Design of FINFET based DRAM cell for low power applications

Jyothi, Grande Naga; Gorantla, Anusha; Kumar, Nagendra; Kundu, Debanjan

doi:10.1201/9780429340710-5

Cited by 2 publications

(2 citation statements)

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“…This mission specializes in broad range applications like agriculture monitoring, land cover classification and water use and it's quality. These satellites were manufactured by consortium led by Airbus defence and space [7].…”

Section: Data Acquisitionmentioning

confidence: 99%

“…In this study we propose a method which is NDWI based canny edge detection which helps in automatic detection of crop field boundaries. Normally, traditional edge detection techniques make use of spatial information of an image but this approach makes use of both spatial and spectral information [5][6][7]. In this study the identification of crop field boundaries is performed in unknown fields with minimal amount of prior information.…”

Section: Introductionmentioning

confidence: 99%

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Crop Field Boundary Detection and Classification using Machine Learning

Mylapalli

2024

SCPE

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Crop classification and detection of crop field boundaries empower farmers which helps the agricultural businesses to estimate crop field dimensions and yields accurately. Our research focuses on estimating crucial agricultural inputs such as seeds, pesticides, insecticides, and fertilizers to enhance overall production. The conventional method of manually identifying field boundaries is both time-consuming with labour-intensive. In contrast, our study harnesses data from diverse satellites such as Sentinel, Landsat, and MODIS, encompassing valuable land usage information. By integrating this data with machine learning algorithms, we achieve real-time monitoring of crop fields through effective classification and boundary identification. For the classification of crop fields within our study area, we recommend employing the Classification and Regression Tree (CART) algorithm. Additionally, we leverage normalized difference indices, such as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), as features for classification. We compare these features with Support Vector Machine (SVM) and Random Forest (RF) algorithms. Subsequently, we utilize the Canny edge detection technique to identify boundaries within the classified crop areas. Notably, our approach utilizes the Google Earth Engine (GEE) as a primary platform for extracting features, conducting data training, and visualizing information. The proposed algorithm yields impressive results with a high level of accuracy. Notably, the CART algorithm achieves a remarkable accuracy rate of 96.1%. Furthermore, we incorporate NDWI-based Canny edge detection into our methodology. The outcomes convincingly underscore the practicality and applicability of our research in real-world scenarios.

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Section: Data Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%