Ch 12 Journal Case Study: Big Data And The Internet O 457252

Ch 12 Journal Case Study Big Data And The Internet Of Things Drive Pre

Ch 12 Journal Case study Big Data and the Internet of Things Drive Precision Agriculture By 2050, the world will be populated with an estimated 9 million people, and in order to feed all of them, agricultural output will need to double. Information technology, in the form of the Internet of Things (IoT), wireless and mobile technologies, and automated data collection and analysis is likely to provide part of the solution to this problem. Purdue University’s College of Agriculture is one of the organizations leading the way toward more data-driven farming. The College has developed an agriculture-oriented network with advanced IoT sensors and devices that will allow researchers to study and improve plant growth and food production processes.

According to Pat Smoker, director of Purdue Agriculture IT, in West Lafayette, Indiana, every process from farm to table has potential for improvement through better use of information technology. Purdue College of Agriculture partnered with Hewlett Packard Enterprise (HPE) on a digital agriculture initiative. In fall 2016, the university began installing an Internet of Things (IoT) network on its 1,408-acre research farm, the Agronomy Center for Research and Education (ACRE). The system captures terabytes of data daily from sensors, cameras, and human inputs. To collect, aggregate, process, and transmit such large volumes of data back to Purdue’s HPE supercomputer, the university is deploying a combination of wireless and edge computing technologies (see Chapters 5 and 7).

They include solar-powered mobile Wi-Fi hotspots, an adaptive weather tower providing high-speed connectivity across the entire ACRE facility, and the PhenoRover, a semi-automated mobile vehicle that roams throughout ACRE research plots capturing real-time data from plant-based sensors. Purdue is also experimenting with drones for plant-growth data collection. ACRE researchers can enter data into a mobile device on-site and transmit them via the wireless network to an HPE data center for analysis. Previously, Purdue’s faculty had to figure out how to transmit data from the sensors back to the lab, and assign someone to write the software for analyzing the data. The new system is faster and responsive.

For example, researchers using mobile devices in the field can transmit data about seed growth back to ACRE labs to analyze the impact of water levels, fertilizer quantities, and soil types. The labs can then communicate the results of their analysis back to the field to allow quick adjustments. Computerized instructions control how planting and spraying machines apply seed and nutrients to a field. The Purdue project is an example of “precision agriculture,” in which data collected and analyzed with digital tools drive decisions about fertilizer levels, planting depth, and irrigation requirements for small sections of fields or individual plants, and automated equipment can apply the ideal treatment for specific weeds.

Large agricultural companies like Monsanto and DuPont are big precision agriculture players, providing computerized data analysis and planting recommendations to farmers who use their seeds, fertilizers, and herbicides. The farmer provides data on his or her farm’s field boundaries, historic crop yields, and soil conditions to these companies or another agricultural data analysis company, which analyzes the data along with other data it has collected about seed performance, weather conditions, and soil types in different areas. The company doing the data analysis then sends a computer file with recommendations back to the farmer, who uploads the data into computerized planting equipment and follows the recommendations as it plants fields.

For example, the recommendations might tell an Iowa corn farmer to lower the number of seeds planted per acre or to plant more seeds per acre in specified portions of the field capable of growing more corn. The farmer might also receive advice on the exact type of seed to plant in different areas and how much fertilizer to apply. In addition to producing higher crop yields, farmers using fertilizer, water, and energy to run equipment more precisely are less wasteful, and this also promotes the health of the planet. Sources: “Envision: The Big Idea,” , accessed April 26, 2018; “Precision Agriculture,” , accessed April 26, 2018; , accessed May 1, 2018; and Eileen McCooey, “Purdue Uses IoT to Reinvent Farming, Boost Output,” Baseline, December 6, 2017.

Paper For Above instruction

Introduction

The integration of big data and Internet of Things (IoT) technologies into agriculture marks a transformative shift toward precision farming. Purdue University exemplifies this trend through its innovative IoT-driven research farm, aiming to enhance crop yields and resource efficiency amidst global food security challenges projected for 2050. This paper explores the application of big data and IoT in agriculture, analyzing the benefits, challenges, and future prospects, with a focus on Purdue's initiatives and their implications for sustainable food production.

Overview of Purdue’s IoT-Driven Agriculture Initiative

Purdue University's Agronomy Center for Research and Education (ACRE) demonstrates a comprehensive deployment of IoT sensors, edge computing, and wireless technologies to collect vast data on plant growth conditions. This initiative includes solar-powered Wi-Fi hotspots, adaptive weather stations, and mobile data collection vehicles like PhenoRover, alongside experimental drone applications. The real-time data transmitted to supercomputers enables swift analysis, allowing researchers to monitor and adjust agricultural practices dynamically, exemplifying the principles of precision agriculture.

Benefits of Big Data and IoT in Agriculture

The utilization of big data and IoT in agriculture offers numerous advantages:

• Enhanced crop monitoring and management through real-time data collection from sensors and drones
• Improved resource efficiency—optimized water, fertilizer, and pesticide application reduces waste
• Increased crop yields by tailoring interventions to specific field zones or individual plants
• Faster decision-making enabled by automated data transmission and analysis
• Potential for sustainable farming practices that minimize environmental impact

Purdue’s approach exemplifies how integrating digital tools into farming enhances productivity and sustainability, aligning with global food security goals.

Challenges and Risks of Implementing IoT and Big Data in Agriculture

Despite the promising benefits, several challenges complicate the widespread adoption of these technologies:

• Technological Infrastructure: Developing reliable and affordable connectivity infrastructure in rural areas is difficult, especially in emerging markets.
• Data Security and Privacy: With vast amounts of sensitive data being collected, cybersecurity threats and privacy concerns are paramount.
• High Initial Investment: The cost of IoT devices, sensors, edge computing, and data centers can be prohibitive, particularly for smallholder farmers.
• Technical Expertise: Effective use requires specialized skills for data analysis and maintenance, which may be scarce in developing regions.
• Environmental and Political Factors: Variability in environmental conditions and political stability influence the adoption and sustainability of IoT projects.

Addressing these challenges necessitates strategic planning, investment, and capacity building to ensure equitable benefits from technological advancements.

Future Outlook and Recommendations

Looking ahead, the continued development of IoT and big data analytics promises to revolutionize agriculture globally. Technologies are expected to become more affordable, scalable, and accessible, even in emerging markets. To capitalize on this potential, recommendations include:

• Promoting public-private partnerships to subsidize infrastructure and technology costs in developing regions
• Developing culturally and environmentally tailored solutions to ensure local relevance and acceptance
• Enhancing cybersecurity frameworks to protect farmers’ data integrity and privacy
• Investing in education and skills training programs to build local technical capacity
• Supporting policies that facilitate innovation while safeguarding the interests of smallholder farmers

If these strategies are implemented, IoT-driven precision agriculture could significantly boost food production, improve resource management, and foster sustainable development over the next five years and beyond.

Conclusion

Purdue University’s IoT and big data initiatives exemplify the transformative potential of digital technology in agriculture. While challenges remain—such as infrastructure, security, and cost barriers—the benefits for productivity, sustainability, and environmental health are substantial. For emerging markets like Peru and Colombia, adopting tailored IoT solutions could address local agricultural challenges, fostering resilience and growth. Strategic investments and capacity building will be critical to realizing this vision and ensuring equitable benefits in global food security efforts.

References

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