AI for Arable Farming: Key Trends to Watch

As of Q2 2024, artificial intelligence (AI) applications in agriculture (and arable farming specifically) are advancing rapidly. Climate change is not waiting for us but speeding up on daily basis. Hence, every farmer and owner of agribusiness think about productivity, sustainability, and efficiency across various crops. The main directions and trends for AI advancements are based on the areas of:

Precision Agriculture and Autonomous Machinery

AI optimizes the application of inputs like fertilizers, pesticides, and irrigation based on real-time field data, enhancing resource use efficiency and crop yields. Hence, look towards Variable Rate Technology (VRT). Get better understanding of this technology by reading how variable-rate fertilization can be applied for citrus orchard management . However, be aware of the barriers for adoption of these innovations using the example of Italy ????

Another thing to watch is soil health monitoring. AI analyzes soil samples and sensor data to provide tailored recommendations for soil management, ensuring optimal conditions for diverse crops. I wish everyone to read the freshly published paper "Advancing Soil Health: Challenges and Opportunities in Integrating Digital Imaging, Spectroscopy, and Machine Learning for Bioindicator Analysis".

One more trend in this direction is development of autonomous tractors and harvesters. As of now experimental AI-driven machinery performs planting, weeding, and harvesting tasks with high precision, reducing labour costs and increasing operational efficiency. No massive adoption yet. However, John Deere has big plans on the matter. One of the interesting things to know about is robotic weed control. AI-powered robots identify and eliminate weeds with minimal chemical use, promoting sustainable farming practices. For example, pulsed microshocks are a suitable alternative for herbicides (bad news for large producers of agrochemicals though)

Crop Monitoring and Yield Prediction

First and foremost, it's all about disease and pest detection. AI processes data from drones, satellites, and ground sensors to detect early signs of diseases and pest infestations, enabling timely interventions. To understand how it works - check this recent Frontiers in Plant Science edition on boosting precision crop protection towards next gen agriculture using machine learning and emerging technologies.

Another key agriculture trend which is speeding up with AI is advanced growth stage monitoring. AI analyzes images and sensor data to monitor crop growth stages, helping farmers optimize irrigation, fertilization, and harvest timing. For example, this research explains how by using machine learning and sentinel remote sensing imagery as well as analysis of the harvest dates from 297 winter yield maps, collected between 2019 and 2021 across 22 farms covering 56000 ha in eastern Australia, it was possible to identify winter crop harvest dates.

As you can see, remote sensing which on practical level is represented by satellite and drone imagery processed by AI provides large-scale yield estimates and identifies variability within fields.

Finally, the mentioned Australian paper featured one more key agritech trend: predictive analytics. Machine learning models use historical data, weather patterns, and real-time field data to forecast crop yields, aiding in planning and market strategies.

That's all for today - you got some food for mind.

Read me tomorrow.

What's next in AI for arable farming?

In the next edition of "AI for Arable Farming" I'll continue on AI developments for arable farming in terms of climate change adaptation, supply chain optimization including quality control and we will even go beyond and speak about market intelligence with price forecasting and consumer trend analysis. Yes, we need to discover new horizons to co-live with AI.

Stay in touch! ??

Wishes of better yields,

Maryna Kuzmenko, Ph.D ???? , Chief Inspiration Officer at Petiole Pro Community

#arablefarming