<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Agricultural Technology |</title><link>http://darrylab.org/tags/agricultural-technology/</link><atom:link href="http://darrylab.org/tags/agricultural-technology/index.xml" rel="self" type="application/rss+xml"/><description>Agricultural Technology</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Thu, 01 Oct 2026 00:00:00 +0000</lastBuildDate><image><url>http://darrylab.org/media/icon_hu_1c0e9cb08cfb822a.png</url><title>Agricultural Technology</title><link>http://darrylab.org/tags/agricultural-technology/</link></image><item><title>LUDA</title><link>http://darrylab.org/projects/luda/</link><pubDate>Sat, 01 Aug 2026 00:00:00 +0000</pubDate><guid>http://darrylab.org/projects/luda/</guid><description>&lt;p&gt;LUDA (Ledger verified Unified Diagnostics for Agriculture) is an Android platform that helps smallholder farmers diagnose crop disease, see the reasoning behind each diagnosis, and buy the inputs they need.&lt;/p&gt;
&lt;p&gt;A team of specialised AI agents follows a verify before commit pattern: no diagnosis, explanation or transaction is recorded until it has been checked, and verified records are anchored on the Internet Computer blockchain. Farmers can pay for inputs through MTN MoMo, Airtel Money and Zamtel. Climate context comes from CHIRPS, ERA5 and NASA POWER, and the models improve through federated learning with differential privacy, so farm data stays on the farmer&amp;rsquo;s device.&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Status:&lt;/strong&gt; In development.&lt;/p&gt;</description></item><item><title>YieldTrace</title><link>http://darrylab.org/projects/yieldtrace/</link><pubDate>Thu, 01 Oct 2026 00:00:00 +0000</pubDate><guid>http://darrylab.org/projects/yieldtrace/</guid><description>&lt;p&gt;When a harvest fails, farmers, insurers and governments all need to know why. YieldTrace estimates what each farm would have produced under normal conditions and attributes the shortfall to its causes.&lt;/p&gt;
&lt;p&gt;The project applies the potential outcomes framework of causal inference at the scale of an individual farm, drawing on CHIRPS and ERA5 climate data. The first study will focus on maize, cassava and tomato in Chinsali District, Muchinga Province, Zambia. The goal is to produce the kind of farm level evidence that agricultural policy commitments under the CAADP Kampala Declaration call for.&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Status:&lt;/strong&gt; Concept stage.&lt;/p&gt;</description></item></channel></rss>