Artificial intelligence is rapidly transforming agriculture from a reactive, data-driven industry into a predictive and increasingly autonomous ecosystem. As climate volatility, labour shortages, and resource constraints intensify, growers are looking beyond traditional farm management tools toward AI systems capable of translating complex datasets into timely, actionable decisions. The convergence of predictive analytics, generative AI, IoT, satellite imagery, and hyperlocal climate intelligence is redefining how crops are monitored, risks are managed, and productivity is optimised.

At the forefront of this transformation is WayBeyond, whose FarmRoad Agronomy Intelligence platform combines predictive AI with generative AI to deliver personalised agronomic recommendations rather than simply reporting farm data. By integrating sensor data, satellite imagery, climate forecasts, and grower-specific operational context, the platform enables farmers to make faster, more informed decisions while improving crop resilience and resource efficiency.

In this exclusive interview with AI Spectrum, Dr. Mpatisi Moyo, Head of Artificial Intelligence at WayBeyond, discusses how the company is bridging the gap between AI-generated insights and real-world farm actions, the role of hyperlocal climate forecasting and explainable AI in building farmer trust, and how the next generation of predictive intelligence, robotics, and autonomous systems could reshape protected cropping and precision agriculture in the years ahead.

FarmRoad Agronomy Intelligence provides daily AI-powered action plans rather than just reporting data. What technological challenges did WayBeyond overcome to shift from data visibility to actionable decision intelligence?

FarmRoad already hosts a rich suite of Predictive AI Models such as Yield Prediction, Yield Swing Events, Disease Risk Models and Internal Climate Forecasts that run on sensor climate data, and irrigation and crop data collected via FarmRoad IoT technology and APPs. But understanding and taking action on these rich predictions is frequently a challenge for some growers. WayBeyond is now integrating GenAI on top of this traditional AI layer to help growers digest this layer of predictive AI into actions they can understand in natural language and are able to follow through on. To enable this,  WayBeyond is designing a complete AI platform that is collecting more and more granular growing practice context and feedback from growers to produce personalised recommended actions from the data and predictions from our traditional AI models.

The platform integrates farm data, sensors, satellite imagery, and hyperlocal climate forecasting. How does the AI synthesise these diverse sources to identify risks and prioritise actions in real time?

We use the full range of AI, starting with Predictive AI models, which are at the moment labelled traditional AI. These do the heavy lifting of converting the varied data sources to information by predicting the future, such as future climate in the greenhouse or tunnel environments, future plant health and disease risks, and future yield. We then use GenAI to close the gap on what all these predictions mean and what the farmer should do with them by adding nuanced knowledge to the predictions.

How do your AI models achieve up to 97 per cent accuracy in hyperlocal climate forecasting, and how does this approach differ from conventional weather prediction systems?

We leverage our FarmRoad Sensors to improve various signals from conventional outside weather predictions so that they mirror climate conditions inside a protected growing area with improved precision. A farmer may have a number of these sensors in an area to accurately map its micro-climates, and this is the data that drives the precise hyperlocal forecasts for their specific farm.

How does FarmRoad Agronomy Intelligence ensure transparency and explainability in its recommendations to build farmer trust in AI-driven outputs?

Besides measurement data, FarmRoad collects rich contextual data from farmers on their specific crop and management practices. This helps us provide intelligence that is grounded in the farmers' practice context and ensures personalised, relevant, explainable, and transparent recommendations that the farmer can trust. We use AI models to mirror the decisions the farmer would likely make in their specific context, only faster and at scale across multiple data sources and farm sectors. 

As climate volatility increases, how do you see predictive agronomy reshaping risk management and crop resilience over the next five years?

Predictive agronomy has the potential to not just influence farmers’ day-to-day growing decisions, but when combined with knowledge of the crops, their genetics and climate trends, it can influence which crops farmers choose to grow in different environments. This is strategic risk management that leverages knowledge on crop genetics, resilience and predicted crop performance in different climates to support operational day-to-day mitigation of risks from weather forecasts.

With the rapid adoption of generative AI and machine learning, what is your vision for the future of autonomous farm decision-making systems in protected cropping?

AI and Machine Learning are yet to achieve tremendous value in Agriculture- this would happen when we begin to integrate precision physical AI predictions and physical automation, such as robotics, and intelligent control systems, which lag in adoption now compared to GenAI. There are promises in autonomous farming; however, some paradigm thinking will need to shift, including the way we design farms and farm spaces to accommodate intelligent robotics and automation. GenAI layers can help in enhancing the knowledge and context of decisions, and enabling natural language interactions with robotics and physical AI, which would largely be the force multipliers in a largely physical labour sector like Agriculture