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Remote Sensing and Machine Learning for Food Crop ...

14/07/2021  Title: Remote Sensing and Machine Learning for Food Crop Production Data in Africa Post-COVID-19. Authors: Racine Ly, Khadim Dia, Mariam Diallo. Download PDF Abstract: In the agricultural sector, the COVID-19 threatens to lead to a severe food security crisis in the region, with disruptions in the food supply chain and agricultural production expected to contract between 2.6%

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Remote Sensing and Machine Learning for Food Crop ...

14/07/2021  This chapter assesses food crop production levels in 2020 -- before the harvesting period -- in all African regions and four staples such as maize, cassava, rice, and wheat. The production levels are predicted using the combination of biogeophysical remote sensing data retrieved from satellite images and machine learning artificial neural networks (ANNs) technique. The remote sensing

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(PDF) Remote Sensing and Machine Learning for Food Crop ...

Food crop production estimation based on remote sensing can be built through two main approaches: (i) Using remotely. sensed data as inputs to Agro-meteorological or plant-physiological models ...

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Remote Sensing and Machine Learning for Food Crop ...

01/07/2021  In the agricultural sector, the COVID-19 threatens to lead to a severe food security crisis in the region, with disruptions in the food supply chain and agricultural production expected to contract between 2.6% and 7%. From the food crop production side, the travel bans and border closures, the late reception and the use of agricultural inputs such as imported seeds, fertilizers, and ...

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[PDF] Remote Sensing and Machine Learning for Food Crop ...

Corpus ID: 237266569. Remote Sensing and Machine Learning for Food Crop Production Data in Africa Post-COVID-19 @inproceedings{Ly2021RemoteSA, title={Remote Sensing and Machine Learning for Food Crop Production Data in Africa Post-COVID-19}, author={Racine Ly and Khadim Dia and M. Diallo}, year={2021} }

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Remote Sensing and Machine Learning for Food Crop ...

Figure 11: The 2020 predicted maize production in southern African countries. Note: Data, methodology, and maps’ sources: Authors. - "Remote Sensing and Machine Learning for Food Crop Production Data in Africa Post-COVID-19"

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Remote Sensing and Machine Learning for Food Crop ...

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Remote Sensing Special Issue : Proximal and Remote ...

Crop disease is widely considered as one of the most pressing challenges for food crops, and therefore an accurate crop disease detection algorithm is highly desirable for its sustainable management. The recent use of remote sensing and deep learning is drawing increasing research interests in wheat yellow rust disease detection. However, current solutions on yellow rust detection are ...

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ARSET - Agricultural Crop Classification with Synthetic ...

Remote sensing methods based on optical and/or microwave sensors have become an important means of extracting crop information as they explain vegetation structure and biochemical properties. This five-part, intermediate webinar series will focus on the use of synthetic aperture radar (SAR) from Sentinel-1 and/or optical imagery from Sentinel-2 to map crop types and assess their biophysical ...

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Title: Remote Sensing and Machine Learning for Food Crop ...

Title: Remote Sensing and Machine Learning for Food Crop Production Data in Africa Post-COVID-19. Authors: Racine Ly, Khadim Dia, Mariam Diallo (Submitted on 14 Jul 2021) Abstract: In the agricultural sector, the COVID-19 threatens to lead to a severe food security crisis in the region, with disruptions in the food supply chain and agricultural production expected to contract between 2.6%

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Remote Sensing and Machine Learning for Food Crop ...

Figure 11: The 2020 predicted maize production in southern African countries. Note: Data, methodology, and maps’ sources: Authors. - "Remote Sensing and Machine Learning for Food Crop Production Data in Africa Post-COVID-19"

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Crop Type Identification and Classification by Reflectance ...

control the variations in the prices of the food grains. Remote sensing methods to identify crop types rely on remotely sensed images of high temporal frequency in order to utilize phenological changes in crop reflectance characteristics. Image identifying major crop types [3], [sets have generally low spatial resolution. This makes difficult any point during the growing season, crops are to ...

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arXiv:2108.10054v1 [cs.LG] 14 Jul 2021

REMOTE SENSING AND MACHINE LEARNING FOR FOOD CROP PRODUCTION DATA IN AFRICA POST-COVID-19 Racine Ly AKADEMIYA2063 Kigali, Rwanda [email protected] Khadim Dia AKADEMIYA2063 Kigali, Rwanda kdia ...

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ARSET - Agricultural Crop Classification with Synthetic ...

Remote sensing methods based on optical and/or microwave sensors have become an important means of extracting crop information as they explain vegetation structure and biochemical properties. This five-part, intermediate webinar series will focus on the use of synthetic aperture radar (SAR) from Sentinel-1 and/or optical imagery from Sentinel-2 to map crop types and assess their biophysical ...

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Remote Sensing Special Issue : Crop Growth Monitoring ...

Remote sensing data acquired by different platforms (e.g., satellite, airborne, UAV and ground) have been increasingly used to capture crop growth at various spatial and temporal scales. More recently, many newly developed sensors and data acquisition technologies have been developed to further enhance the capability of remote sensing in supporting crop growth monitoring and yield prediction ...

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Combining Remote Sensing Data and Machine Learning to ...

Combining Remote Sensing Data and Machine Learning to Predict Crop Yield Jiaxuan You, Xiaocheng li, melvin low, David B. Lobell, Stefano Ermon . Understanding crop yield is central to sustainable development. Understanding worldwide crop yield is central to addressing food security challenges and reducing the impacts of climate change. It can help achieve zero hunger, which is

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Estimating crop yields with remote sensing and deep ...

21/07/2020  Estimating crop yields with remote sensing and deep learning. 07/21/2020 ∙ by Renato Luiz de Freitas Cunha, et al. ∙ ibm ∙ 0 ∙ share . Increasing the accuracy of crop yield estimates may allow improvements in the whole crop production chain, allowing farmers to better plan for harvest, and for insurers to better understand risks of production, to name a few advantages.

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Applications of Remote Sensing in Precision Agriculture: A ...

Applications of Remote Sensing in Precision Agriculture: A Review Rajendra P. Sishodia 1,*, ... enhanced crop productivity and food security, especially in developing nations [4]. Consequently, despite the doubling population and tripling food demand since the 1960s, global agriculture has been able to meet the demands with only a 30% expansion in the cultivated area [4,5]. The demand for food ...

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MACHINE LEARNING FOR CROP YIELD REMOTE SENSING

MACHINE LEARNING FOR CROP YIELD REMOTE SENSING IMAGE ANALYSIS Amoli Belsare1*, ... food security etc. The study shows that along with the ML algorithms generation of ground truth image data is also important for achieving more accuracy as compared to state of art methods. ML approaches can be applied in image fusion, segmentation, crop identification, crop forecasting etc. Keywords:

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arXiv:2108.10054v1 [cs.LG] 14 Jul 2021

REMOTE SENSING AND MACHINE LEARNING FOR FOOD CROP PRODUCTION DATA IN AFRICA POST-COVID-19 Racine Ly AKADEMIYA2063 Kigali, Rwanda [email protected] Khadim Dia AKADEMIYA2063 Kigali, Rwanda kdia ...

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Remote Sensing and Machine Learning in Crop Phenotyping ...

Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming Caiwang Zheng 1,2,* , Amr Abd-Elrahman 1,2 and Vance Whitaker 1,3 Citation: Zheng, C.; Abd-Elrahman, A.; Whitaker, V. Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming.

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Combining Remote Sensing Data and Machine Learning to ...

Combining Remote Sensing Data and Machine Learning to Predict Crop Yield Jiaxuan You, Xiaocheng li, melvin low, David B. Lobell, Stefano Ermon . Understanding crop yield is central to sustainable development. Understanding worldwide crop yield is central to addressing food security challenges and reducing the impacts of climate change. It can help achieve zero hunger, which is

View More

MACHINE LEARNING FOR CROP YIELD REMOTE SENSING

MACHINE LEARNING FOR CROP YIELD REMOTE SENSING IMAGE ANALYSIS Amoli Belsare1*, ... food security etc. The study shows that along with the ML algorithms generation of ground truth image data is also important for achieving more accuracy as compared to state of art methods. ML approaches can be applied in image fusion, segmentation, crop identification, crop forecasting etc. Keywords:

View More

Estimating crop yields with remote sensing and deep ...

21/07/2020  Estimating crop yields with remote sensing and deep learning. 07/21/2020 ∙ by Renato Luiz de Freitas Cunha, et al. ∙ ibm ∙ 0 ∙ share . Increasing the accuracy of crop yield estimates may allow improvements in the whole crop production chain, allowing farmers to better plan for harvest, and for insurers to better understand risks of production, to name a few advantages.

View More

Applications of Remote Sensing in Precision Agriculture: A ...

Applications of Remote Sensing in Precision Agriculture: A Review Rajendra P. Sishodia 1,*, ... enhanced crop productivity and food security, especially in developing nations [4]. Consequently, despite the doubling population and tripling food demand since the 1960s, global agriculture has been able to meet the demands with only a 30% expansion in the cultivated area [4,5]. The demand for food ...

View More

Crop Type Identification and Classification by Reflectance ...

control the variations in the prices of the food grains. Remote sensing methods to identify crop types rely on remotely sensed images of high temporal frequency in order to utilize phenological changes in crop reflectance characteristics. Image identifying major crop types [3], [sets have generally low spatial resolution. This makes difficult any point during the growing season, crops are to ...

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Combining crop models and remote sensing for yield prediction

trade, food security safety net and relief programs, agricultural insurance, and recommendations about crop varieties and production technologies depend on or benefit from the best possible estimates of . 6 crop production. They differ primarily in the timing of key actions and hence the required lead-time. Agricultural and food security management can generally benefit from improvements in ...

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An overview of crop nitrogen status assessment using ...

01/03/2021  A synergy of hyperspectral remote sensing and crop growth model is one of the most promising approaches to describe the dynamic process of crop N status (Baret et al., 2007; Inoue, 2003). The crop traits derived from hyperspectral data can initialize state variables of crop growth model as well as reduce uncertainties of model parameters. Crop growth model can provide dynamic simulations as ...

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Remote sensing and machine learning

POSTNOTE 628 June 2020 Remote sensing and machine learning Page 2 Box 1: Machine and deep learning Box 2 Machine learning Machine learning is a branch of AI where algorithms can learn and improve from experience and data without being specifically programmed, reducing the level of human intervention.14 The quality of the output from the model depends on the quality of the data used to

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