakrabani

2020

Soil Use and Management

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The need for facilitation of access to soil information has never been greater. Growing human population, shrinking land and water resources, soil pollution, climate change and unequal distribution of agriculture-oriented technology impact negatively on global food security. There has been a longstanding interest in developing low-cost and easily accessible soil field kits to measure different properties of agricultural soils in order to improve their agronomic capacity. Test strips, in particular, have provided a favoured method of obtaining soil nutrient status information since the 1970s. Today there is renewed interest in using semi-qualitative colorimetric methods in soil assessment due to incorporation of modern technological solutions, such as smartphones, which could in turn increase the accuracy and precision of the existing methods. In this paper, we propose streamlined testing procedures based on experience gathered that may be conducted prior to a field kit development involving test strips. Results from laboratory and field experiments are presented, highlighting important factors which ought to be taken into account at the commencement of test-strip oriented studies.

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Section: Introductionmentioning

confidence: 99%

Novel procedure for testing of soil field test kits involving paper strips

Golicz

Hallett

Sakrabani

2020

Soil Use and Management

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“…By comparison, images captured by drones are more realistic and clearer with higher resolution than satellite images (Khaliq et al, 2019). In addition, most of the currently available mobile apps focus on one specific management aspect, such as pest and disease management (Johannes et al, 2017), irrigation system (Kodali & Sarjerao, 2017) and soil study (Stiglitz et al, 2017). In contrast, Padi2U covers all the management aspects in paddy cultivation, including planting schedule, pest management, disease management, weed management, weather forecast, farmer report, and yield harvest information.…”

Section: Padi2u Mobile Applicationmentioning

confidence: 99%

Smartphone Application Development for Rice Field Management Through Aerial Imagery and Normalised Difference Vegetation Index (NDVI) Analysis

Roslin¹,

Che’Ya²,

Rosle³

et al. 2021

JST

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In the current practices, farmers typically rely on the traditional method paper-based for farming data records, which leads to human error. However, the paper-based system can be improved by the mobile app technology to ease the farmers acquiring farm data as all of the farm information will be stored in digital form. This study aimed to develop a smartphone agricultural management app known as Padi2U and implement User Acceptance Test (UAT) for end-users. Padi2U was developed using Master App Builder software and integration with the multispectral imagery. Padi2U provides recommendations based on the Department of Agriculture’s (DOA), such as rice check, pest and disease control, and weed management. Through the Padi2U, farmers can access the field data to understand the crop health status online using the Normalised Difference Vegetation Index (NDVI) map derived from the multispectral images. The NDVI is correlated to the Soil Plant Analysis Development (SPAD) value, corresponding to R² = 0.4012. UAT results showed a 100 percent satisfaction score with suggestions were given to enhance the Padi2U performance. It shows that Padi2U can be improved to help farmers in the field monitoring virtually by integrating multispectral imagery and information from the field.

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“…Stiglitz et al (2017a) used the Nix Pro to predict soil organic carbon using soil color sensor data. Stiglitz et al (2017b) developed a mobile application that connects to the Nix Pro and allows users to collect soil color data, while also recording additional information (sample photo, dry/moist, field/laboratory, and GPS location). Collected information is uploaded to a cloud storage system (Stiglitz et al, 2017b).…”

Section: The Lori Model Rubric (Adapted From Leacock and Nesbit 2007)mentioning

confidence: 99%

Teaching Sensor Technology and Crowdsourcing with Reusable Learning Objects

Mikhailova¹,

Stiglitz²,

Post³

et al. 2018

Natural Sciences Education

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Core Ideas Reusable learning objects are self‐contained digital modules. Reusable learning objects were developed for teaching soil color determination using a color sensor application and crowdsourcing. Reusable learning objects were effective teaching tools. Students positively described their perception of the sensor‐based method of soil testing. Reusable learning objects (RLOs) are self‐contained digital modules commonly utilized in e‐learning. The purpose of this study is to develop RLOs for teaching soil color determination using a color sensor application: soil color, Soil Scanner application, global positioning system (GPS) location, color conversion, and crowdsourcing. Each RLO is a self‐contained learning unit with specific learning goals, educational materials, quiz, and assessment. Navigation of each object is controlled by the participant via tabs to allow the user to control the pace of the RLO. The quality of each RLO is assessed by a learning object review instrument (LORI) framework rubric. Online quizzes at the end of each RLO are used to examine the learning outcomes. This study also used the web‐based survey tool Qualtrics before and after the laboratory‐based activity to systematically measure various constructs including familiarity with sensors, crowdsourcing, and perception of the sensor‐based method of soil testing. Reusable learning objects were effective teaching tools as demonstrated by excellent scores (A) received by the students for all RLO quizzes. Each RLO scored well for each category of the LORI model framework assessment. Additional comments suggest that students were receptive to the RLOs as a learning tool. Students positively described their perception of the sensor‐based method of soil testing compared with the traditional method, a Munsell color chart.

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Soil color sensor data collection using a GPS-enabled smartphone application

Cited by 36 publications

References 24 publications

Novel procedure for testing of soil field test kits involving paper strips

Novel procedure for testing of soil field test kits involving paper strips

Smartphone Application Development for Rice Field Management Through Aerial Imagery and Normalised Difference Vegetation Index (NDVI) Analysis

Teaching Sensor Technology and Crowdsourcing with Reusable Learning Objects

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