Mr. Vishal Sharma Profile

Vishal Sharma

at Indian Institute of Technology Roorkee

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 17 October 2023 Presentation + Paper

Generating mustard crop maps using planet scope high resolution dataset by applying machine learning algorithms

Vishal Sharma, Sanjay Kumar Ghosh

Proceedings Volume 12727, 1272705 (2023) https://doi.org/10.1117/12.2684661

KEYWORDS: Vegetation, Machine learning, Agriculture, Planets, Random forests, Satellites, Accuracy assessment, Remote sensing

Read Abstract +

Crop type information is crucial for several agricultural applications and even useful for different agencies to take important decision. Therefore, accurate crop mapping is required which is a simple yet critical issue in agriculture. Remote sensing has played an important role in acquiring necessary crop data. The free available Planet Scope dataset with a spatial resolution of 3m has generated new opportunities for mapping small land holding. The objective of the study is to map a Mustard field in Haridwar district, as it is considered as one of the important catch crop in the study area. Random Forest (RF) and Classification and Regression Tree (CART) algorithms of machine learning have been in this study. Further, spectral indices images of NDVI, EVI2, NDRE1 and BBI were generated from the original data set. On the temporal PlanetScope dataset, separability analysis is first carried out using the transformed divergence approach. This gives us the optimal three band combination and best time stamp for mapping mustard, which is then used in the study together with spectral indices for mapping mustard crop. Hyperparameter tuning is done to achieve high accuracy, and utilizing the optimized value of the parameters, the classification is carried out using the aforementioned powerful algorithms. The classification findings demonstrate that RF (85.78%) offers a more accurate result than CART (77.75%) in terms of total accuracy. However, both classifiers offer approximately same result in the field of agricultural. For example, RF classifies mustard with an accuracy of 93.33% while CART achieves 90.69%, while for mapping other crops, RF achieves 91% accuracy and CART achieves 84.67%. However, RF provides more precise mapping for Mustard than CART does. According to the results of the study, Random Forest produces the best outcomes when original data and spectral indices are combined.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years