AI is transforming farming in Kenya—from diagnosing crop diseases to boosting incomes. Discover how tradition meets technology as smallholder farmers embrace digital tools, leading to smarter harvests, stronger communities, and a more food-secure future.
Once Upon a Farm in Kericho…
Just a few years ago, in the rolling hills of Kericho, Kenya—famous for its tea and morning mists—Sammy Selim stood in his field at dawn, hands resting on a rusted hoe, eyes scanning the uneven rows of coffee plants. He had done everything his father taught him—checked the moon cycles, burned charcoal to fight pests, called on the local cooperative for advice—but each season brought diminishing returns. The rains came too early or too late, pests were getting smarter, and the market never seemed fair.
Like many smallholder farmers across the country, Sammy had inherited more than a plot of land. He inherited the weight of tradition, the sting of unpredictable harvests, and the resilience to keep trying. Yet, resilience alone wasn’t paying school fees or buying fertilizer.
In the past, farming in Kenya was a generational dance with nature—beautiful, but increasingly brittle in the face of climate change, dwindling resources, and lack of access to information. Extension officers were few and far between, and even when they visited, their advice often came too late.
But now, under the same early morning sun, Sammy stands in his field holding a smartphone. It’s not just a gadget; it’s his agronomist, pest detector, weather advisor, and market analyst. With a few swipes on PlantVillage Nuru and a voice message to Farmer.Chat, he knows what to plant, how to treat that leaf discoloration, and when to expect a fair price at the local market.
The transition from hoe to AI wasn’t immediate—it took trust, learning, and a leap of faith. But it signals a powerful shift: farming in Kenya is entering a digital renaissance, and farmers like Sammy are at the forefront of it.
So how exactly is AI helping farmers reshape their lives and livelihoods? Let’s dig into the tools, stories, and philosophical reflections behind this agricultural transformation.
The Rise of the Digital Agronomist
When Sammy first heard the term “digital agronomist,” he laughed. “Is it like having a robot come to your shamba?” he joked to a neighbor. But curiosity got the better of him. He borrowed his nephew’s smartphone, downloaded an app he’d heard about at the local co-op meeting, and started typing questions about his stunted coffee crop.
What he found wasn’t a robot—but something arguably better. On the other end of that app was a machine learning model trained on thousands of crop scenarios. It processed Sammy’s soil quality, recent weather patterns, historical yield data, and even the images he took of his leaf problems. Within minutes, it offered tailored advice on what fertilizer to apply, how much to water, and which pest was nibbling his leaves.
This, in essence, is a digital agronomist: a virtual assistant trained in the science of crop production and powered by artificial intelligence. Where traditional agronomists rely on years of hands-on experience, a digital agronomist has been trained on millions of data points, satellite images, plant diseases, soil samples, and market signals. It doesn’t just give general advice—it provides personalized, real-time guidance based on precise, localized data.
The implications are profound.
In regions where one government extension officer may be responsible for thousands of farmers, access to reliable agronomic advice can mean the difference between a surplus and a failed harvest. A study from the International Food Policy Research Institute (IFPRI) found that access to timely, relevant agricultural information can increase farm productivity by up to 25% (Sheahan & Barrett, 2017).
And this is where digital agronomy thrives. Apps like Virtual Agronomist and Apollo Agriculture are closing the gap, offering services through low-bandwidth platforms such as WhatsApp or SMS, in local languages, and at little or no cost to the farmer. These tools help farmers make more informed decisions about inputs—reducing waste, minimizing environmental impact, and optimizing yield.
Dr. David Muchiri, a Nairobi-based agritech consultant, describes it this way:
“The digital agronomist doesn’t replace the wisdom of the farmer—it enhances it. It’s the flashlight in the dark, not the map. Farmers still lead; AI just helps them see a few steps further.”
And the beauty lies in scale. Unlike human agronomists, AI can handle thousands of interactions simultaneously. It never sleeps, never forgets, and constantly learns. In countries like Kenya, where agricultural productivity is tightly linked to national food security, these innovations are more than just helpful—they’re critical infrastructure.
As the digital agronomist becomes more accessible, it’s also becoming more integrated—pairing with weather APIs, market pricing tools, satellite imagery, and pest detection systems. Which leads us naturally to the next piece of the puzzle: how farmers are using AI tools to see the invisible threats in their fields—pests and diseases—before they destroy a season’s work.
When Help Doesn’t Come: AI as a Substitute for Extension Services
Long before AI arrived in Kenya’s farming communities, another system carried the promise of agricultural transformation: extension services.
Extension officers—trained in agronomy, animal husbandry, and rural development—were meant to be the lifeline between government research institutions and smallholder farmers. Their job was to bring new techniques, share best practices, and troubleshoot problems in the field. In theory, they were the walking encyclopedias of agriculture. In practice, however, they were often overworked, under-resourced, and tragically outnumbered.
Imagine one extension officer tasked with supporting 2,000 farmers across multiple villages, some accessible only by motorcycle through muddy paths. Appointments would be delayed. Visits would be rushed. Information would be generic. Many farmers, like Sammy, would go entire seasons without a single expert visit—left to rely on hearsay, guesswork, or past experience.
A 2020 report from the Alliance for a Green Revolution in Africa (AGRA) found that in Kenya, there was roughly one extension officer for every 1,500 farmers—far below the recommended ratio of 1:400 (AGRA, 2020). Worse, many officers lacked updated training on climate-resilient practices or emerging crop diseases. This system, once a pillar of support, began to buckle under its own weight.
That’s where AI didn’t just step in—it leapt.
Enter Farmer.Chat, a multilingual AI chatbot developed by Digital Green, and trained to answer a vast array of farming queries: “Why are my tomato leaves curling?” “When should I plant maize this year?” “Is that yellowing due to pests or nutrient deficiency?” The bot responds in real time, in local dialects, and tailors its advice based on geographic data, past user behavior, and research-backed agronomic knowledge.
By the end of its first year, Farmer.Chat had fielded over 260,000 questions from 14,000 users in Kenya alone. Farmers began reporting quicker decisions, earlier interventions, and fewer crop losses. “I used to wait weeks to get advice,” one user, Wanjiku Mwangi, shared in a Digital Green case study. “Now, I just ask my phone. The answers come before the pests do.”
What makes AI uniquely suited for this role isn’t just its speed or scale—it’s its consistency. While extension officers can be subjective or influenced by limited data, AI models draw from enormous, up-to-date databases. They’re continuously improved, frequently audited, and designed to deliver science-based recommendations.
Still, it’s important to recognize that AI doesn’t replace human expertise—it supplements it. In fact, in areas where extension services still function well, AI is becoming a co-pilot rather than a substitute. Extension officers use apps to pre-screen problems, generate localized reports, and prepare smarter recommendations. It’s a symbiosis, not a takeover.
And the results have been striking—not just in the field, but in the wallet.
So what happens when farmers not only grow smarter crops—but also grow their income? That’s where the conversation shifts from survival to economic uplift.
Economic Uplift: From Surviving to Thriving
For generations, Kenyan smallholder farmers like Sammy Selim toiled under the weight of uncertainty—weathering unpredictable climates, market volatility, and limited access to resources. Their primary goal was often mere survival, with little room for growth or prosperity. However, the advent of AI-driven tools has begun to shift this narrative, offering a pathway from subsistence to sustainability.
In 2024, a study by the GitLab Foundation revealed that AI-powered agricultural advisory services increased annual incomes by an average of $161 per farmer across 155,505 individuals in Kenya. This translated to more than $236 million in additional lifetime earnings, yielding a remarkable 549x return on the initial funding investment (GitLab Foundation, 2024).
Similarly, Nairobi-based agritech innovations such as digital marketplaces and AI-optimized cold storage have been credited with raising smallholder farmer incomes by over 15%. These tools help farmers reduce post-harvest losses and gain access to higher-paying markets without middlemen (Farmonaut, 2024).
These figures underscore a broader trend: AI is not merely enhancing productivity—it’s catalyzing economic empowerment. By delivering timely, personalized insights on crop management, pest control, and price forecasts, AI allows farmers to make smarter decisions that directly affect their bottom lines.
In parallel, AI is quietly revolutionizing how smallholder farmers interact with financial institutions. Traditionally locked out of credit markets due to informality and risk perception, many farmers now leverage AI-generated farm data to access microloans and insurance. AI models assess risk profiles more objectively than traditional underwriting, leading to greater inclusion in financial ecosystems (BMZ Digital, 2024).
The ripple effects of this economic uplift are profound. Increased income means children stay in school longer, healthcare becomes more accessible, and families invest in tools, irrigation systems, and quality inputs. Rural communities see declines in poverty, and young people—previously disillusioned with farming—begin to view agriculture as a viable, even exciting, future.
As Dr. Amina Mwangi, a respected agricultural economist, puts it:
“AI should not replace the farmer’s intuition but enhance it. The synergy between traditional knowledge and modern technology can lead to more resilient farming systems.”
AI is not only a set of tools—it is becoming a catalyst for structural change in Kenya’s rural economy. The next decade may very well see rural prosperity not as an exception, but as a widespread outcome of smart, data-driven agriculture.
🧭 Philosophy in the Field: When Tradition Meets Technology
Beneath the rapid digitization of agriculture in Kenya lies a deeper story—a quiet, sometimes uncomfortable, conversation between tradition and technology.
On one hand, farming is an ancient craft, steeped in generational wisdom. Many of Kenya’s smallholder farmers learned by watching their parents and grandparents, not by consulting an app. They plant according to seasons, read the clouds like scripture, and understand soil textures the way a sommelier reads a wine glass. This knowledge is intimate and intuitive. It’s culturally rooted, and in many communities, sacred.
So, when AI entered the scene—with its neural networks, predictive models, and smartphone interfaces—it didn’t just offer help; it challenged a worldview.
“My father never used machines to know when to plant,” says 61-year-old maize farmer Charles Mutua from Kitui. “He used the moon and the birds. Now you tell me a robot knows more?”
This skepticism is not unique. Adoption of AI tools in rural Kenya has followed a classic diffusion of innovation curve. The early adopters—often younger farmers, more tech-savvy or exposed to urban education—jumped in eagerly. They saw smartphones not as foreign but as familiar. For them, apps like PlantVillage Nuru were simply the next tool in their belt—like the plough, the pesticide sprayer, or the rain gauge.
But for older generations, or those in remote communities with limited digital infrastructure, the resistance is more than just technical—it’s emotional. There’s a fear that trusting algorithms over elders undermines the wisdom that has sustained families for centuries. And to some extent, it’s a valid concern.
“Technology without context is dangerous,” warns Professor Wanjiku Githongo, a social anthropologist at Egerton University. “We must be careful not to replace indigenous knowledge systems, but to integrate them—like weaving a new thread into a well-loved fabric.”
Still, something remarkable is happening. Farmers who once scoffed at AI are now asking their grandchildren to take pictures of sick plants and send them to chatbots. Cooperative societies are offering digital literacy classes alongside fertilizer distributions. Even radio programs are broadcasting how to use AI in agriculture, in Kiswahili and local dialects.
There’s a growing recognition that adopting technology doesn’t mean abandoning tradition. Instead, it’s about harmonizing them. AI can predict rain patterns, but only a farmer who has walked their land for 30 years knows how quickly the clay soil holds it. AI can suggest optimal planting windows, but it can’t tell when the bees are unusually quiet this season.
This blending of wisdom and data is the true frontier of agricultural transformation. The question isn’t whether AI will replace farmers—it won’t. The real question is: how can it make farmers even better at what they’ve always done?
And when this harmony is achieved—when silicon meets soil, and algorithms meet ancestral wisdom—that’s when technology becomes more than a tool. It becomes a partner in legacy.
🌾 Conclusion: Planting the Future, One Byte at a Time
Standing in his coffee field, smartphone in one hand and a handful of healthy soil in the other, Sammy Selim no longer sees a conflict between tradition and technology. Instead, he sees collaboration. He’s still farming his father’s land—but now with tools his father never dreamed of.
The story of AI in Kenyan agriculture is not one of machines replacing humans. It’s about machines empowering humans—giving farmers timely insights, restoring economic dignity, and creating new pathways for rural prosperity. AI has not rewritten the farming story. It’s added a new chapter.
From digital agronomists who never sleep, to chatbots offering multilingual support to farmers in remote villages, to predictive tools that detect crop diseases before they become crises—artificial intelligence is rapidly becoming part of the DNA of modern agriculture. But its real power lies not in the code, but in the communities it serves.
There are still challenges, yes. Digital literacy, infrastructure, and equitable access remain hurdles to overcome. But the momentum is undeniable—and it’s being fueled by a generation of farmers who are no longer waiting for help. They’re building their own solutions, often from the ground up, and often with nothing more than a smartphone and a patch of land.
Kenya’s fields are beginning to whisper a new language—one written in algorithms, informed by satellite data, and powered by ancient hands that still know the rhythm of the earth.
And maybe, just maybe, the future of farming doesn’t look like science fiction after all.
It looks like Sammy.
It looks like community.
It looks like wisdom—augmented.
📚 References
- BMZ Digital. (2024). AI levels the field: Kenyan farmers get smarter access to credit. BMZ Digital Global. https://www.bmz-digital.global/en/news/ai-levels-the-field-kenyan-farmers-get-smarter-access-to-credit/
- Farmonaut. (2024). Revolutionizing Kenyan agriculture: How digital platforms and cold storage are empowering smallholder farmers. https://farmonaut.com/africa/revolutionizing-kenyan-agriculture-how-digital-platforms-and-cold-storage-are-empowering-smallholder-farmers
- GitLab Foundation. (2024). Empowering Kenyan farmers: How AI is revolutionizing agriculture with Digital Green. https://www.gitlabfoundation.org/our-journey/empowering-kenyan-farmers-how-ai-is-revolutionizing-agriculture-with-digital-green
- Scholar Media Africa. (2023, November 8). AI in agriculture: How Kenyan farmers benefit from PlantVillage Nuru app. https://scholarmedia.africa/agribusiness/ai-in-agriculture-how-kenyan-farmers-benefit-from-plantvillage-nuru-app/
- The Guardian. (2024, September 30). High tech, high yields? The Kenyan farmers deploying AI to increase productivity. https://www.theguardian.com/world/2024/sep/30/high-tech-high-yields-the-kenyan-farmers-deploying-ai-to-increase-productivity
🔗 Additional Resources
- PlantVillage Nuru App
Diagnose crop diseases using AI image recognition.
https://plantvillage.psu.edu/nuru - Digital Green – Farmer.Chat
AI chatbot for personalized agricultural advice.
https://www.digitalgreen.org/solutions/farmer-chat - Apollo Agriculture
Access credit, inputs, and crop insurance using AI-powered decision tools.
https://apolloagriculture.com - Kenya Agricultural and Livestock Research Organization (KALRO)
Official resource hub for national agri-research.
https://www.kalro.org
📖 Additional Reading
- International Food Policy Research Institute (IFPRI). (2023). Digital agriculture and smallholders in Sub-Saharan Africa: Closing the knowledge gap.
https://www.ifpri.org - Rabobank. (2024). Tech-Enabled Farming: AI’s Role in Food Security in Africa.
https://www.rabobank.com - Stanford Economic Review. (2025). AI in Agriculture: Transforming Food Security in Sub-Saharan Africa.
https://stanfordeconreview.com - Reuters. (2024, September 25). Debate rages over push for new green revolution in Africa’s agriculture.
https://www.reuters.com