AI Market In Agriculture Market Size

Artificial Intelligence (AI) in Agriculture Market Analysis

The AI Market In Agriculture Industry is expected to grow from USD 2.08 billion in 2024 to USD 5.76 billion by 2029, at a CAGR of 22.55% during the forecast period (2024-2029).

The driverless tractor is trending in the market, as these tractors can steer automatically using GPS-based technology, lift tools from the ground, recognize the boundaries of a farm, and be operated remotely using a tablet. A fleet of smaller automated tractors could raise farmer revenue by more than 10 percent and reduce farm labor costs.

• Maximizing crop yield using machine learning techniques is driving the market. Species selection is a tedious process of searching for specific genes that determine water and nutrient use effectiveness, adaptation to climate change, disease resistance, nutrient content, or a better taste. Machine learning, in particular deep learning algorithms, takes decades of field data to analyze crop performance in various climates. Based on this data, one can build a probability model to predict which genes will most likely contribute a beneficial trait to a plant.
• An increase in the adoption of cattle face recognition technology is driving the market. By applying advanced metrics, including cattle facial recognition programs and image classification incorporated with body condition scores and feeding patterns, dairy farms can now individually monitor all behavioral aspects of a group of cattle.
• The increased use of unmanned aerial vehicles (UAVs) across agricultural farms is driving the market, as the use of drones in the agriculture industry can be used in crop field scanning with compact multispectral imaging sensors, GPS map creation through onboard cameras, heavy payload transportation, and livestock monitoring with thermal-imaging camera-equipped drones, which increases the demand for UAVs.
• However, the need for standardization is restraining market growth as the need for data collection and data sharing standards is high. Machine learning, artificial intelligence, and advanced algorithm design have moved quickly, but collecting well-tagged, meaningful agricultural data is way behind.
• The overall impact of COVID-19 on the AI agriculture market was positive. The pandemic acted as a catalyst for innovation and digital transformation in the industry, driving the adoption of AI-driven solutions for increased efficiency, productivity, and sustainability. The need for remote monitoring and management accelerated the digitization of agricultural processes. AI-driven tools for data analysis, predictive modeling, and smart farming have become essential for optimizing production, reducing waste, and ensuring food security.