Agriculture is a major industry that is constantly evolving and adapting to the changing environment. As the world population continues to grow, so do the demands on the agricultural industry to produce more food with fewer resources. As a result, there are a number of trends emerging in the agricultural sector that are driving innovation and efficiency.
One of the most significant trends in agriculture is the increasing use of technology. Farmers are now able to use sophisticated software and hardware to monitor and manage their crops and livestock. Additionally, the use of drones and other aerial imaging systems are becoming more common, allowing farmers to monitor their fields from the sky. From measuring soil nutrient levels to monitoring irrigation and the use of drone imagery to map and estimate disease presence, artificial intelligence (AI) will become a constant presence in agriculture productions of all sizes. This technology allows them to maximize yields, reduce costs, and increase efficiency.
Global spending on smart technology and connected systems in the ag space is projected to triple in revenue by 2050. That includes AI and machine learning. AI spending alone is predicted to grow at a compound annual growth rate of 25.5% between 2020 and 2026, eventually reaching $4 billion, and with it, the capabilities of these technologies will grow.
Another trend in agriculture is the growing focus on sustainable practices. Farmers are increasingly turning to organic and regenerative farming methods to reduce their environmental impact. This includes the use of cover crops, crop rotation, and no-till farming. These practices help to reduce soil erosion, conserve water, and improve soil health.
The use of precision agriculture is also becoming more popular. This involves using sensors and other technology to monitor and manage crops on a very small scale. This allows farmers to apply the right amount of fertilizer and water to each plant, resulting in higher yields and lower costs.
Finally, the use of big data is becoming more common in agriculture. This involves collecting and analyzing large amounts of data to identify patterns and trends. This data can be used to optimize crop production, predict weather patterns, and even identify pests and diseases.
Ag growers are looking at synthetic data which is often used to validate AI models. Based on real-world data, and created by a model that uses the parameters of real-world datasets, it can be used to create a "digital twin." This synthetic digital twin emulates real life, which can be particularly helpful in agriculture, where variables like soil types and weather conditions must be understood for real-world applications. With so much potential, use of this will increase, with Gartner predicting its use will outpace real data in AI models by 2030. Already trending in other industries, synthetic data is on agriculture’s waiting list for 2023.