This region, encompassing modern-day Iraq, Syria, Turkey, and parts of Iran, has been identified as the cradle of civilization. The Fertile Crescent’s fertile soil, abundant rainfall, and proximity to the Mediterranean Sea have made it a prime location for agriculture. The development of agriculture was a gradual process, with early farmers initially relying on simple tools and techniques.
This time, it’s driven by artificial intelligence (AI). It’s not just about automating tasks; it’s about fundamentally changing how we live, work, and interact with the world. The fourth industrial revolution, often referred to as Industry 4.0, is characterized by the convergence of physical and digital worlds.
Here, InformationWeek investigates the rise of IoT and AI in farming operations, with insights from Lisa Avvocato, vice president of global marketing for AI and computer vision data annotation company Sama, Mike Flaxman, vice president of product at big data analytics company HEAVY.AI, and Valeria Kogan, founder and CEO of Fermata, which offers AI solutions for monitoring plant health. (Editor’s note: This feature story covers AI’s contributions to agriculture. However, AI also competes with agriculture, for resources like land and water. These issues will be covered in more depth in upcoming InformationWeek sustainability coverage.) Meeting the Needs of a Growing Population
This rapid growth in population puts immense pressure on our planet’s resources, including water, land, and food. To address this challenge, the agricultural sector is undergoing a transformation, driven by the integration of technology, particularly in the form of AgTech. AgTech encompasses a wide range of technologies that aim to improve agricultural practices, increase productivity, and enhance sustainability. These technologies include precision agriculture, vertical farming, and robotics.
The global food system is facing a significant challenge: a growing demand for food coupled with declining production rates. This challenge is exacerbated by climate change, which is impacting agricultural yields. **Detailed Text:**
The global food system is grappling with a complex and multifaceted challenge: a burgeoning demand for food, coupled with a decline in production rates.
The use of AI in agriculture has been steadily increasing, with the adoption rate growing rapidly in recent years. **AI in Agriculture: A Comprehensive Overview**
Artificial intelligence (AI) is revolutionizing the agricultural sector, offering solutions to complex challenges and enhancing productivity. This overview explores the various applications of AI in agriculture, highlighting its potential to transform the industry.
This statement highlights the crucial role of AI in data analysis. It emphasizes that while vast amounts of data are generated daily, AI’s ability to sift through and organize this data is essential for meaningful insights. Flaxman’s explanation underscores the importance of AI’s ability to identify patterns and extract meaningful information from unstructured data.
This technology, which is still in its early stages, is being developed by a team of researchers at the University of California, Berkeley. The researchers are working on a system that can understand and respond to natural language queries, and they are using a variety of techniques to achieve this. These techniques include deep learning, computer vision, and natural language processing.
This practice has continued through the centuries, with farmers using various methods to track their harvests, from simple tally sticks to sophisticated computer systems. However, the advent of AI has revolutionized the way farmers collect, analyze, and utilize data. AI-powered tools can now automate data collection, analyze vast amounts of data, and provide actionable insights, leading to more efficient and sustainable farming practices.
Precision agriculture utilizes data-driven approaches to optimize agricultural practices, aiming to maximize yield and minimize environmental impact. It leverages a variety of technologies, including sensors, drones, and satellites, to collect and analyze data on various aspects of the farm, such as soil conditions, crop health, and weather patterns. This data is then used to make informed decisions about planting, fertilization, irrigation, and pest control.
* **Precision agriculture:** Using sensors and data analytics to optimize fertilizer and pesticide application, reducing waste and maximizing yields. * **Crop monitoring:** Using drones and cameras to monitor crop health and identify potential problems early on, allowing for timely intervention and preventing losses. * **Irrigation management:** Optimizing water usage through sensors and data analytics, reducing water waste and improving efficiency.
Other systems integrate data on evaporation, humidity, soil and ambient temperature as well as satellite and drone data that can indicate early signs of drought stress. “The resolution of all those sensors is increasing,” Flaxman says of satellite data. “The resolution has a spatial component that people are used to, but it also has, very importantly for agriculture, spectral resolution. If you think more like a honeybee, honeybees clue in on flowers because they can see a lot further into the infrared than humans can. When vegetation is under drought stress, you can see it in those infrared bands weeks before the human eye can see it. Those weeks are precious, because that’s enough time to give you the ability to deploy a countermeasure.”
AI assessment of the exact water needs of different areas of a farm, which have distinct terrain and soil parameters, can ensure plants receive optimal levels of moisture — neither overwatered nor underwatered. This in turn can lead to better absorption of fertilizers and reduce the incidence of pests that thrive under overly damp or overly dry conditions. Runoff, which in addition to being wasteful, carries contaminants into the water supply, is also reduced. The management of water will become increasingly important as droughts increase, groundwater resources are depleted, and the quality of arable land decreases over time. As it is, less than 1% of the world’s freshwater is available for use; some 70% of groundwater is used for irrigation.
A. The Power of History in Shaping Modern Agriculture
B.
This is particularly relevant in the historical context of climate change and its impact on agricultural production. The analysis of historical data can also be used to identify patterns and trends that can inform the development of new agricultural practices. By understanding the historical relationship between weather patterns, crop yields, and other factors, farmers can make more informed decisions about planting, irrigation, and pest control. Furthermore, historical data analysis can be used to predict future crop yields and potential risks associated with climate change.
AI can be highly effective in analyzing aerial images taken by drones and satellites for subtle spectral changes that indicate the early onset of disease or a pest infestation. Even a decade ago, sugar beet diseases could be detected by AI analysis with accuracy of up to 90%. “AI is trained on high-quality datasets of thousands of examples of different pests and diseases for various plants. In our products, we use deep learning and neural networks to analyze the visual data and identify pests and diseases,” Kogan says. “Normally, people called scouts walk through the greenhouse or fields and look at every single leaf of every single plant to identify the abnormalities. Of course, this is very hard work; scouts miss things and eventually, 30% of harvests are lost on average due to late reaction to pests and diseases.”
AI-powered pest control systems are revolutionizing the agricultural industry by offering a more efficient and sustainable approach to pest management. These systems utilize artificial intelligence to analyze data from various sources, including weather patterns, soil conditions, and pest populations, to predict and prevent pest outbreaks. AI-powered pest control systems are designed to be highly accurate and efficient, reducing the need for manual labor and minimizing the use of pesticides.
AI technology is revolutionizing agriculture by enabling precise weed control. This technology utilizes computer vision to analyze images of crops and weeds, allowing for targeted weed removal without harming surrounding plants. **Detailed Text:**
The agricultural landscape is undergoing a significant transformation, driven by the rapid advancements in artificial intelligence (AI). One of the most impactful applications of AI in agriculture is the development of precise weed control systems.
AI can also be used to detect the emergence of disease in livestock. Cameras can be used to detect signs of pathogens in fish farms, for example. And smart collars can monitor heart rate, respiration, and other vital signs of mammals, thus detecting indications of ill health before they become acute. Soil Conditions and Planting In addition to monitoring soil moisture and drainage, AI can assist in analyzing its nutrient content, composition, and texture using data gathered from IoT devices and historical sources. Classifying soil types and how they align with the needs of various crops can assist farmers in deciding which species, and specific varieties, to plant, how deep seeds should be planted, and how they should be spaced.
* **AI: The New Frontier in Precision Agriculture**
* **AI-Powered Farming:
* **Identifying Deficiencies:** AI can analyze soil samples and identify nutrient deficiencies in real-time. For example, an AI-powered system could detect a lack of nitrogen in a tomato plant’s soil, allowing farmers to apply nitrogen-rich fertilizer promptly. * **Aligning with Plant Needs:** AI can consider the specific needs of different plant species.
This technology can be applied to various agricultural settings, including small farms, large farms, and even urban farms. It can be used to automate tasks, improve efficiency, and reduce labor costs. The use of AI and IoT in agriculture is not without its challenges. One of the the biggest challenges is the cost of implementation.
“The quality of data still remains a huge challenge due to the absence of ground truth — you have to rely on agronomists’ opinions on the images as you can’t do a lab test for every single diagnosis,” Kogan says. Valeria Kogan, Fermata “We tend to have better mapping for things that are aerially extensive. Things that are smaller are poorly covered,” Flaxman adds. Thus, the technology may not even yet be available for some plants. “That’ll be the next major push in agriculture. It will basically help this technology get special crop specifics, which is what it needs to be. Nobody grows general crops, right?” Flaxman says. “That’s an area where the mapping is going to get better. If you grow strawberries, and you’re mapping your strawberries, you’re going to do a better job at that than a government agency has ever done just because it’s in your interest.”
* **High Initial Investment Costs:** IoT devices and AI systems require significant upfront investment. For example, a farm might need to purchase sensors, actuators, and data analytics software, which can cost thousands of dollars.
“The biggest barrier to entry is spending the money to buy that infrastructure,” Avvocato says. “It can add up quite quickly.” FMISs have shown promise in putting more generalized insights from publicly available data into practice but flying drones over smaller operations to spot localized instances of disease and installing sensors to monitor soil moisture remain out of reach to many. Further, gathering data at a single point in time has limited utility. Granular, localized data — as opposed to broader trends gathered from historical observations — becomes more useful as it accrues over longer periods. Flaxman notes that services are available that can conduct aerial surveys for farmers who cannot afford the equipment on their own. There are even cooperative programs in which neighboring farmers can collaborate on surveys and share the resulting data amongst themselves. He suggests combining this data with other, freely available satellite data and analyzing it using subscription web services and open-source tools as a start.
The summary provided highlights the potential of IoT and AI in agriculture, particularly for growers in developing countries facing food insecurity. Let’s delve deeper into this potential and explore the benefits and challenges associated with its implementation. **Benefits of IoT and AI in Agriculture:**
* **Increased Efficiency and Productivity:** IoT sensors can monitor various aspects of crop growth, such as soil moisture, temperature, and nutrient levels.
