AGRICULTURE SECTOR
PRECISION AGRI REVOLUTION
Akila Wijerathna explains how AI and other technologies can benefit farming
Precision agriculture (PA) is a revolutionary approach to farming as it optimises agricultural processes by leveraging advanced technologies to monitor and manage resources effectively.
It addresses critical challenges such as increasing the demand for food, climate change and resource scarcity by fostering sustainable farming practices.
As subsets of advanced computing, AI and machine learning (ML) enable data driven decision making, automation and improved efficiency. The transformative potential of these lies in their ability to analyse vast datasets, predict outcomes and optimise farming practices.
Artificial intelligence and ML can process complex datasets collected from sensors, drones and satellite imagery. They can uncover patterns and insights that traditional methods often overlook, and enable farmers to make informed decisions about crop management, resource allocation and environmental sustainability.
This has revolutionised crop management by making way for precise monitoring and prediction.
For instance, computer vision algorithms analyse images captured by drones to detect diseases, pests and nutrient deficiencies at an early stage.
Such insights enable timely intervention, reduce crop losses and deliver healthy yields. Yield prediction models leverage historical and real-time data – which includes weather conditions, soil health and crop performance – to forecast harvest outcomes.
Soil health is fundamental to agricultural productivity, and AI powered tools play a crucial role in its assessment. Sensors equipped with these capabilities measure parameters such as pH, moisture and nutrient levels to provide actionable insights.
ML models analyse this data to recommend optimal fertilisation and soil treatment strategies. Artificial intelligence also aids in soil mapping by identifying variations across fields.
Efficient water usage is a critical aspect of sustainable farming. AI driven irrigation systems optimise water distribution based on real-time data from weather forecasts and soil moisture sensors.
These systems ensure that crops receive adequate water while minimising wastage and addressing water scarcity concerns. Predictive models also help farmers anticipate water requirements by enabling proactive management.
By analysing historical and environmental data for example, machine learning algorithms can forecast drought conditions and suggest adaptive strategies.
AI and machine learning are transforming livestock farming, by automating routine tasks and improving animal welfare. Sensors and cameras equipped with artificial intelligence detect changes in animal behaviour that could indicate health issues or stress. This early detection enables prompt intervention; it also reduces morbidity and mortality rates.
Automated feeding systems analyse animal specific data to determine optimal feeding schedules and portions, improve productivity and reduce feed wastage. ML models also predict productivity metrics such as milk yield and assist farmers in optimising operations.
Accurate weather forecasting is vital for agricultural planning. AI powered models analyse vast datasets from meteorological sources to provide reliable predictions. These forecasts help farmers schedule planting, irrigation and harvesting, and mitigate risks associated with adverse weather conditions.
As climate change intensifies, AI and ML also facilitate adaptation strategies. For example, algorithms identify climate resilient crop varieties and recommend changes in farming practices to cope with environmental conditions.
However, the efficient adoption of artificial intelligence in agriculture is hindered by high implementation costs and the complexity of these systems. Since many farmers lack the technical expertise required to operate and maintain AI tools, user-friendly designs and training programmes need to be created.
AI systems rely heavily on high quality data for accurate predictions. But the lack of standardised data collection methods and incomplete datasets often limit their effectiveness.
Smallholder farmers – who form the majority in many regions – face serious challenges in accessing AI technologies due to financial constraints and limited awareness. Bridging this gap requires targeted initiatives such as subsidies and community based training.
Government support and regulatory frameworks play a crucial role in fostering AI adoption. Policies must address issues such as affordability, accessibility and data ethics to ensure the availability of equitable benefits. Ethical considerations include the responsible use of AI to prevent exploitation and ensure environmental sustainability.
Stakeholders must collaborate to ensure that AI driven agriculture is inclusive, sustainable and accessible to all. Investing in these technologies today is essential to securing the future of global food systems.