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Vertical farming grows crops in stacked layers within controlled environments, reshaping agriculture during our era of urbanisation and climate change. Unlike traditional methods requiring extensive land and favorable weather, vertical farms thrive in urban spaces—from skyscrapers to warehouses to shipping containers.
Today’s vertical farming gets a boost from machine learning and satellite technology. These tools help farmers make smarter decisions and monitor environmental conditions in real time. This piece examines how Earth observation combined with computing power is creating more sustainable urban agriculture.
The challenge: Feeding a growing urban population
UN projections show global population reaching 9.7 billion by 2050, with 68% living in cities. India specifically expects urban residents to reach 50 per cent by 2047. This shift strains land, water, and food systems tremendously.
Conventional farming faces multiple roadblocks: shrinking farmland, unpredictable weather, and depleted resources. Food grown far from cities creates lengthy supply chains and higher emissions.
Vertical farming addresses these problems directly. It uses minimal land, practically eliminates pesticides, and slashes transportation by growing food near consumers. The key to success lies in smart management systems.
Machine learning takes root in farming
Smart farm operations rely heavily on machine learning. Farmers use data analysis to predict yields, spot diseases early, and fine-tune resource usage. These systems determine perfect harvest timing by processing growth patterns and environmental readings.
Climate control benefits particularly from automation. Systems track historical performance alongside current sensors to maintain ideal growing conditions—constantly adjusting light, humidity, temperature, and carbon dioxide. Watering systems become increasingly precise with each growing cycle.
Computer vision, often paired with ML, allows farms to monitor plant health in real time. Cameras capture images of crops, and algorithms assess leaf color, shape, and texture to identify nutrient deficiencies, pest infestations, or stress signs.
Satellite data: Outside information for indoor growing
While ML provides intelligence from within the farm, satellite data offers the external context. Modern satellites provide multispectral imagery, weather forecasts, and environmental metrics such as humidity, solar radiation, and air quality.
By integrating this data, vertical farms can anticipate external stressors and adjust operations accordingly. For instance, satellite-derived climate models can trigger internal cooling systems or adjust LED light spectrums to mimic optimal growing conditions.
Case studies from Europe and Japan have demonstrated how combining satellite weather data with crop growth models can help forecast pest outbreaks or periods of heat stress—saving costs and reducing pesticide use. In India, ISRO’s remote sensing programs are already supporting precision agriculture in open fields, and similar models are being adapted for enclosed vertical systems.
The integrated growing environment
Modern tech-enabled vertical function as connected ecosystems. Sensors throughout the facility measure growing conditions and feed this information to analysis systems that spot trends and suggest improvements.
Weather and environmental data streams in from satellites while digital interfaces display information and control farm systems. These dashboards alert farmers to problems and automatically adjust growing conditions.
The ultimate goal creates a self-regulating system where all resources maximize harvest while minimizing waste. During predicted drought or cloudy periods, indoor lighting automatically compensates without requiring staff intervention.
Financial and environmental benefits
Advanced vertical farming shows strong results for both business and ecology. The market reached $5.70 billion in 2023 and should grow to $50.10 billion by 2032, expanding at 28.1 per cent annually according to Fortune Business Insights.
Resource conservation stands out among the advantages. These farms typically use 95% less water than field agriculture and much less fertilizer. Precise nutrient delivery reduces waste while speeding plant development.
The environmental footprint shrinks through reduced transportation, minimal pesticides, and preservation of natural spaces. For countries like India where farming consumes 80% of freshwater, these savings could transform agriculture nationwide.
Obstacles to overcome
Despite clear benefits, several challenges slow widespread adoption. Questions about data ownership remain unresolved—who controls information from farms, satellites, and software? Connected systems demand strong security measures.
Startup costs present a major hurdle. Building high-tech vertical farms requires significant investment, particularly for advanced sensors, software systems, and satellite connections. Running these operations needs skilled workers often unavailable in agricultural regions.
Education programs and collaborative funding models will help make these technologies more accessible to diverse farming communities.
Looking forward
Upcoming developments point toward self-sufficient operations. Local computing power enables faster decisions without constant internet connectivity. Tomorrow’s farms might run almost entirely through automated systems using satellite information.
Developers are creating affordable technology packages suitable for smaller farms. Indian agricultural technology companies like Fasal and CropIn currently provide field farming solutions that could easily adapt to vertical methods, potentially democratizing high-tech growing.
Conclusion
Machine learning paired with satellite technology transforms farming from soil to skyline. Vertical agriculture using these tools offers practical, expandable solutions for feeding urban populations sustainably.
As these technologies mature and combine, they create farming that produces abundantly, wastes little, protects our environment, and reaches more producers—agriculture climbing upward while guided by space-based observations.
The author is Practice Head, Agritech Division at [x]cube LABS
Published on April 13, 2025
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