Abstract:
Mapping crop types and analyzing their characteristics has been a long-standing practice to
ensure food security, utilize land effectively, and shape agricultural policy. This has become a
crucial task for many nations due to its impact on economics of the country, trade, and global
food security. This topic is widely study in policy making, land management and economics.
Most of the countries are now utilizing satellite imagery, which offers a comprehensive view,
multiple time coverage, and cost efficiency, for crop identification and analysis. Various
methods have been tried to gather crops information but now satellite data with its improved
accuracy and resolution used to generate crop type maps. Remote sensing techniques using
optical sensors have become a significant tool for understanding vegetation structure and
properties. This study focuses on using optical imagery from Sentinel-2 to map crop types. The
study tells how to use sentinel 2 imagery, pre-processing, and analysis of Sentinel-2 data using
the ArcMap, ERDAS Imagine and R code in RStudio. The thesis outlines a plan for categorizing
crops using data collection techniques, machine learning algorithms, and validation methods to
achieve accurate results on a nationwide scale.
We used a machine learning RF classification algorithm and SVM to create accurate crop type
maps using (S2) data for growing season of Rabi and Kharif in pothohar region which consists
of five districts Attock, Jehlum, Rawalpindi, Islamabad, and Chakwal. The algorithms have
shown improved accuracy in previous studies by using sample data and ensemble decision trees.
The purpose of the study is to compare the precision of crop classification between Support
Vector Machine (SVM) and Random Forest (RF) algorithms.
The overall accuracy of Rabi maps for SVM is 96.49% and for RF 91.22% with kappa
coefficient 0.94 and 0.86 respectively. The overall accuracy of Kharif maps for support vector
machine is 95.38% and for RF 89.23% with kappa coefficient 0.93 and 0.84 respectively.
The study compares the two crop classification methods. These results show that with point
training data SVM results in better accuracy than random forest. The study found that the
machine learning methods (RF & SVM) showed improved robustness in solving multiclassification problems, achieving a user accuracy of greater than 90% in SVM and more than
85% in RF algorithm for each crop type.
