I have worked for many years on crop modeling, climate variability, prediction, and agricultural decision-support systems. More recently, I have been focusing on how AI, machine learning, remote sensing, digital twins, and data-driven methods can be combined with agricultural science.

I recently started an educational YouTube channel, Digital Agriculture and AI Lab, where I share short, practical videos on topics such as AI in agriculture, precision agriculture, crop modeling, smart irrigation, remote sensing, digital twins, and climate-smart agriculture.

I would be very interested to hear from this community: where do you currently see the greatest practical value of AI in agriculture?

Channel:
https://www.youtube.com/@DigitalAgAILab

Best

Bryan

China has launched CottonMind 1.0, a specialised #AI platform designed to connect cotton research, breeding, field diagnostics and farmer advisory services through a single digital system.

Developed by the Institute of cotton Research of the Chinese Academy of Agricultural Sciences, the platform includes a web-based research interface and a mobile mini-programme for growers.

Its cotton-focused knowledge base contains 41,680 research papers, more than 2,000 patents and standards, 559 specialised books and 1,806 approved cotton-variety records.

Farmers can use the platform to ask crop-management questions, upload images for preliminary diagnosis and consult agricultural specialists.

Researchers can use it to search scientific literature and connect information on genes, traits, varieties, cultivation and plant-health issues.

CottonMind supports Chinese, English, Uyghur and Uzbek, potentially improving access to technical information across major cotton-growing regions.

The developers plan to further expand the platform’s analytical capabilities and precision services, aiming to shorten the gap between cotton research and practical farm-level decision-making.