Abdul Ghani


Master of Science in Computer Science


Department of Computer Science

Faculty / School

School of Mathematics and Computer Science (SMCS)

Date of Submission



Dr. Muhammad Sarim, Visiting Faculty, Department of Computer Science

Document type

MSCS Survey Report


To cope up with the food security challenges either due to climate or non-climate actors, technology can play a vital role. By the efficient use of Computer Vision, we can significantly increase crop productivity by well-informed data driven decisions, in a timely fashion. Airborne optical sensors, mainly multispectral and hyperspectral sensors, high-resolution cameras and thermal cameras, can be used to detect the anomalies in the crop, health and quality of the crop, yield estimation, disease and weed detection, soil conditions to name a few. Computer Vision forms the basis of automation in the field of agriculture and precision agriculture.

In this research work, computer vision for agriculture is discussed from very basics. Starting from the mechanisms used to acquire imagery (on-field and aerial). Platforms used to acquire imagery (satellite, manned and unmanned aerial vehicles, fixed wing and rotary wing drones). Processes related to data acquisition, data processing and data analysis for agriculture. Different types of sensors used for computer vision in the field of agriculture.

Significance of data obtained from these sensors and how we can utilize this data to get actionable insights about the field. How image processing and machine learning plays a vital role in decision making. Last, but not the least, traditional operations in the field of agriculture (the traditional way of doing things) and suggested ways of doing these on day to day basis and long-term operations using computer vision are discussed for better understanding of the whole procedure.

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