Smart Farming vs. Precision Agriculture: Differences, Benefits, and AI-driven Innovations

Synopsis: This blog explores the similarities and differences between precision agriculture and smart farming, as well as the benefits and challenges of both. It also uncovers how AI is transforming these technologies and future trends.

In today’s agricultural landscape, technology is no longer a luxury but a critical component across all stakeholders, including farmers, food processors, commodity traders, retailers, insurers, lenders, and agronomists. From drones to satellite imagery and advanced sensors, the industry is undergoing a digital transformation that is reshaping how we farm, process, and distribute food.  However, the evolution of digital technology has resulted in the emergence of new concepts, including precision agriculture, smart farming, and digital farming. While often used interchangeably, these concepts have distinct nuances that define their unique approaches to modern agriculture.

In this blog post, we will explore two closely related concepts: precision agriculture and smart farming.  We will explore their common ground as digital farming methodologies and uncover the subtle differences that set them apart.

What is the Difference Between Smart Farming and Precision Agriculture

Smart farming is a comprehensive approach to agriculture that utilizes a diverse range of technologies. By integrating tools such as the Internet of Things, artificial intelligence, and automation, it creates a sophisticated and interconnected farming ecosystem. Precision agriculture is a subset of smart farming. This farm management approach uses digital techniques with a specific focus on monitoring and optimizing agricultural production processes. The key here is to optimize input resource management at the crop level, leveraging technologies like sensors and data analytics tools. It uses technology to collect data about the farm, such as soil conditions, weather patterns, and crop health, and then that data is used in the decision-making process to manage the farm. For example, as farms are heterogeneous, precision agriculture uses targeted fertilizer applications to specific areas of the field that need more nutrients and adjusts irrigation based on soil moisture levels. 

Smart farming encompasses a broader range of technologies than precision agriculture, extending beyond crop management to include livestock monitoring, supply chain optimization, and environmental impact assessments. While precision agriculture focuses specifically on optimizing crop-related activities, smart farming takes a more holistic approach, aiming to improve overall farm efficiency and sustainability.

Overview of Smart Farming

What is Smart Farming

Smart farming focuses on implementing data and information technologies to utilize human labor more effectively and enhance crop quality and quantity and overall farm management. Most farmers rely on learnings passed down through generations and approximate estimations to carry out seeding, applying fertilizers and crop protection products, and harvesting.  Smart farming improves these processes and enhances efficiency by leveraging agtech tools and software solutions. It helps producers make more informed, data-driven decisions and achieve economic efficiency by reducing workforce requirements.

Farm data is captured using mobile devices such as smartphones and tablets, enabling access to neat real-time data. Using data about the condition of soil and plants, terrain, climate, weather, resource usage, pests, manpower, bank loans, etc., decisions are made. As a result, farm operations thrive on data-driven decision-making as against intuition, improving predictability and efficiency. Smart farming technologies include IoT devices in agriculture, smart greenhouses, robots, drones, connected tractors, etc. It involves not just individual machines but overall farm operations.

Smart farming technologies transmit agricultural data through information and communication technologies (ICT)-based platforms. `Some of the components that ICT-based platforms use for smart farming are:

What are the Benefits of Smart Farming

Smart farming empowers farmers with improved efficiency and productivity, which are typical attributes of automation. Data-driven insights drive sustainable agriculture and risk mitigation based on evidence. By leveraging automation, precise control over environmental factors, and real-time data, farmers can optimize production costs and ensure sustainable agriculture.

Overview of Precision Agriculture

What is Precision Agriculture

As the name suggests, precision farming or precision agriculture involves highly controlled, accurate, and optimized agricultural production. It facilitates more efficient resource utilization, better yield, and reduced environmental impact, all at the same time. An ideal example of precision farming practice is a focused agrochemical application with AI-aided analysis that targets only areas that need attention instead of the blanket application.

Steps in Precision Agriculture

Technologies Used in Precision Agriculture

Precision agriculture is a farming practice that leverages modern information and communication technologies (ICT). It incorporates a combination of devices and machinery to capture vital field data, including Remote Sensors, Autonomous Vehicles, Automated Hardware and Software, GPS Soil Sampling, Telematics, Robotics, Drones, and Big Data Analytics.

Precision farming has existed since John Deere introduced GPS guidance for tractors in the 1900s. Thanks to rapid technological advancements in recent years, its inventions have evolved significantly. Precision agriculture optimizes input resource usage, reduces waste, increases per-acre yield, and improves environmental sustainability.

What are the Benefits of Precision Agriculture

Precision agriculture offers many benefits for farmers. By precisely targeting specific areas of land, farmers can optimize resource use, leading to increased yields, reduced costs, and improved environmental outcomes. This site-specific crop management (SSCM) approach enhances profitability and contributes to sustainable climate-smart agriculture.

Challenges in Adopting Precision Agriculture and Smart Farming

Precision agriculture and smart farming, while offering significant benefits, are not without their challenges. Here are some key concerns:

• High Initial Investment: Implementing these technologies often requires substantial upfront costs for hardware, software, and infrastructure.
• Technical Expertise: Operating and maintaining complex systems may demand specialized knowledge and skills that farmers may not possess.
• Interoperability: Ensuring compatibility between different hardware and software systems can be challenging.
• Data Integration: Integrating data from multiple sources is essential for deriving comprehensive insights, but it can be challenging due to varying formats and standards.
• Internet Connectivity: Reliable internet access is crucial for data transmission, especially in remote areas with limited connectivity.
• Data Security and Privacy: Protecting sensitive farm data from unauthorized access and ensuring data privacy is a top priority.
• Infrastructure Development: To support the adoption of technologies, investing in infrastructure upgrades, such as improved internet connectivity and power grids, may be necessary.
• Data analytics and management: Analyzing and managing large volumes of data effectively requires specialized tools and expensive expertise.

Smart Farming vs. Precision Agriculture: Key Differences

How Artificial Intelligence (AI) is Transforming Precision Agriculture and Smart Farming

As AI's global reach expands, agriculture is increasingly adopting it. Smart farming approaches frequently leverage AI for automation, advanced data analytics, and predictive modeling. Integrating AI into smart farming systems enhances the ability to make intelligent decisions.

Precision farming, a subset of smart farming, heavily utilizes AI. Precision farming optimizes crop-related operations by leveraging AI for big data analytics and data-driven decision-making. AI further improves the precision and efficiency of farmers' operations.

How is AI Used in Precision Farming?

In precision agriculture, AI analyzes vast amounts of data from various sources, such as sensors, drones, and satellites. AI-powered solutions are deployed for real-time monitoring and offer data-driven insights to create detailed crop maps, identify areas of variation, and optimize inputs like fertilizers, pesticides, and water. AI-powered algorithms can also predict crop yields, detect diseases and pests, and recommend optimal planting and harvesting times.

What is the Role of Machine Learning in Precision Agriculture?

Machine Learning (ML) algorithms are a subset of AI, where programs are trained to learn from data and make predictions even at an unsupervised level. In precision agriculture, machine learning models are used to analyze historical and real-time data along with multiple other datasets to identify patterns and predict future crop performance. Some examples of predictions made by ML models include Growing Degree Days (GDD), Evapotranspiration, Harvest Window Estimation, etc. These predictions help farmers make informed decisions about planting, fertilization, irrigation, and harvesting.

What is the Role of Robotics and Automation in Precision Agriculture?

Robotics and automation are transforming farming. AI-powered robots can perform tasks such as planting, weeding, and harvesting with greater precision and efficiency, adding significant value to optimizing labor resources. These robots can also collect data on crop health and soil conditions, which can be used to optimize farming practices. Additionally, automation can reduce labor costs and improve farms' overall productivity.

Future Trends in Smart Farming and Precision Agriculture

What is Digital Agriculture

Digital farming creates value from data. It means going beyond the presence and availability of data to develop actionable intelligence and meaningful added value from such data. Digital farming integrates the concepts of precision farming and smart farming. For farmers, digital agriculture allows them to increase their farm’s production, save costs in the long term, and eliminate risks. Many view digital agriculture as the future of the agricultural industry.

According to a paper on Digital Agriculture by DLG (German Agricultural Society), digital farming is understood to mean “consistent application of the methods of precision farming and smart farming, internal and external networking of the farm and use of web-based data platforms together with Big Data analyses.”

How can you use Cropin’s Solutions to advance your Agri-operations?

Here is a list of operations that growers can streamline using Cropin’s products.

Smart Farming

Explore the smart farming capabilities of Cropin Cloud

• Collect, process, and analyze agri-data from multiple sources for data-driven farming: Cropin’s digital agriculture platform uses satellite monitoring and weather data, information that field staff collects from farmers, and data from IoT devices, too. These data points are stored in the cloud for easy access anytime and anywhere. Cropin's farm management software, Cropin Grow, excels in this task, while Cropin Intelligence leverages advanced algorithms to analyze these data points and deliver actionable insights that guide decision-making processes for enterprises.

• Automate processes to reduce human effort, cut costs, and increase efficiency: One of the primary goals of smart farming is to achieve maximum utilization of human labor, and it does so by automating repetitive tasks. Cropin Grow allows users to configure agronomic advisories, activity plans, and the package of practices for each crop variety and set up notifications for field agents. It also automatically creates a backup of the data field that staff captures in the mobile app to eliminate paperwork, ease recovery, and increase productivity.

• Monitor operations remotely and ensure high productivity: Cropin uses satellite monitoring data from IoT devices to track plot performance continuously. The app Cropin Connect on the Cropin platform pushes alerts to field staff to guide farmers with any possible risk of pests, diseases, or unfavorable weather conditions. Accordingly, they can address these issues immediately to ensure quality yield and minimal losses.
• Increase transparency and accountability at all levels: Food companies and agribusinesses can also build customer brand reliability by deploying Cropin Grow and Cropin Trace. Field staff can record every farm activity in the mobile app to facilitate tracking of an end-product to, quite literally, the roots. With Cropin Trace, individuals can scan a given QR code to track the item’s origin, safety, and quality, thus facilitating complete transparency.

Precision Farming

• Get better control over crop production and lower production risks with pixel-level intelligence: Cropin Intelligence utilizes AI and satellite imagery analysis to carry out farm plot mapping and 360-degree monitoring. Enterprises can access near-real-time analytics on crop health at plot and regional levels throughout the crop cycle. At the same time, they can receive alerts about potential risks, if any. Such continuous invigilation can help farmers determine adverse issues beforehand, thus managing crop production more efficiently.

• Optimize resource usage to decrease wastage, reduce costs, and farm sustainably: The satellite monitoring by Cropin can also help farmers with advisories on optimizing the utilization of water, nutrients, and crop protection products. It can help them cut down on resource wastage and unnecessary costs and aid in better management. Additionally, agricultural companies can get weather predictions before seeding and harvesting to determine the perfect time for both.

• Reduce the environmental impact of agricultural activities: Sustainability and precision agriculture go hand in hand. Implementing digital solutions is only a way to further align with the eco-friendly narrative. Inch-perfect application of fertilizers, pesticides, and insecticides can prevent soil and water pollution due to chemical runoffs. Advisories on effective irrigation cycles and soil conservation techniques can also prevent the unnecessary depletion of these natural resources.

• Estimate yield in advance to make better agronomic and post-harvest decisions: Cropin Intelligence overlays historical cropping pattern data and periodical satellite images,  zone sampling done using its Smart Sampling onto its contextualized AI/ML models  to analyze crop health, crop growth progression, and yield. It enables farming companies to plan supply chain activities effectively, without any delay. It also helps agri-input companies develop efficient sales and marketing plans. 

Now that you are aware of the detailed differences between smart farming and precision agriculture make no mistake in focusing on individual objectives instead of confusing both for one. 

Cropin’s Agri-intelligence for Digital Farming

Cropin’s contextual deep-learning models overlay a host of data obtained from satellite imagery, multiple connected devices used in farming, and Cropin’s proprietary crop knowledge graph covering 10,000 crop varieties to derive actionable predictive insights for dynamic decision-making on and off the field. The over 25 AI models developed and field tested by Cropin provide insights into crop detection, yield estimation, irrigation scheduling, pest and disease prediction, nitrogen uptake, water stress detection, harvest date estimation, change detection, and plot score, among others, driving smart farming and precision agriculture.

This article has been updated to be more comprehensive in November 2024 by Haripriya Muralidharan.