AI-Driven Agriculture: AgriTech for a Sustainable Future

Published on
May 17, 2024
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Artificial Intelligence (AI) has permeated nearly every industry, and now, by combining with the Internet of Things (IoT), it holds the potential to revolutionise agriculture. While agriculture may not be the first field that comes to mind when considering AI applications, AgriTech is rapidly gaining importance. Like other industries, agriculture faces significant challenges, including a limited workforce and the severe impacts of climate change.

In the past, farmers relied on predictable seasons, manageable pest cycles, and fertile soil. However, intensive farming practices, climate change, and a rapidly growing global population have placed immense pressure on natural resources and farming requirements.

Today, the agriculture sector must meet higher consumer demands for both animal and plant-based products, comply with stricter climate regulations (e.g., pesticide use), and address increasing social concerns around animal welfare (e.g., free-range farming). The industry is also grappling with a shortage of human capital. These factors are driving the agricultural sector to adapt and optimise production.

Technology, and specifically AI, can be a powerful ally in addressing these challenges. This blog post will explore how AI can support the farming industry in meeting these evolving requirements, ultimately paving the way for a more efficient, sustainable, and resilient agricultural future.

Soil Conditions in Agriculture: The Foundation for Sustainable Crop Cultivation

Soil quality is one of the most crucial yet often overlooked factors in plant-based farming and crop cultivation. Although we may not always consider it, high-quality crops are vital for a functioning society. They are essential for food production (e.g., wheat and rice for human consumption), livestock feed (e.g., alfalfa and hay), textile manufacturing (e.g., cotton and flax), and various industrial purposes (e.g., rubber and tobacco).

Intensive farming practices and climate change have significantly contributed to soil degradation, making it increasingly important for farmers to consider soil conditions when planning their crops. Gone are the days when crops were naturally suited to specific soils and seasons.

Innovative solutions like advanced weather forecasting, incorporating multimodal and multi location sensors (soil, air, and temperature), now allow landowners to monitor soil quality, apply targeted fertilisers, and enhance overall harvest efficiency. Furthermore, matching plant performance to soil profiles enables a more agile approach to agriculture.

Technologically, these solutions can be implemented using time-series approaches like predictive modelling and recommendation systems that suggest the optimal crop-soil combination. By leveraging these advancements, farmers can improve crop yields and contribute to sustainable farming practices, ensuring long-term agricultural productivity.

Revolutionising Plant Health Monitoring with Technology

Historically, monitoring plant health involved manual inspection of leaves, fruits, and flowers to detect diseases and pests. However, this process is time-consuming and requires significant human resources, particularly for larger farms. Additionally, some crop diseases can spread rapidly, necessitating swift action.

Technology offers farmers a range of tools to monitor and maintain plant health effectively. Unmanned aerial vehicles (UAVs) like drones, along with autonomous ground vehicles, can be equipped with cameras to capture images and identify diseases and pests automatically. Monitoring routes can be scheduled to keep a close eye on treatment progress, monitor plant health improvements, and facilitate early problem detection. Recommendation systems, powered by data such as weather conditions, soil quality, and plant imagery, can provide tailored treatment recommendations for specific pests and plant needs.

This solution can be technologically implemented using computer vision techniques, such as convolutional neural networks (CNNs) and vision transformers, to classify diseases, assess plant conditions, and identify pests. Additionally, georeferencing can optimise the process of human verification of plant conditions and treatment effectiveness.

Animal Health and Monitoring: Leveraging Technology for Improved Animal Welfare

Society is increasingly conscious of animal welfare, and intensive animal farming practices have come under significant scrutiny. In response, free-range farming—where animals roam freely during the day and return to enclosures at night—is gaining popularity. While beneficial for animal welfare, this approach poses challenges in herd monitoring and animal counting. Free-range settings require careful attention and substantial human effort to monitor animal health and ensure accurate counts, as animals can get lost, injured, or fail to return to the barn.

Historically, herds were monitored by shepherds and their dogs. However, the current scale of agriculture cannot sustain these practices due to their high labour requirements and inefficiency.

Technology can be an invaluable ally in overcoming these challenges while enabling larger roaming areas for animals. Autonomous vehicles connected with animal identification systems, such as collars, can help monitor herds. When these vehicles are also equipped with cameras, they can provide valuable insights into animal health by recording and processing images of these. Additionally, technology can streamline animal counting as herds return to the barn and can also be employed in wildlife-protected areas to identify and count animals as they move in and out of the region.

These applications rely on computer vision techniques capable of classifying, identifying, and tracking animals. Specifically, convolutional neural networks (CNNs) and object detection algorithms can visually monitor, track and identify individual animals, and enable efficient animal counting. Incorporating these technologies into animal farming practices would enhance animal welfare and provide farmers with crucial data to optimise herd management.

Final Thoughts

AI technology can be a powerful tool in addressing the new needs and challenges faced by the agriculture sector. From monitoring soil quality to ensuring animal health and optimising crop yields, AI-driven solutions offer a range of approaches tailored to various use cases. Whether it's computer vision for disease detection or autonomous vehicles for herd monitoring, the agricultural industry can harness these innovations to improve efficiency, sustainability, and productivity. Ultimately, embracing AI will empower farmers to adapt to changing demands and create a more resilient agricultural future.

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