Harnessing AI For Agricultural Transformation– A Prospect Of Pakistan
“Pakistan is an agricultural country” is a phrase every Pakistani has frequently heard and read since the nation’s independence in 1947. Further, the country’s location in the fertile Indus plains, abundant arable land, and access to both ground and surface water have supported its growing demand for food. Pakistan is the 5th most populous country globally and ranks as the 41st largest economy. However, despite being an agricultural nation, a significant portion of the population still faces food security challenges.
Although Pakistan is among the top ten producers of crops like wheat, cotton, sugarcane, and rice, its per-acre yield lags behind competitors. For instance, China’s wheat yield is 5.39 tons per hectare compared to Pakistan’s 2.92 tons per hectare. The situation is worsened by the severe impacts of climate change, such as flooding, extreme weather conditions, and insufficient rainfall during critical crop growth periods. These factors, combined with existing challenges like poor crop management practices, inefficient use of fertilizers and pesticides, lack of storage facilities, and declining soil fertility, exacerbate the crisis. However, the impact of agricultural challenges can be significantly reduced with the use of technology, particularly AI.
The rapid growth of generative AI powered by large language models (LLMs) has demonstrated its potential across various fields, including production, manufacturing, and transportation. The agriculture sector now stands at a crucial point where deploying this technology is essential. AI can play a pivotal role in offering advisory services at different stages of crop production, predicting weather patterns and water availability both within and beyond farms, and optimizing value chains from farm to market. Government-led initiatives to incorporate AI into agriculture have been underway since 2018.
AI is revolutionizing agriculture, with one of the key areas being predictive analytics. This technology enables farmers to forecast crop yields, adjust planting schedules, and anticipate market trends. By analyzing historical data on soil conditions and weather patterns, AI can predict potential issues in crop production, allowing farmers to prepare for variations and minimize losses through better planning for storage and marketing.
Crop monitoring has also been improved through AI technologies like drones and satellite imagery, providing real-time surveillance of crops. This allows for early detection of diseases, pest infestations, and nutrient deficiencies, enabling targeted treatments to control the spread of disease while addressing specific crop needs. Precision agriculture, supported by sensors and algorithms, can guide farmers on the optimal use of fertilizers by analyzing soil conditions, ensuring efficient and cost-effective crop management.
AI can analyze crop images and environmental data to manage pest outbreaks, suggesting appropriate pesticide dosages or using natural predators to protect crops. This approach reduces the need for broad-spectrum pesticides, saving both money and resources. Additionally, meteorological data analyzed by AI can help farmers plan planting and harvesting activities, especially as climate change causes unexpected weather events. Accurate predictions of rainfall, thunderstorms, and heatwaves enable proactive crop protection.
Irrigation management also benefits from AI, with systems providing data-driven recommendations on watering schedules based on weather forecasts, soil analysis, and crop water needs. AI-based applications can suggest crop varieties suited to specific weather patterns and soil conditions, increasing resilience and productivity. Sensors equipped with AI can analyze soil health, identifying nutrient deficiencies, PH levels, and moisture content, helping farmers implement targeted and cost-effective soil treatments.
Cloud computing can play a significant role in storing and analyzing vast amounts of agricultural data. AI platforms utilizing cloud technology can aggregate data from multiple farms, offering insights and strategies for better farm management. The agricultural supply chain can also be transformed through AI, improving market access for farmers and strengthening their connections with buyers. By predicting market trends based on historical data, AI can help farmers plan crop production more effectively and manage storage to secure better prices.
Deploying AI in Pakistan’s agriculture sector requires a comprehensive approach. The first step is to set clear objectives and assess the areas where AI can have the greatest impact, such as crop monitoring and water management while identifying all relevant stakeholders from the public, corporate, and non-profit sectors. Defining the scope of the project and establishing a timeline for achieving these objectives is crucial for integrating AI into agriculture.
Building the necessary infrastructure is the next key step. This involves acquiring hardware such as sensors and drones, installing them, setting up cloud computing systems for data storage, and ensuring that these technologies can be seamlessly connected at the farm level. Continuous data transmission and real-time analysis are essential to fully leverage AI’s potential.
Given Pakistan’s diverse agricultural landscape, with varying crops, soils, climates, and resources, a one-size-fits-all approach is not feasible. A pilot phase under controlled conditions is advisable before any large-scale rollout. AI technologies for crop monitoring or precision agriculture should be tested in these pilot phases, with regular assessments and user feedback used to refine the system. This phased approach will help address challenges in data processing and ensure the system is functioning optimally before scaling up.
Comprehensive training programs are critical for successful adoption. Tailored programs in local languages for farmers, technicians, and other stakeholders should be developed. Interactive workshops and hands-on training sessions will help ensure that users fully understand AI technology and can use it effectively. Engaging educated youth through internships and fieldwork can also promote AI adoption.
Once training and capacity-building efforts are in place, full-scale implementation can begin, with customized software and hardware designed to meet local needs. Ongoing support is essential to ensure AI is smoothly integrated with existing agricultural practices. A continuous feedback mechanism, along with regular monitoring and evaluation, will help improve system efficiency over time.
Finally, conducting a cost-benefit analysis (CBA) is crucial to assess the overall impact of AI integration in agriculture. The costs of technology investment, training, pilot projects, human resources, maintenance, and scaling up should be weighed against the benefits, including increased productivity, cost savings, improved decision-making, and enhanced sustainability. Evaluating the return on investment (ROI) by comparing these benefits with the costs, and considering long-term gains such as improved food security and environmental sustainability, will provide a clearer picture of AI’s significance in agriculture.
The challenges faced by Pakistani farmers can be effectively addressed through AI, potentially transforming the country’s agricultural economy. By increasing per-acre productivity, optimizing resource use, and improving market insights, AI can enhance farmers’ livelihoods and financial security.
Pakistan has long been struggling with economic instability, marked by inflation, rising taxes, shifting public sector policies, and uncertainties in imports and exports, all of which hamper economic growth and affect everyday life. While AI holds undeniable potential for agriculture, its integration presents significant challenges. These include the need for rapid infrastructure development, improved connectivity in remote areas, and tailored training programs for a largely less-educated farming community. Additionally, the high initial costs of implementing AI, coupled with the need for continuous government support, make this a complex endeavor.
To ensure success, AI applications must be customized for local needs. This involves adapting software to local languages, creating user-friendly interfaces, aligning with local resources and practices, and ensuring affordability for small and medium-sized farm owners when it comes to subscriptions and service fees.
A coordinated effort between the public and private sectors, academia, and research organizations is essential for the successful integration of AI in agriculture. Government policies should be clear, well-coordinated, and aligned with the interests of both investors and farmers. Public-private partnerships can be explored to make AI-based tools and services more user-friendly and affordable. The potential impact of such efforts would be significant, leading to increased food security and economic growth in Pakistan.
The writer is an M&E officer at, the Planning Commission
