Jakarta, INTI – Amid the sweeping digital revolution in various sectors, a quiet yet powerful change is also unfolding in Indonesia’s farmlands. Not with flashy high-tech devices, but through the gradual integration of Artificial Intelligence (AI) into the lives of small-scale farmers. One real-life example can be seen at SMKN Pertanian Pembangunan (PP) Lembang in West Java, where students use smartphones to operate an automatic irrigation system powered by Internet of Things (IoT) technology. This is a glimpse into the rise of smart farming among the younger generation and in Indonesia’s agricultural landscape.
In recent years, AI has emerged not to replace humans, but as a support tool offering efficiency, knowledge access, and more resilient food systems.
From Large-Scale Farms to Smallholders
AI-based agricultural technologies have long been adopted on large farms in developed countries. Innovations like the LaserWeeder G2 and John Deere’s “See & Spray” system are prime examples. Using computer vision and machine learning algorithms, these systems can differentiate between crops and weeds, then act with precision either by targeting with lasers or selective herbicide spraying.
According to a 2024 study by the PrecisionAg Alliance, this system reduced herbicide use by up to 77% on soybean and corn farms in the U.S. But what's even more compelling is how similar technologies are now being adapted to benefit small-scale farmers, especially in developing countries.
AI in the Hands of Small Farmers
In India, the AI4AI (Artificial Intelligence for Agriculture Innovation) project by Microsoft and ICRISAT provides a simple yet impactful solution. Farmers can send a photo of their plant’s leaves via WhatsApp, and the AI system will diagnose the disease and recommend treatment based on local agronomic data.
The World Economic Forum reported that this approach has increased productivity by up to 30% while significantly reducing crop losses due to plant diseases.
Indonesia has great potential to adopt such a model. With 62.14% of farmers categorized as smallholders (owning less than 0.5 hectares of land), AI could revolutionize their access to information, markets, and agricultural extension services.
Overcoming Limitations Through Contextual Innovation
A 2023 report from CIFOR-ICRAF highlights how AI, when combined with local weather data, soil sensors, and satellite imagery, can help farmers respond to climate change more effectively. A similar approach is already being tested in South Sulawesi, in collaboration with local agri-tech startups and BMKG (Indonesia’s Meteorology Agency).
However, the success of such technology depends not only on the tools but also on ecosystem readiness. Currently, only 10.8% of rice farmers in Indonesia use the internet, while the rest remain offline. This means AI must be deployed through community-based approaches, ongoing training, and inclusive policy interventions.
Data Protection and Governance Are Key
AI systems rely heavily on farmers’ data ranging from harvest yields and planting patterns to land location. Without proper regulation, this data could become a commodity accessed or exploited by third parties without farmers’ consent. The government must therefore establish strong policies on agricultural data protection, similar to the EU's GDPR for smart farming.
Farmers as the Central Actors
The digital transformation of agriculture is not just about automation it’s about empowerment. AI should serve as a tool for agricultural advisors and farmers, not replace their roles. Training programs by organizations like FAO and IRRI in several ASEAN countries serve as excellent models, using participatory and problem-based learning methods in field schools.
Local extension workers who understand AI must also be equipped to verify and interpret AI-generated outputs within each region.
Inclusive Innovation Ecosystems
Indonesia has a wealth of young tech talent. Innovation competitions in AI-powered agri-tech, organized by BRIN, the Ministry of Agriculture, and universities, could incubate practical solutions. For example, a generative AI chatbot called “Tanya Tani” (Ask the Farmer), which can answer questions in local languages like Javanese, Sundanese, or Buginese, could help accelerate digital transformation in rural areas.
By turning villages into innovation labs, Indonesia’s agriculture can leap forward without needing to fully mimic models from developed countries.
Conclusion
AI in agriculture isn’t just about robots and automation. It’s about giving smallholder farmers access to better, faster, and more affordable information. It’s about enabling smarter decisions on planting, harvesting, and processing. More importantly, it’s about building a future for agriculture that is resilient, fair, and deeply human.
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