GIS/GPS based Precision Agriculture Model in India –A Case study

2018 ◽  
Vol 10 (2) ◽  
pp. 1-7
Author(s):  
Suresh Kumar Mudda ◽  
Keyword(s):  
Precision Agriculture

scholarly journals Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning—A Case Study of Bale Detection

2020 ◽  
Vol 13 (1) ◽  
pp. 23
Author(s):  
Wei Zhao ◽  
William Yamada ◽  
Tianxin Li ◽  
Matthew Digman ◽  
Troy Runge
Keyword(s):  
Object Detection ◽  
Transfer Learning ◽  
Precision Agriculture ◽  
Crop Production ◽  
Domain Adaptation ◽  
Training Data ◽  
Detection Accuracy ◽  
Detection Model ◽  
Agriculture Products

In recent years, precision agriculture has been researched to increase crop production with less inputs, as a promising means to meet the growing demand of agriculture products. Computer vision-based crop detection with unmanned aerial vehicle (UAV)-acquired images is a critical tool for precision agriculture. However, object detection using deep learning algorithms rely on a significant amount of manually prelabeled training datasets as ground truths. Field object detection, such as bales, is especially difficult because of (1) long-period image acquisitions under different illumination conditions and seasons; (2) limited existing prelabeled data; and (3) few pretrained models and research as references. This work increases the bale detection accuracy based on limited data collection and labeling, by building an innovative algorithms pipeline. First, an object detection model is trained using 243 images captured with good illimitation conditions in fall from the crop lands. In addition, domain adaptation (DA), a kind of transfer learning, is applied for synthesizing the training data under diverse environmental conditions with automatic labels. Finally, the object detection model is optimized with the synthesized datasets. The case study shows the proposed method improves the bale detecting performance, including the recall, mean average precision (mAP), and F measure (F1 score), from averages of 0.59, 0.7, and 0.7 (the object detection) to averages of 0.93, 0.94, and 0.89 (the object detection + DA), respectively. This approach could be easily scaled to many other crop field objects and will significantly contribute to precision agriculture.

scholarly journals Use of multi-spectral visible and near-infrared satellite data for timely estimates of the Earth's surface reflectance in cloudy conditions: Part 2 - image restoration with HICO satellite data in overcast conditions

2021 ◽  
Author(s):  
Joanna Joiner ◽  
Zachary Fasnacht ◽  
Bo-Cai Gao ◽  
Wenhan Qin
Keyword(s):  
Spatial Resolution ◽  
Satellite Data ◽  
Precision Agriculture ◽  
Land Surface ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Space Station ◽  
Water Quality Assessment ◽  
Low Contrast

Satellite-based visible and near-infrared imaging of the Earth's surface is generally not performed in moderate to highly cloudy conditions; images that look visibly cloud covered to the human eye are typically discarded. Here, we expand upon previous work that employed machine learning (ML) to estimate underlying land surface reflectances at red, green, and blue (RGB) wavelengths in cloud contaminated spectra using a low spatial resolution satellite spectrometer. Specifically, we apply the ML methodology to a case study at much higher spatial resolution with the Hyperspectral Imager for the Coastal Ocean (HICO) that flew on the International Space Station (ISS). HICO spatial sampling is of the order of 90 m. The purpose of our case study is to test whether high spatial resolution features can be captured using multi-spectral imaging in lightly cloudy and overcast conditions. We selected one clear and one cloudy image over a portion ofthe panhandle coastline of Florida to demonstrate that land features are partially recoverable in overcast conditions. Many high contrast features are well recovered in the presence of optically thin clouds. However, some of the low contrast features, such as narrow roads, are smeared out in the heavily clouded part of the reconstructed image. This case study demonstrates that our approach may be useful for many science and applications that are being developed for current and upcoming satellite missions including precision agriculture and natural vegetation analysis, water quality assessment as well as disturbance, change, hazard, and disaster detection.

scholarly journals A Case Study about the Use of Precision Agriculture Technology Applied to a Zn Biofortification Workflow for Grapevine Vitis vinifera cv Moscatel

2021 ◽  
Vol 3 (1) ◽  
pp. 2
Author(s):  
Diana Daccak ◽  
Inês Carmo Luís ◽  
Ana Coelho Marques ◽  
Ana Rita F. Coelho ◽  
Cláudia Campos Pessoa ◽  
...  
Keyword(s):  
Vitis Vinifera ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Foliar Application ◽  
Fold Increase ◽  
Production Cycle ◽  
Smart Farming ◽  
Reduced Surface

As the human population is growing worldwide, the food demand is sharply increasing. Following this assumption, strategies to enhance the food production are being explored, namely, smart farming, for monitoring crops during the production cycle. In this study, a vineyard of Vitis vinifera cv. Moscatel located in Palmela (N 38°35′47.113′′ O 8°40′46.651) was submitted to a Zn biofortification workflow, through foliar application of zinc oxide (ZnO) or zinc sulfate (ZnSO4) (at a concentration of 60% and 90%—900 g·ha−1 and 1350 g·ha−1, respectively). The field morphology and vigor of the vineyard was performed through Unmanned Aerial Vehicles (UAVs) images (assessed with altimetric measurement sensors), synchronized by GPS. Drainage capacity and slopes showed one-third of the field with reduced surface drainage and a maximum variation of 0.80 m between the extremes (almost flat), respectively. The NDVI (Normalized Difference Vegetation Index) values reflected a greater vigor in treated grapes with treatment SZn90 showing a higher value. These data were interpolated with mineral content, monitored with atomic absorption analysis (showing a 1.3-fold increase for the biofortification index). It was concluded that the used technologies furnishes specific target information in real time about the crops production.

Precision conservation meets precision agriculture: A case study from southern Ontario

2018 ◽  
Vol 167 ◽  
pp. 176-185 ◽  
Author(s):  
Virginia Capmourteres ◽  
Justin Adams ◽  
Aaron Berg ◽  
Evan Fraser ◽  
Clarence Swanton ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Southern Ontario ◽  
Precision Conservation

scholarly journals T-MQM: Testbed based Multi-metric Quality Measurement of Sensor Deployment for Precision Agriculture-A Case Study

2016 ◽  
pp. 1-1 ◽  
Author(s):  
omprakash kaiwartya ◽  
Abdul Abdullah ◽  
Yue Cao ◽  
Ram Shringar Rao ◽  
Sushil Kumar ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Quality Measurement ◽  
Sensor Deployment

Architecting an IoT-enabled platform for precision agriculture and ecological monitoring: A case study

2017 ◽  
Vol 140 ◽  
pp. 255-265 ◽  
Author(s):  
Tomo Popović ◽  
Nedeljko Latinović ◽  
Ana Pešić ◽  
Žarko Zečević ◽  
Božo Krstajić ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Ecological Monitoring

scholarly journals LAND COVER CLASSIFICATION USING A UAV-BORNE SPECTROMETER

2017 ◽  
Vol XLII-2/W6 ◽  
pp. 269-273 ◽  
Author(s):  
S. Natesan ◽  
G. Benari ◽  
C. Armenakis ◽  
R. Lee
Keyword(s):  
Land Cover ◽  
Precision Agriculture ◽  
Visible Spectrum ◽  
Land Cover Classification ◽  
Ground Point ◽  
Aerial Vehicle ◽  
Preliminary Study ◽  
Low Altitude

Small fixed wing and rotor-copter unmanned aerial vehicles (UAV) are being used for low altitude remote sensing for thematic land classification and precision agriculture applications. Various sensors operating in the non-visible spectrum such as multispectral, hyperspectral and thermal sensors can be used as payloads. This work presents a preliminary study on the use of unmanned aerial vehicle equipped with a compact spectrometer for land cover type characterization. When calibrated, the measured spectra by the UAV spectrometer can be processed and compared reference data to generate georeferenced reflection spectra enabling the identification, classification and characterization of land cover elements. For this case study we used a DJI Flamewheel F550 hexacopter and the FLAME-NIR spectrometer for hyperspectral measurements. The calibration of the spectrometer is described as well the approach to determine its spatial footprint. The spectrometer spectral exposure labeled ground point can be used to determine the land cover classification. Preliminary results of a case-study are presented.

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