AI and Machine Learning in Modern Agriculture

Guest Author: Vasyl Cherlinka, Doctor of Biosciences specializing in pedology (soil science), with 30 years of experience in the field.

19 February 2026, London: In November 2025, the World Economic Forum published a report titled “Shaping the Deep-Tech Revolution in Agriculture”. It declared that agriculture stands at a defining moment in history. On the one side, climate change, resource degradation, and geopolitical instability are raging. However, on the other hand, agriculture has undergone significant technological innovations, such as Artificial Intelligence, robotics, and biotechnology, which are essential for addressing current and future agricultural challenges: diagnosing crop stress, reducing cultivation costs, improving yields, securing better prices, and enhancing resilience.

From Traditional Farming to Intelligent Agriculture

Today’s unpredictable climate and tighter resource margins demand more from farmers than their ancestors’ experience, instincts, and on-field inspections. FAO Director-General QU Dongyu’s quote: AI “can have a tremendous positive impact, making agriculture more productive and sustainable”. And really, with modern precision agriculture tools, farmers can find latest satellite imagery, for instance, and fill the gaps that human observation alone can’t cover:

• Predictive analytics: algorithms process data from decades of historical weather and real-time soil readings to predict issues before they occur. A nitrogen deficiency or an approaching drought can be flagged weeks before the first visible sign shows up in the field.
• Satellite-ground integration cross-references sensor data with current satellite imagery to generate detailed crop health maps of moisture levels, pest pressure, and stress zones. Information that would take days to gather on foot now becomes visible in seconds.
• Variable Rate Technology (VRT): Machinery equipped with onboard software can automatically adjust the application of fertilizer, water, or pesticides to the square meter, sending resources exactly where the land needs them and reducing waste.

Spectral Sensing: Seeing What the Eye Misses

But satellites can provide not only raw images and snapshots from above. Spectral sensors on satellites can send invisible electromagnetic waves to provide more information about your field. The industry standard, the Normalized Difference Vegetation Index (NDVI), turns a basic current satellite view of Earth into a detailed health check that spots trouble long before you walk the rows:

• The Science: Healthy plants absorb visible light for photosynthesis and reflect near-infrared energy. Stressed plants do the exact opposite.
• The Scale: NDVI ranges from -1 to +1. High values (usually shown on the map as rich green) indicate thriving crops, while low values (shown as yellow or red) signal immediate issues such as disease, dehydration, or pest pressure.

Advanced farming now integrates hyperspectral data to analyze soil moisture and chlorophyll levels with even finer granularity. When we overlay a current satellite view of Earth with these multi-spectral layers, we get a complete “bio-signature” of the land. 

Intelligent Forecasting: The Farmer’s Crystal Ball

AI algorithms are the engine behind the shift, processing ground sensor data alongside current satellite views to create a comprehensive digital twin of the farm. Additionally, we can work more professionally with weather forecasting. Farmers can see specific outcomes like disease outbreaks, precise irrigation needs, and even the optimal logistics for harvest. By estimating bio-productivity, these systems determine exactly which crops will generate the highest biomass and, subsequently, the most profit.

In 2021, EOS Data Analytics showcased this potential in Kazakhstan. By blending satellite data from the Copernicus program with NASA’s meteorological inputs, they developed a biophysical model that identified the perfect sowing and harvesting windows for five key crops. This didn’t just provide data; it directly boosted the region’s food output.

This foresight is also fundamentally changing how the industry handles risk. In the U.S. and Australia, insurance companies now lean on these insights to verify drought claims and evaluate flood damage with total objectivity. It turns raw information into a financial shield, ensuring that every decision, from the first seed planted to the final insurance payout, is rooted in hard evidence rather than just hope.

Beyond the Human Eye: Precision Crop Classification

Crop classification is the digital mapping of specific crop types across vast regions, and it is critical for food security. By using AI-based tools like EOSDA, farmers can finally bring clarity to the chaos, distinguishing between crops without boots ever hitting the ground. A typical project of crop classification lasts for 3-6 weeks. The process is smooth and has standard project stages:

1. Investigation of vegetation features for AOI.
2. Ground data collection, verification, and filtering.
3. Search and download of required satellite data.
4. Model training and crop classification.

According to the report of the Computer Science Laboratory of the University of Biskra, modern ML algorithms deliver a superior accuracy of 99.77%. As a result, such precise crop classification can enable significant advancements in agricultural productivity, resource optimization, and sustainable food systems. 

The Future of Food Chains Is Bright

Artificial intelligence and machine learning in agriculture will not replace the farmer. They are here to back them up. The real goal is to pair a grower’s deep, generational intuition with the undeniable clarity of real-time data. The farm of tomorrow will be managed as much from a digital dashboard as from the tractor seat, where producers can check soil sensors alongside a live satellite view of Earth to verify their crops’ health instantly.

Also Read: FMC and the Two-Engine Dilemma: When the Present Devours the Future

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