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.

Hey everyone — I'm working on a project called irriGATE and would love feedback from actual farmers and ag folks (not just my classmates 😅).

The idea: drone-based multispectral/thermal imagery to catch dry zones early and cut water waste from runoff, evaporation, and deep percolation — basically turning aerial data into smarter irrigation decisions.

If you work in farming, agronomy, or ag research, I'd really appreciate 5 minutes to fill this out — it's mostly quick multiple choice with a couple open questions: https://www.surveymonkey.com/r/MPNV9GZ

Happy to answer questions in the comments, and thanks in advance to anyone who takes the time!

Hi everyone,

Over the past few months, I’ve been working on Agrocore, an open-source management software for agriculture, farming, and land management, built entirely with Rust.

Why build this?

Many existing agricultural systems are either expensive, proprietary cloud platforms that hold your data hostage, or outdated systems that lack flexibility. I wanted to build a modern, self-hostable, and privacy-first solution that puts control back into the hands of farmers and homesteaders.

Key Highlights

• Built in Rust: Designed for performance, safety, and reliability on light edge-hardware.
• Modular Architecture: Core data engine with extensible modules for crop tracking, machinery maintenance, and resource planning.
• Self-Hostable: Keep complete ownership of your agricultural and operational data.

Tech Stack

• Language: Rust
• Repository:https://github.com/peopleandpixel/agrocore-rs

Looking for Feedback & Contributors

The project is still in active development. I’d love to get feedback from the community:

1. What features would you consider essential for open-source farm management?
2. Any Rust developers interested in contributing to early architecture decisions or module design?

Feel free to check out the repo, star it if you find it interesting, or drop your thoughts below!

I built Groundtruth.
Disclosure up front: this is not a stealth ad.

Most AgTech for small produce is a hosted calendar:

Standing orders spawn next week.
The phone writes.
Stripe lives in someone else’s tenant.
That’s a real product.
It’s also a different constitution than a perishable wholesale desk.

The constraint I would not break:

One PC is the only writer.
The farm file is the books.
Replay the event log against the projection, and the screen is allowed to go red if they drift.
Connection state — Stripe keys, farm display name, etc. — is config, not farm truth.
Import refuses a foreign dump.

What that looks like on a wiped machine:

Empty on purpose.
No demo venue.
No seed inventory.
No invented calm zero.
First actions:
→ Venue
→ Standing or sample
→ Sow to cover demand
→ First wholesale order

The domain that’s easy to get wrong — and calendar apps usually flatten:

• Standing demand is weekly and undated.
A wholesale line is dated.
Those are not the same object.

• Harvest is crop-first. Pack is customer-first.
What the knife cuts vs. who gets the trays.
Both are printed from the same ordered-for-today read.
No second “cut” event kind.

• Leftover is actual harvested ounces.
Listed off the harvested tray, then collected on that row.
Not a guaranteed-yield leftover cap.

• Health is computed.
Healthy is not the default skin.
Stack, short:
Rust/Tauri + SQLite event log
React desk
Local-first
Free
No paid SaaS as the source of truth
Live card keys are still off.
Collect is test-mode on purpose.
I’m not claiming this replaces a multi-seat van + live invoicing suite.

Those products win at:
“Less thinking on Sunday.”
This is aimed at:
“Less lying on Monday.”
If you work on farm software:
How do you keep standing orders (rate) and wholesale orders (date) from collapsing into one table?
That collapse is the lie I kept hitting.

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