Satellite Monitoring versus IoT Devices, Which is Better for Agriculture

Synopsis: 

This blog explores the synergistic benefits of IoT and satellite monitoring in today's agricultural landscape. While each technology offers unique advantages, their combined power can provide a comprehensive solution for optimizing farm management. Here, we delve deep into the benefits and challenges of both technologies to help you assess the optimal combination for your business. You will also uncover how Cropin leverages data from both these technologies to deliver data-driven insights for decision-making. 

Today, agriculture is far more challenging and competitive than ever. Climate change, unpredictable weather events, pests, diseases, and weeds create a dynamic and unpredictable environment for crop growth and production. Agri-businesses must prioritize effective monitoring and management to thrive in this complex landscape. This has fueled a convergence of agriculture and technology, fostering innovation in sustainability and farming methods.

Satellite monitoring, an essential component of the agtech toolkit, enables near real-time monitoring of agricultural operations. By tracking crop health, soil conditions, and weather patterns, satellite data provides valuable information for informed decision-making. The Internet of Things (IoT) is another key technology for agtech development and application. IoT-enabled devices and sensors provide real-time data, empowering stakeholders with actionable insights that can optimize production processes and mitigate risks. 

Choosing the right tools to help you 

There has always been debate on which technology is best and why. Understanding the distinct functions and applications of satellite monitoring and IoT-enabled devices is crucial for making informed choices. While each technology offers unique advantages, the optimal combination may vary depending on specific needs and objectives. Here, we analyze the benefits and drawbacks of both technologies and determine why neither gains the upper hand.

Key Considerations for Technology Selection:

• Scalability: Consider technologies that can adapt to your farm's size and complexity.
• Integration: Ensure seamless integration with your existing systems and workflows.
• Data Quality: Prioritize technologies that provide accurate and reliable data for informed decision-making.

What is Satellite Monitoring: The Eye in the Sky?

Satellites have long served as the world's eyes in the sky, providing a wealth of data for various applications. Satellite monitoring in agriculture refers to remote sensing using satellite imagery to collate information about crops and cropland. They allow us to quickly observe large regions of agricultural land worldwide and capture images and information at an unprecedented scale. Improved accuracy and economy of scale make them indispensable in monitoring and managing agrarian production. 

Satellite monitoring helps to detect problems before they escalate into crises and enable proactive mitigation. The International Production Assessment Division of the United States Department of Agriculture relies heavily on satellite data for global agricultural intelligence. Their publications, including the World Agricultural Production Circulars, Commodity Intelligence Reports, Percent of Average Seasonal Greenness Reports, Standardized Precipitation Index, and Tropical Cyclone Monitor, all rely on satellite data.

The Scope of Earth Observation Satellites in Smart Farming

Sown area estimation: Remote sensing accurately estimates the acreage for informed decision-making.

Pest and weed identification: Remote sensing and spatial analysis provide information about weed and pest distribution over a large area at a relatively low cost, enabling mitigation efforts.

Crop health estimation: The Normalized Difference Vegetation Index is among the many indices used with satellite imagery to estimate crop biomass and health efficiently without manual intervention. 

Weather data: Satellite monitoring provides timely meteorological information and advanced weather forecasting about critical conditions affecting crops.

Soil mapping: Enables the choice of the crop varieties best suited to the land’s current and future production capacity.

Sustainable agriculture: Data-driven insights enable the optimization of input resource usage and facilitate sustainable agriculture.

Natural catastrophe modeling: The system predicts the outcome and behavior of natural hazards with GIS (Geographic Information System) data, scientific and historical studies, and computer-simulated catastrophe models.

Challenges in Satellite Monitoring

Satellite monitoring, while powerful, presents specific challenges. Separating satellite signals from noise to recover information and accurate instrument calibration are ongoing concerns. Satellite coverage limitations exist, with low-earth-orbiting satellites offering frequent but partial image coverage with greater spatial or temporal variability and high-orbit satellites providing broader coverage but lower spatial resolution. The sheer volume of satellite data can be overwhelming, often requiring extensive sifting to extract meaningful crop-specific information. Developing deep learning artificial intelligence (AI)/Machine Learning (ML) algorithms is essential to enhance data quality and ensure its continuous value. This is critical to harness the full potential of satellite data for improved decision-making and sustainable agricultural practices.

What is the Internet of Things (IoT) in Agriculture? 

As connectivity improved, a wave of innovative agricultural observation tools emerged, capable of seamlessly communicating with one another. IoT in agriculture is a network connecting production tools, physical objects, and environmental factors, enabling farm management and monitoring. Once a nascent technology, IoT has rapidly gained prominence in the agricultural sector with wide adoption. IoT tools in agriculture, including remote sensors, drones, agribots, connected machines, computer imaging, and more, have become indispensable assets for modern agriculture. 

When combined with powerful analytical tools and machine learning models, these technologies enable precise and comprehensive crop monitoring and remote farm management. The economic potential of IoT is immense, with McKinsey predicting a global value between $5.5 trillion and $12.6 trillion by 2030. Agriculture is poised to benefit greatly from the predicted growth. The ability to monitor crops and manage farms remotely, coupled with data-driven insights, will drive efficiency, reduce costs, and improve overall productivity.

Role of IoT devices in monitoring and management

Weeding: Weeding robots scan through images stored in their database and detect dissimilarities with the crops sown to spray precise quantities of crop protection products.

Harvesting: Working 24/7, harvesting agri-bots uses a combination of image processing and robotic arms to determine the quality and maturity of crops to harvest them at the right time. 

Heavy-duty labor: Material-handling robots are used to effortlessly lift heavy materials and perform repetitive tasks like planting with high accuracy and appropriate spacing.

Sensors: Sensors installed at different corners of the farmland measure and collect data on various parameters and efficiently transfer the data to advanced data analysis software for insights.

Automated equipment: GPS (Global Positioning System) and smartphone-controlled tractors and farming equipment self-adjust and navigate to plow and harvest large farm tracts efficiently.

Drones: Equipped with infrared cameras and sensors and remotely controlled, drones effectively map farms, report variations, and spray crop protection products, reducing time and human errors.

Computer imaging: Images collected over many crop cycles from various digitized farming equipment are processed and compared to detect anomalies and limiting factors.

Challenges in IoT

A significant challenge to adopting the IoT in agriculture is poor communication infrastructure in remote regions, which hampers real-time access to information. Implementing or scaling up IoT devices can be expensive. Rented equipment could incur operator fees and other additional expenses. IoT devices can also collect an enormous amount of data that can be difficult to secure. IoT devices interact with and transfer data to other devices across public networks, which are open to data leaks and unauthorized access. 

Comparative Overview: Satellite Monitoring v/s IoT in Agriculture

Several factors make satellite monitoring a better alternative to IoT devices. Here are some:

Cropin's Data-Driven Solutions: Harnessing the Power of Both Earth Observation Satellites and IoT Devices

Cropin leverages earth observation satellite data to address a wide range of critical agricultural challenges. From pest identification and crop health monitoring to yield estimation, weather analysis, soil mapping, and natural catastrophe modeling, Cropin's data pipeline ensures that raw satellite data is meticulously processed and transformed into actionable information. The data obtained is cleaned, normalized, and contextualized and then shared with clients through a user-friendly REST API or Python library for further analysis by their internal expertise for meaningful insights.

Cropin Data Hub, a state-of-the-art platform, addresses the complexities of remote monitoring by incorporating advanced data frameworks. These frameworks enable tasks such as boundary detection of farm plots, segmentation of land use, and cloud-free satellite imagery processing. Cropin Cloud uses satellite images, which have increased granularity and frequency and provide easy accessibility to cloud-stored imagery. This empowers users to create a "single source of truth" for agricultural data, enabling comprehensive monitoring of field variations throughout the growing season.

New IoT applications are constantly being developed, presenting challenges for seamless integration with older software. Cropin Cloud enables cross-platform capability. Cropin Data Hub connects effortlessly with various agri-data sources such as IoT devices, on-the-field farm management apps, remote sensing satellites, mechanized farm equipment, and weather analysis solutions. It establishes a balance between hardware and software functions, facilitating the convergence of established and emerging technologies to arrive at robust solutions. It effectively manages a vast variety and volume of unstructured and structured data and enables consolidation for accurate analysis. With Cropin, you can leverage IoT-enabled devices to collate data for efficient monitoring and management. 

By overlaying data from satellite imaging, IoT devices, soil analysis, weather data, and other relevant sources, Cropin Intelligence equips agri-businesses with insights and predictive intelligence derived from over 22 Cropin contextual deep-learning AI (Artificial Intelligence) models. Cropin’s advanced contextualized AI/ML models provide insights for data-driven decision-making that improves per-acre yield in agriculture. It augments existing analytics with additional information for in-depth insights.

Conclusion

IoT and satellite monitoring, while distinct technologies, complement each other to provide a comprehensive solution for modern agriculture. IoT devices offer real-time insights into farm-level operations, while satellites fuel smart farming decisions at scale derived from granular data. Together, they provide a more holistic understanding of agricultural operations and enable the development of effective strategies to address emerging challenges. In addition, harnessing their combined strength can help conventional farming practices meet the increasing demands while engaging farmers for rational farm management, field mapping, crop monitoring, and more. Cropin has mastered the science of integrating data from satellite and IoT devices to provide micro and macro-level visibility and present an accurate picture in near real-time. The cloud platform serves as a source for validation, and the advanced algorithms offer reliable intelligence.