The Government of Pakistan is committed to enhancing agriculture productivity. It has initiated Prime Minister’s agriculture emergency program, which primarily focuses on, i) productivity enhancement of wheat, rice & sugarcane, ii) oilseeds enhancement program, iii) conserving water through the lining of watercourses, iv) enhancing command area of small and mini dams in pluvious areas, v) water conservation in rainy areas of Khyber Pakhtunkhwa. Getting benefit from such initiatives, proposing solutions to these and upcoming projects, and finding and materializing new and innovative ideas is the key objective of establishing a smart agriculture laboratory.

After the green revolution, in which technology transfer in the agriculture sector started to begin (the 1950s and 1960s), the agriculture sector’s new methods and methodologies increased crop productivity and high yield production, specifically in the developing world. Moreover, due to advancements in the IT sector, there have been significant developments in other industries. Agriculture is one of them that witnessed the direct impact of AI and the IoT. This laboratory aims to provide customized solutions to local farmers using precision agriculture, smart irrigation, drone technology, predictive analysis, insect monitoring, advisory services, hydroponics farming, and robots to improve the agriculture production landscape. Further, this laboratory will also focus on monitoring irrigation canals and suggesting new link canals using AI-based models in collaboration with the smart city and urban planning laboratory.

• Developing information mapping systems and control systems for more precise in-field crop planting and chemical application management. 
• Water quality testing to identify contaminants of interest include sediment, nutrients, pathogens, and chemicals. 
• Remote sensing for data processing from satellites, planes, and ground-based vehicles leads to both map-based and real-time control of crop planting and chemical application. 
•  Machine learning techniques to take field data to analyze crop performance in various climates and new characteristics developed in the process. 
• Using machine learning for accurate and faster results analysing the leaf vein morphology carries more information about the leaf properties.  
• Machine learning algorithms to study evaporation processes, soil moisture, and temperature to understand the dynamics of ecosystems and the impingement in agriculture. 
•  ML-based applications for effective use of irrigation systems and prediction of daily dew point temperature, which helps identify expected weather phenomena and estimate evapotranspiration and evaporation. 
• Crop management 
• Yield prediction 
• Crop quality 
• Disease and weed detection 
• Livestock management 
• Livestock Production (Weight predicting systems can estimate the future weights 150 days prior to the slaughter day, allowing farmers to modify diets and conditions, respectively) 
• Farmer’s Little Helper (development of specialized chatbots that would be able to converse with farmers and provide them with valuable facts and analytics)