Mobile Data Collection Transforms Agriculture: Industry Case Studies

How Does Mobile Data Collection Work?

Mobile data collection is a technology for capturing, storing, and managing data in real-time using mobile devices (mobile phones and tablets).

In agriculture, mobile data collection is used to track crop health, monitor weather conditions, and manage irrigation systems by recording real-time data from the field.

Farmers can make more informed decisions on planting, harvesting, and resource management, improving efficiency and sustainability.

Here is a step-by-step breakdown of how mobile data collection works, along with some of the tools used at each stage of the process:

1. Designing Flexible and Dynamic Digital Forms

Survey authoring tools allow agricultural research organizations to create customized digital forms capable of handling various data types, including text, GPS coordinates, barcodes, images, and voice recordings.

These tools assist in designing, testing, and deploying digital forms efficiently.

Some mobile tools used in form design include:

1. XLSForms – Excel-based form design standard used by ODK, KoboToolbox, and CommCare.
2. ODK Build – Drag-and-drop form designer for ODK users.
3. Kobo Form Builder – Simplified visual form creator for KoboToolbox.
4. SurveyCTO Designer – Enterprise-grade form-building with conditional logic.
5. Google Forms – Basic web-based form creation tool.
6. Magpi Forms – Mobile-friendly form designer for surveys and data collection.

Mobile data collection forms capture and transmit data in real-time, improving efficiency and accuracy. Picture/Courtesy

2. Data Collection on Mobile Devices

Field agents, agricultural extension officers, and researchers use mobile devices to collect data in the field.

Many tools support barcode scanning, GPS tracking, image capture, voice-to-text input, digital signatures, and many other functionalities.

Mobile data collection tools can also capture data offline, store it locally, and synchronize it with a server once an internet connection is available.

Some of the tools used to collect data using mobile devices include:

1. ODK Collect – Open-source mobile app for mobile data collection using ODK. ODK Collect can collect data offline.
2. KoboCollect – Mobile app for data collection using KoboToolbox.
3. SurveyCTO Collect – Secure, offline-first data collection platform.
4. Survey123 by Esri – GIS-enabled app for geospatial data collection.
5. CommCare – Mobile case management tool used in health and humanitarian projects.
6. TaroWorks – Salesforce-integrated data collection tool.
7. Magpi+ – Supports multimedia data input.
8. Dimagi CommCare – Supports longitudinal tracking and case management.

3. Offline Data Collection: Capturing Data Without Internet Access

A major advantage of modern data collection tools is their ability to store data offline and automatically sync it once an internet connection is restored.

Some of the mobile tools for offline data collection include:

1. ODK Collect – Stores responses offline and syncs later to a cloud server.
2. KoboCollect – Stores responses offline and syncs later to a cloud server.
3. Magpi+ – Designed for low-bandwidth regions.
4. TaroWorks – Works offline and syncs with Salesforce CRM.
5. CommCare – Enables offline case tracking for healthcare and NGOs.
6. Surveys on Tab – Offline-first solution for market and corporate surveys.

In agriculture, mobile data collection is used to track crop health, monitor weather conditions, and manage irrigation systems by recording real-time data from the field. Picture/Courtesy

4. Data Synchronization: Uploading & Syncing Data to the Cloud

When an internet connection is restored, mobile data collection tools automatically sync the gathered data to a centralized cloud or on-premise servers for processing.

Some of the mobile tools for data synchronization include:

1. ODK Central – Store and manage synchronized data.
2. ODK Aggregate – Store and manage synchronized data.
3. KoboToolbox Servers – Store and manage synchronized data.
4. Google Sheets API – Auto-syncs data to spreadsheets for easy access.
5. RedCap – Secure data management for research and healthcare projects.
6. ODK Cloud, Microsoft Azure, AWS S3, Digital Ocean, Google Cloud – Secure cloud-based data storage solutions.

5. Data Management: Cleaning, Processing, and Storing Data

Once collected, the data needs to be cleaned, processed, and organized to facilitate analysis and informed decision-making.

Some of the tools for data management include:

1. Microsoft Excel & Google Sheets – Used for basic data cleaning and filtering.
2. Power BI – Dashboard creation and business intelligence reporting.
3. Python & R – Advanced data processing and analytics.
4. Tableau – Data visualization and storytelling.
5. QGIS – GIS software for geospatial data mapping.
6. Google BigQuery – Cloud-based big data warehousing.

A farmer in protective clothing uses a tablet to monitor production and food supply at a poultry farm. Picture/Courtesy

6. Reporting & Action: Analyzing Data for Decision-Making

Once the data is collected and processed, organizations create reports, visualize insights, and inform decision-making processes.

Some of the tools for reporting & action include:

1. Microsoft Power BI – Interactive data visualization and reporting.
2. Google Data Studio – Cloud-based business intelligence platform.
3. Tableau – Advanced analytics and visualization.
4. SPSS & Stata – Statistical tools for complex data analysis.
5. QGIS – GIS mapping and spatial analytics.
6. Looker (Google Cloud) – Data-driven business intelligence platform.

While mobile data collection can take time to master, expert training greatly speeds up the learning process.

In-person training, consultancy, or self-paced mobile data collection courses significantly enhance the capacity of agricultural organizations to collect data through digital tools and ensure real-time access to actionable insights.

In agriculture, mobile data collection is used to track crop health, monitor weather conditions, and manage irrigation systems by recording real-time data from the field. Picture/Courtesy

Case Studies on the Global Impact of Mobile Data Collection in Agriculture

The following is a list of projects and case studies where agricultural organizations successfully adopted mobile tools and other modern modes of data collection.

These case studies will provide insights into the challenges faced by different entities before the adoption of modern methods, the benefits after adoption, additional details about the project’s scope, and the impact on beneficiaries.

1. KoBoToolBox in the Philippines’ Department of Agrarian Reform Project

Farmland ownership in the rural Philippines has been a complex and contentious issue for decades, with various policies and problems contributing to the economic crisis.

The country’s arable areas have been historically controlled by a few wealthy landowners, leading to serious economic inequality, particularly for the peasant farmers.

The Support to Parcelization of Lands for Individual Titling (SPLIT) project, is a major initiative by the Department of Agrarian Reform (DAR) to help sort out the land ownership problems in the Philippines.

Project Details

1. The Philippines Department of Agrarian Reform adopted KoBoToolBox as a mobile data collection tool to support the SPLIT project.
2. Having started in 2022, the project was expected to conclude the distribution of land with title deeds exercise by 2024.
3. SPLIT actively aids government initiatives by accelerating the subdivision process of existing collective land titles.
4. The project is, therefore, expected to resolve centuries of landlessness, improving food security for thousands of Filipino farmers and driving economic growth.

Challenges Before the Adoption of the KoBoToolBox Data Collection Tool

1. The manual collection of land information was time-consuming and required significant resources.
2. Many rural areas in the Philippines are difficult to access, making it challenging for officials to visit these regions frequently for data collection.
3. Paper data records were prone to mismanagement, loss, or damage.

Ifugao Province in the Philippines is famous for the Ifugao Rice Terraces, a UNESCO World Heritage Site. These terraces were built by indigenous people over 2,000 years ago. Picture/Courtesy

Key Benefits of Mobile Data Collection in the Department of Agrarian Reform Project

1. With mobile devices, data could be captured on-site and transmitted directly to centralized systems, drastically reducing delays in processing.
2. KoBoToolBox’s offline data collection capabilities allowed teams to collect information in remote areas without worrying about connectivity issues.
3. KoBoToolBox helped reduce data-entry errors by automating parts of the process and integrating validation checks.
4. With COVID-19 restrictions in place, the adoption of digital tools allowed for safer interactions.
5. With over 1,500 land titles issued by early 2022 and plans for more, KoBoToolbox is enabling broader access to land ownership for rural communities, contributing to long-term rural development.

Source

How KoboToolbox supports equity, social justice, and rural development in the Philippines – KoBoToolBox.org

2. Open Data Kit (ODK) for Seed Yam Tracking in Ibadan, Nigeria

Ibadan is the capital and most populous city of Oyo State in Nigeria. Alongside other locations like Benue State, the fertile soil and favorable climate in Ibadan and its surrounding areas make it ideal for growing yams.

This makes Nigeria the largest yam producer in the world, contributing to two-thirds of global yam production each year, and approximately half of all Nigerian households consume the crop regularly.

Seed yam is a crucial input for yam farming, and its quality directly impacts crop yield.

There was a need for a more efficient system to track seed yam quality, location, and distribution to improve overall yam farming productivity.

Project Details

1. Open Data Kit (ODK) was introduced in 2018 by a collaboration between agricultural researchers, extension workers, and local farmers in the Ibadan region.
2. Historically, seed yam data in Nigeria were primarily collected using field notebooks, which were later transferred into Microsoft Excel spreadsheets or Access databases for analysis and reporting.
3. ODK replaced the traditional paper-based methods because of its versatility, offline data collection capability, and cost-effectiveness.

Challenges Before the Adoption of ODK For Seed Yam Tracking in Nigeria

1. Data collection on seed yam quality was time-consuming and prone to human errors, which led to inaccurate or incomplete data.
2. The manual process of data collection and feeding of databases caused delays in making timely decisions.
3. Many small-scale farmers did not maintain proper records of their seed yam sources or quality, leading to inconsistencies in the quality of yam planted and difficulty tracking seed origins.
4. Without real-time data, it was difficult for agricultural extension workers and researchers to monitor and respond to seed yam quality problems.

Yams on sale at a local Nigerian market. Quality seed yam distribution has solidified Nigeria’s leading position in the global production of the tuber crop. Picture/Courtesy

Benefits After the Adoption of ODK

Yam farmers, agricultural extension workers, and other stakeholders benefitted from mobile ODK data collection as follows:

1. Farmers, distributors, and extension workers could now collect and access data on seed yam quality, location, and distribution in real-time.
2. ODK’s mobile forms reduced the errors associated with manual data entry.
3. Extension workers and researchers could monitor seed yam conditions at different stages of the supply chain, identifying problems early and offering timely advice to farmers.
4. With better-quality seed yams being distributed and monitored, farmers were able to plant healthier crops, leading to improved yields.

Source

Open data kit (ODK) in crop farming: mobile data collection for seed yam tracking in Ibadan, Nigeria

3. Use of Drones by CIMMYT in Southern Africa for Crop Data Collection

The International Maize and Wheat Improvement Center (CIMMYT) is a global research organization dedicated to improving the productivity, sustainability, and resilience of maize (also known as corn) and wheat production.

Founded in 1966, CIMMYT forms part of the Consultative Group on International Agricultural Research (CGIAR), a global outfit that does agricultural research and development.

Project Details

1. CIMMYT has effectively integrated drone technology (also known as Unmanned Aerial Vehicles – UAVs) into its maize breeding programs in Southern Africa.
2. CIMMYT introduced drones in 2013, and today, UAVs play a key role in maize breeding across regions including Eastern and Southern Africa, Latin America, and Asia.
3. Drones have helped streamline data collection, enhance accuracy, and reduce labor costs.
4. The adoption of drones marked a significant shift from traditional, manual data collection methods, improving the agricultural sector in the region.

Challenges Before the Integration of Drones in Agriculture Data Collection

1. Manual data collection for breeding trials, which involved checking and recording information from thousands of plants, was extremely time-consuming.
2. It was difficult to maintain the speed and accuracy needed to evaluate large numbers of plant varieties.
3. Manual methods had high labor costs and increased the risk of human error, which compromised the quality of the data.

Drones equipped with sensors collect data on crop health and soil conditions, which are analyzed via mobile devices for precision farming. Picture/Courtesy

Key Benefits of Drones in Agricultural Data Collection

1. Drones can collect data from up to 1,000 plots in just 10 minutes, a task that would take hours if executed manually.
2. Drones have significantly reduced labor costs making mass data collection a cost-effective exercise.
3. Drones help farmers improve crop productivity and resilience, ultimately benefiting food security in the region.
4. The technology empowers breeders to manage large-scale trials more efficiently, reducing workload and enabling them to focus on improving crop varieties for local conditions.
5. The ability to collect real-time data helps researchers optimize and effectively manage resources such as research plots, farm inputs, and manpower.

Source

Drone Technology is aiding data collection for crop breeding in Africa – FAO

4. Collecting Trial Data in Uganda’s National Bean Program Using ODK

Uganda’s National Bean Program is one of the country’s key agricultural initiatives, aimed at improving bean production.

Alongside bananas, beans are among the staple foods in Uganda, providing revenue for local small-scale farmers.

Farmers’ productivity was severely affected by crop diseases among other challenges, brought by limited data access.

Project Details

1. The Uganda National Bean Program adopted ODK in 2017 as a solution to streamline data collection, improve the accuracy of trial results, and make the data more accessible to researchers and stakeholders.
2. ODK played a key role in the development of climate-smart bean varieties that are resistant to multiple stresses and tolerant to drought.
3. ODK offered offline functionality (critical for rural areas with limited internet access), and flexibility in customizing forms for different types of agricultural trials.

Challenges Before Adoption

1. Field researchers were using paper forms to collect trial data from multiple trial sites. This approach was extremely slow and prone to human error, which led to inaccuracies.
2. There were inconsistencies in different data since different researchers collected data using different formats.
3. Researchers, farmers, and policymakers had limited access to trial progress or results, which hindered effective decision-making.

Bean farming is a vital part of global agriculture, providing a nutritious food source while also contributing to soil health through nitrogen fixation. Picture/Courtesy

Key Benefits of ODK to Ugandan Farmers, Extension Workers, and Policy Makers

1. Data could be synchronized in real-time or once the device was back online, significantly speeding up the process of data collection and analysis.
2. With ODK’s form validation features, researchers could ensure data was recorded consistently and accurately. For example, predefined answer choices and checks for valid entries reduced human errors.
3. The use of ODK facilitated better coordination between researchers, extension workers, and policymakers. Data from different trial sites could be integrated and accessed by all relevant stakeholders for collaborative efforts.

Source

e-Agriculture Promising Practice: Open Data Kit: a new field data collection tool for ban breeders and researchers – Food and Agriculture Organization (FAO)

Also read: What is ODK? Unlocking the Power of Open Data Kit in Healthcare Data Collection

5. Mobile Data Collection in Livestock Disease Control in Kenya – ILRI

The International Livestock Research Institute (ILRI) has long been at the forefront of tackling livestock health challenges in Africa.

Kenya’s Rift Valley Province has been a hotbed of livestock zoonotic diseases which is attributed to the high number of pastoralists in the region.

In Kenya, where livestock farming plays a crucial role in the economy and food security, diseases such as foot-and-mouth disease, brucellosis, and Rift Valley fever pose significant risks to both livestock and human health.

Project Details

1. The International Livestock Research Institute (ILRI) began the implementation of mobile data collection for livestock health management between 2013 and  2016.
2. In addition to tools like the Open Data Kit (ODK), ILRI developed more customized in-house mobile tools such as the LivHealth E-surveillance App.
3. The LivHealth E-surveillance App allows community disease reporters (CDRs) to easily record syndromic symptoms and reported diseases.
4. It facilitates the capture and documentation of both disease syndromes and confirmed cases reported by communities engaged in livestock farming.
5. ODK also assisted in livestock identification and traceability as partner agencies rushed to contain spreading animal infections.

Challenges Before the Adoption of Mobile Data Collection By ILRI in Kenya

1. Paper-based systems have been used for data collection and project monitoring and evaluation in rural areas. This process was slow, prone to errors, and inefficient.
2. Reporting of disease outbreaks and response measures was slow leading to huge losses.
3. Field agents and veterinarians had limited access to real-time data, which slowed down decision-making and intervention processes.

Key Benefits After ODK Adoption

Local pastoralist communities, extension officers, filed veterinary officers, and ILRI researchers benefited from mobile data collection in the following ways:

1. Farmers, particularly those with small to medium-scale livestock operations, benefited from faster response times to disease outbreaks.
2. The field agents who collected disease data benefited from a more efficient, user-friendly system.
3. ILRI’s research teams were able to access real-time data, enabling them to track disease trends and patterns more accurately.
4. Local authorities and country policymakers benefited from improved data on livestock disease patterns.

Data-led livestock disease control can prevent significant losses by enabling early detection and targeted interventions, improving animal health, and reducing deaths. Picture/Courtesy

Sources

1. ODK used by ILRI for livestock data collection – International Livestock Research Institute (ILRI)
2. Piloting livestock identification and traceability systems in pastoral production systems in eastern Africa – International Livestock Research Institute (ILRI)
3. ILRI digital applications, software, and tools – CGIAR research centers