International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 08 | Aug 2023
p-ISSN: 2395-0072
www.irjet.net
Identification, Discrimination and Classification of Cotton Crop by Using Multispectral imagery using complete enumeration approach over Haryana state Om Pal1, Hemraj1 1Haryana Space Application Centre, Hisar, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The accurate and timely crop production
Multispectral and hyperspectral data, as well as vegetation indices such as NDVI (Normalized Difference Vegetation Index), have been widely utilized in crop classification studies [1], [2].
forecast plays a pivotal role in management for taking the decision related to import, export etc. by the policy makers which may be very helpful for enhancing productivity, resource allocation, and sustainable land management. The crop production forecast comprises mainly crop area and yield assessment. In recent years, crop classification and area assessment using remote sensing and GIS techniques have gained prominence due to their non-invasive and cost-efficient nature. The use of multi-spectral data operating in the visible and near infrared region of electromagnetic spectrum is widely used for the crop area, health and condition assessment. This research paper presents an in-depth exploration of cotton crop classification and area estimation using single date multi-spectral imagery by employing complete enumeration approach in major cotton growing districts of Haryana. The study investigates the potential of optical data for accurate cotton crop identification and monitoring. The paper discusses the methodology, data acquisition, feature extraction, classification algorithms, and showcasing their applicability in different agricultural contexts. Through an extensive review of existing literature and empirical analysis, the study suggests the implication of supervised classification technique and the role of remote sensing and GIS based technology in enhancing agricultural crop management.
Machine learning algorithms have shown remarkable success in analysing optical data for crop classification. Maximum Likelihood, Convolutional Neural Networks (CNNs), Random Forest, Support Vector Machines (SVMs), and k-Nearest Neighbors (k-NN) are commonly employed for feature extraction and classification [3], [4]. Accurate identification of cotton growth stages is crucial for timely crop assessment. Optical data can assist in distinguishing between stages such as planting, emergence, flowering, boll development, and senescence. Machine learning models trained on annotated datasets can accurately classify these stages [5]. Early detection of pest infestations and diseases is vital for preventing yield losses. Optical data can reveal stress indicators and anomalies in cotton fields. Integration of spectral data with machine learning algorithms enables the assessment of health conditions, facilitating targeted treatment [6]. Geographical information system and satellite-based imageries are helpful for identification and demarcation of cotton crop from other associated land cover features thus capable of generating the crop map which is used as basic input for further crop health and yield assessment. This study demonstrates the potential of using high resolution imagery of Sentinel-2 for the identification, discrimination and demarcation of cotton crop from other associated land cover classes.
Key Words: Agriculture, Cotton crop, Optical data, Supervised Classification, GIS, Area estimation
1. INTRODUCTION Cotton, a vital cash crop, plays a crucial role in the global economy by providing raw material for the textile industry. Temporal monitoring the growth stages and crop health assessment of cotton crop is essential for optimizing yield and resource utilization. Traditional methods of crop assessment using field survey is labour-intensive and often may be lacking of accuracy due to human intervention. In recent years, remote sensing technologies coupled with advanced machine learning techniques have emerged as powerful tools for crop classification and monitoring. Optical data from satellite and drone-based sensors provide valuable information about the spectral reflectance of crops. These temporal data sources offer insights into various physiological and biochemical changes within the plants.
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2. STUDY AREA AND DATA 2.1 Study Area The study area is situated in the extreme west and southern part of Haryana state consisting of major cotton growing districts viz., Bhiwani, Charkhi Dadri, Fatehabad, Hisar, Jind and Sirsa (Figure 1) and covering total geographical area around 18,278 Kilometre square. The study area lies between the 27 degree 37' to 30 degree 35' latitude and between 74 degree 28' to 77 degree 36'’ longitudes. The mean elevation of the area is 190 to 210 above sea level with annual precipitation of 32-53 mm. The climate of study area is semi-
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