Computer Vision and Machine Learning based approaches for Food Security: A Review

2021 ◽  
Author(s):  
Shivani Sood ◽  
Harjeet Singh
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Food Security

USING SATELLITES AND MACHINE LEARNING TO ENHANCE AND PROTECT FOOD SECURITY IN AFRICA

2020 ◽  
Author(s):  
Catherine Nakalembe ◽  
◽  
Inbal Becker-Reshef ◽  
Hannah Kerner ◽  
Ritvik Sahajpal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Food Security

scholarly journals A Systematic Survey of ML Datasets for Prime CV Research Areas—Media and Metadata

Data ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 12
Author(s):  
Helder F. Castro ◽  
Jaime S. Cardoso ◽  
Maria T. Andrade
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Qualitative Analysis ◽  
Continuous Growth ◽  
Scientific Databases ◽  
Extensive Survey ◽  
Research Areas ◽  
Current State ◽  
Global View ◽  
Cooperative Production

The ever-growing capabilities of computers have enabled pursuing Computer Vision through Machine Learning (i.e., MLCV). ML tools require large amounts of information to learn from (ML datasets). These are costly to produce but have received reduced attention regarding standardization. This prevents the cooperative production and exploitation of these resources, impedes countless synergies, and hinders ML research. No global view exists of the MLCV dataset tissue. Acquiring it is fundamental to enable standardization. We provide an extensive survey of the evolution and current state of MLCV datasets (1994 to 2019) for a set of specific CV areas as well as a quantitative and qualitative analysis of the results. Data were gathered from online scientific databases (e.g., Google Scholar, CiteSeerX). We reveal the heterogeneous plethora that comprises the MLCV dataset tissue; their continuous growth in volume and complexity; the specificities of the evolution of their media and metadata components regarding a range of aspects; and that MLCV progress requires the construction of a global standardized (structuring, manipulating, and sharing) MLCV “library”. Accordingly, we formulate a novel interpretation of this dataset collective as a global tissue of synthetic cognitive visual memories and define the immediately necessary steps to advance its standardization and integration.

Comparing established visitor monitoring approaches with triggered trail camera images and machine learning based computer vision

2021 ◽  
Vol 35 ◽  
pp. 100387
Author(s):  
Jeroen Staab ◽  
Erica Udas ◽  
Marius Mayer ◽  
Hannes Taubenböck ◽  
Hubert Job
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Trail Camera

scholarly journals Delineating Smallholder Maize Farms from Sentinel-1 Coupled with Sentinel-2 Data Using Machine Learning

2021 ◽  
Vol 13 (9) ◽  
pp. 4728
Author(s):  
Zinhle Mashaba-Munghemezulu ◽  
George Johannes Chirima ◽  
Cilence Munghemezulu
Keyword(s):  
Machine Learning ◽  
Food Security ◽  
Rural Communities ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Subsistence Agriculture ◽  
Smallholder Farms ◽  
Main Driver ◽  
Sentinel 2

Rural communities rely on smallholder maize farms for subsistence agriculture, the main driver of local economic activity and food security. However, their planted area estimates are unknown in most developing countries. This study explores the use of Sentinel-1 and Sentinel-2 data to map smallholder maize farms. The random forest (RF), support vector (SVM) machine learning algorithms and model stacking (ST) were applied. Results show that the classification of combined Sentinel-1 and Sentinel-2 data improved the RF, SVM and ST algorithms by 24.2%, 8.7%, and 9.1%, respectively, compared to the classification of Sentinel-1 data individually. Similarities in the estimated areas (7001.35 ± 1.2 ha for RF, 7926.03 ± 0.7 ha for SVM and 7099.59 ± 0.8 ha for ST) show that machine learning can estimate smallholder maize areas with high accuracies. The study concludes that the single-date Sentinel-1 data were insufficient to map smallholder maize farms. However, single-date Sentinel-1 combined with Sentinel-2 data were sufficient in mapping smallholder farms. These results can be used to support the generation and validation of national crop statistics, thus contributing to food security.

scholarly journals Technical Note: A Guide to Annotation of Neurosurgical Intraoperative Video for Machine Learning Analysis and Computer Vision

2021 ◽  
Author(s):  
Dhiraj J. Pangal ◽  
Guillaume Kugener ◽  
Shane Shahrestani ◽  
Frank Attenello ◽  
Gabriel Zada ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Technical Note ◽  
Learning Analysis

scholarly journals End-To-End Computer Vision Framework: An Open-Source Platform for Research and Education

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3691
Author(s):  
Ciprian Orhei ◽  
Silviu Vert ◽  
Muguras Mocofan ◽  
Radu Vasiu
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Computer Vision ◽  
Open Source ◽  
Visual Processing ◽  
Research Field ◽  
Learning Models ◽  
Research Activity ◽  
End To End ◽  
Machine Learning Models

Computer Vision is a cross-research field with the main purpose of understanding the surrounding environment as closely as possible to human perception. The image processing systems is continuously growing and expanding into more complex systems, usually tailored to the certain needs or applications it may serve. To better serve this purpose, research on the architecture and design of such systems is also important. We present the End-to-End Computer Vision Framework, an open-source solution that aims to support researchers and teachers within the image processing vast field. The framework has incorporated Computer Vision features and Machine Learning models that researchers can use. In the continuous need to add new Computer Vision algorithms for a day-to-day research activity, our proposed framework has an advantage given by the configurable and scalar architecture. Even if the main focus of the framework is on the Computer Vision processing pipeline, the framework offers solutions to incorporate even more complex activities, such as training Machine Learning models. EECVF aims to become a useful tool for learning activities in the Computer Vision field, as it allows the learner and the teacher to handle only the topics at hand, and not the interconnection necessary for visual processing flow.

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