Performance Evaluation of RF and SVM for Sugarcane Classification Using Sentinel-2 NDVI Time-Series

Virnodkar, Shyamal; Pachghare, Vinod; Patil, V. C.; Jha, Sunil Kumar

doi:10.1007/978-981-15-6353-9_15

Cited by 8 publications

(8 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, we can conclude that in terms of accuracy assessment, the RF classifier performs slighter better than SVM. This result also agrees with other studies [ 42 , 43 , 44 , 45 ].…”

Section: Discussionsupporting

confidence: 94%

“…Recognizing land uses by land administration is a major funding source such as tax, stamp duty on property transfers, etc. [ 43 ]. Derived irrigated crop types maps can be utilized by regional land administration offices to monitor the spatial extent of crops location and its monitoring as well as modeling and predicting crop yields and production by different models.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Irrigated Crop Types Mapping in Tashkent Province of Uzbekistan with Remote Sensing-Based Classification Methods

Erdanaev

Kappas

Wyss

2022

Sensors

View full text Add to dashboard Cite

Appropriate crop type mapping to monitor and control land management is very important in developing countries. It can be very useful where digital cadaster maps are not available or usage of Remote Sensing (RS) data is not utilized in the process of monitoring and inventory. The main goal of the present research is to compare and assess the importance of optical RS data in crop type classification using medium and high spatial resolution RS imagery in 2018. With this goal, Landsat 8 (L8) and Sentinel-2 (S2) data were acquired over the Tashkent Province between the crop growth period of May and October. In addition, this period is the only possible time for having cloud-free satellite images. The following four indices “Normalized Difference Vegetation Index” (NDVI), “Enhanced Vegetation Index” (EVI), and “Normalized Difference Water Index” (NDWI1 and NDWI2) were calculated using blue, red, near-infrared, shortwave infrared 1, and shortwave infrared 2 bands. Support-Vector-Machine (SVM) and Random Forest (RF) classification methods were used to generate the main crop type maps. As a result, the Overall Accuracy (OA) of all indices was above 84% and the highest OA of 92% was achieved together with EVI-NDVI and the RF method of L8 sensor data. The highest Kappa Accuracy (KA) was found with the RF method of L8 data when EVI (KA of 88%) and EVI-NDVI (KA of 87%) indices were used. A comparison of the classified crop type area with Official State Statistics (OSS) data about sown crops area demonstrated that the smallest absolute weighted average (WA) value difference (0.2 thousand ha) was obtained using EVI-NDVI with RF method and NDVI with SVM method of L8 sensor data. For S2-sensor data, the smallest absolute value difference result (0.1 thousand ha) was obtained using EVI with RF method and 0.4 thousand ha using NDVI with SVM method. Therefore, it can be concluded that the results demonstrate new opportunities in the joint use of Landsat and Sentinel data in the future to capture high temporal resolution during the vegetation growth period for crop type mapping. We believe that the joint use of S2 and L8 data enables the separation of crop types and increases the classification accuracy.

show abstract

“…However, we can conclude that in terms of accuracy assessment, the RF classifier performs slighter better than SVM. This result also agrees with other studies [ 42 , 43 , 44 , 45 ].…”

Section: Discussionsupporting

confidence: 94%

Section: Discussionmentioning

confidence: 99%

Irrigated Crop Types Mapping in Tashkent Province of Uzbekistan with Remote Sensing-Based Classification Methods

Erdanaev

Kappas

Wyss

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Recent years bring amelioration in deep learning (DL) methods (deep neural networks), especially convolutional neural networks (CNNs). CNN gained popularity because of achieving state-of-the-art results in many computer vision tasks such as image classification [2][3][4], scene recognition [5], image annotation and captioning [6,7], handwritten digits classification, object detection [8], and semantic segmentation [9]. A review has been presented by Singh [10] on biotic and abiotic stress detection using CNN models such as AlexNet, ZFNet, GoogLeNet, VGGNet, ExceptionNet, InceptionNet, and ResNet in various fruits and vegetables.…”

Section: Literature Surveymentioning

confidence: 99%

DenseResUNet: An Architecture to Assess Water-Stressed Sugarcane Crops from Sentinel-2 Satellite Imagery

Virnodkar¹,

Pachghare²,

Patil³

et al. 2021

Self Cite

View full text Add to dashboard Cite

A single most immense abiotic stress globally affecting the productivity of all the crops is water stress. Hence, timely and accurate detection of the water-stressed crops is a necessary task for high productivity. Agricultural crop production can be managed and enhanced by spatial and temporal evaluation of water-stressed crops through remotely sensed data. However, detecting water-stressed crops from remote sensing images is a challenging task as various factors impacting spectral bands, vegetation indices (VIs) at the canopy and landscape scales, as well as the fact that the water stress detection threshold is crop-specific, there has yet to be substantial agreement on their usage as a pre-visual signal of water stress. This research takes the benefits of freely available remote sensing data and convolutional neural networks to perform semantic segmentation of water-stressed sugarcane crops. Here an architecture ‘DenseResUNet’ is proposed for water-stressed sugarcane crops using segmentation based on encoder-decoder approach. The novelty of the proposed approach lies in the replacement of classical convolution operation in the UNet with the dense block. The layers of a dense block are residual modules with a dense connection. The proposed model achieved 61.91% mIoU, and 80.53% accuracy on segmenting the water-stressed sugarcane fields. This study compares the proposed architecture with the UNet, ResUNet, and DenseUNet models achieving mIoU of 32.20%, 58.34%, and 53.15%, respectively. The results of this study reveal that the model has the potential to identify water-stressed crops from remotely sensed data through deep learning techniques.

show abstract

“…The classification methods of support vector machine (SVM), Maximum Likelihood Classification (MLC), random forest (RF), K-Nearest Neighbor (KNN), and Decision Tree (DT) have been demonstrated to effectively distinguish various plant types in remote sensing data [8,9]. Virnodkar et al [10] employed the random forest and support vector machine classification methods to categorize seven distinct classes, including sugarcane, maize, bare land, and buildings. This classification was based on the analysis of the Normalized Difference Vegetation Index (NDVI) and Sentinel-2 remotely sensed data.…”

Section: Introductionmentioning

confidence: 99%

“…The findings indicated that the support vector machine achieved a producer accuracy of 81.68%, while the random forest algorithm achieved a higher producer accuracy of 96.58%. Previous research has demonstrated that random forests exhibit strong performance in the classification of crops [10,[14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Research on Maize Acreage Extraction and Growth Monitoring Based on a Machine Learning Algorithm and Multi-Source Remote Sensing Data

Luan,

Shen,

et al. 2023

Sustainability

View full text Add to dashboard Cite

Getting accurate and up-to-date information on the cultivated land area and spatial arrangement of maize, an important staple crop in the Ningxia Hui Autonomous Region, is very important for planning agricultural development in the region and judging crop yields. This work proposes a machine-learning methodology to extract corn from medium-resolution photos obtained from the Sentinel-2 satellite. The Google Earth Engine (GEE) cloud platform is utilized to facilitate the process. The identification of maize cultivation regions in Huinong District in the year 2021 was performed through the utilization of support vector machine (SVM) and random forest (RF) classification techniques. After obtaining the results, they were compared to see if using the random forest classification method to find planting areas for maize was possible and useful. Subsequently, the regions where maize was cultivated were combined with image data from the Moderate Resolution Imaging Spectroradiometer (MODIS), which has a high temporal resolution. The Normalized Difference Vegetation Index (NDVI) contemporaneous difference method, which gives regular updates, was then used to track the growth of maize during its whole growth phase. The study’s results show that using the GEE cloud platform made it easier to quickly map out data about where to plant maize in Huinong District. Furthermore, the implementation of the random forest method resulted in enhanced accuracy in extracting maize planting areas. The confusion matrix’s evaluation of the classification performance produced an average overall accuracy of 98.9% and an average Kappa coefficient of 0.966. In comparison to the statistics yearbook of the Ningxia Hui Autonomous Region, the method employed in this study consistently yielded maize-planted area estimates in Huinong District with relative errors below 4% throughout the period spanning 2017 to 2021. The average relative error was found to be 2.04%. By combining MODIS image data with the NDVI difference model in the year 2021, the high-frequency monitoring of maize growth in Huinong District was successful. The growth of maize in Huinong District in 2021 exhibited comparable or improved performance in the seedling stage, nodulation stage, and the early stage of staminate pulling and spitting, possibly attributed to the impact of climate and other relevant elements. After that, the growth slowed down in August, and the percentage of regions with slower growth rates than in previous years gradually increased. However, overall, the growth of maize in Huinong District during the year 2021 showed improvement relative to the preceding years. The present study introduces a novel approach that demonstrates the capability to accurately extract corn crops in the Huinong District while simultaneously monitoring their growth at a high frequency.

show abstract

Performance Evaluation of RF and SVM for Sugarcane Classification Using Sentinel-2 NDVI Time-Series

Cited by 8 publications

References 29 publications

Irrigated Crop Types Mapping in Tashkent Province of Uzbekistan with Remote Sensing-Based Classification Methods

Irrigated Crop Types Mapping in Tashkent Province of Uzbekistan with Remote Sensing-Based Classification Methods

DenseResUNet: An Architecture to Assess Water-Stressed Sugarcane Crops from Sentinel-2 Satellite Imagery

Research on Maize Acreage Extraction and Growth Monitoring Based on a Machine Learning Algorithm and Multi-Source Remote Sensing Data

Contact Info

Product

Resources

About