Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data

Kavats, Olena; Khramov, Dmitriy; Sergieieva, Kateryna; Vasyliev, Volodymyr V.

doi:10.3390/rs12244080

Cited by 16 publications

(11 citation statements)

References 42 publications

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“…Interferometric coherence has also been used for retrieval of biophysical parameters [ 19 , 20 ], as well as for crop height estimation [ 21 , 22 , 23 ] and determination of harvest dates [ 24 ]. However, its usefulness as a tool specifically for crop monitoring has not been explored in depth.…”

Section: Introductionmentioning

confidence: 99%

Multi-Annual Evaluation of Time Series of Sentinel-1 Interferometric Coherence as a Tool for Crop Monitoring

Villarroya-Carpio

López-Sánchez

2023

Sensors

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Interferometric coherence from SAR data is a tool used in a variety of Earth observation applications. In the context of crop monitoring, vegetation indices are commonly used to describe crop dynamics. The most frequently used vegetation indices based on radar data are constructed using the backscattered intensity at different polarimetric channels. As coherence is sensitive to the changes in the scene caused by vegetation and its evolution, it may potentially be used as an alternative tool in this context. The objective of this work is to evaluate the potential of using Sentinel-1 interferometric coherence for this purpose. The study area is an agricultural region in Sevilla, Spain, mainly covered by 18 different crops. Time series of different backscatter-based radar vegetation indices and the coherence amplitude for both VV and VH channels from Sentinel-1 were compared to the NDVI derived from Sentinel-2 imagery for a 5-year period, from 2017 to 2021. The correlations between the series were studied both during and outside the growing season of the crops. Additionally, the use of the ratio of the two coherences measured at both polarimetric channels was explored. The results show that the coherence is generally well correlated with the NDVI across all seasons. The ratio between coherences at each channel is a potential alternative to the separate channels when the analysis is not restricted to the growing season of the crop, as its year-long temporal evolution more closely resembles that of the NDVI. Coherence and backscatter can be used as complementary sources of information, as backscatter-based indices describe the evolution of certain crops better than coherence.

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Section: Introductionmentioning

confidence: 99%

Multi-Annual Evaluation of Time Series of Sentinel-1 Interferometric Coherence as a Tool for Crop Monitoring

Villarroya-Carpio

López-Sánchez

2023

Sensors

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“…This analysis could be based on predictive analytics fed by images of large areas and publicly available optical satellite data (Kavats et al, 2020). Few researchers are addressing strategic questions such as infrastructure investment decisions for building roads, irrigation infrastructure, etc.…”

Section: Production and Operations Managementmentioning

confidence: 99%

Opportunities in farming research from an operations management perspective

Gupta

Berenji

Shukla

et al. 2023

Production and Operations Management

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We review and analyze the farming (upstream agribusiness supply chain) research literature since 1965 to identify farming research opportunities for operations management (OM) researchers. A majority of reviewed papers in our corpus, until the turn of the 21st century, primarily focus on improving operational efficiency and effectiveness of farming using optimization techniques. However, during the last two decades, farmers’ welfare and the interests of other stakeholders have drawn OM researchers’ attention. This expanded focus on farming research has become possible due to the proliferation of mobile communication devices and the Internet as well as advancements in information technology platforms and social media. Our review also shows that there is a paucity of OM literature that leverages increased data availability from the emergence of precision agriculture and blockchain to address major challenges for the farming sector emanating from climate change, natural disasters, food security, and sustainable and equitable agriculture, among others. Big data, in conjunction with opportunities for field‐based experimentation, artificial intelligence and machine learning, and integration of predictive and prescriptive analytics, can be leveraged by OM scholars engaged in farming research. We zero in on specific questions, issues, and opportunities for research in farming.

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“…Based on the studies carried out by [11,12]it can be seen that the tendency of vegetation analysis is carried out by means of normalized coloration indices (NDVI, NDRE, GNDVI and WDRVI). In addition, for studies of large areas in production estimates and phytosanitary status, the acquisition of images provided by satellites is selected [11,12,13].…”

Section: Literature Reviewmentioning

confidence: 99%

Design of a Sugarcane Diseases Recognition System Based on GoogLeNet for a Web Application

Maza¹,

Córdova²,

Beltran³

2022

IJETAE

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—Sugarcane diseases in Peru occur due to the agricultural community's lack of understanding of these, which means a slow response to the application of methods of control and eradication of these diseases; thus, causing economic losses and underproduction. Due to the aforementioned, a web application for sugarcane diseases recognition is proposed. The five types of sugarcane diseases that will be recognized using this system are: Pineapple Sett Rot, Ring Spot, Mosaic, Brown Rust and Leaf Scorch. This system was developed using GoogLeNet, which is a 22 layers convolutional neural network (CNN), and also the Matlab software and its App Designer extensions (for the web application creation); additionally, Matlab Web App Server was used to host the application on the web. The pre-trained neural network developed in Matlab based on the GoogLeNet architecture allowed the creation and configuration of the training parameters (supervised learning) that were evaluated, and it was considered convenient to split the data between training, validation and testing (70%, 20% and 10%, respectively). A total of 250 images composed of 50 images for each disease were used. The web application was designed in App Designer which provided us with a set of tools and a programming interface for the insertion of the trained CNN, with a validation percentage of 94.67% obtained by varying the number of epochs, reaching a maximum of 6000 iterations. Finally, the web application supported by the Matlab Web App Server was generated and tests were performed on a local network, resulting in a web application capable of identifying images within the established guidelines, with an accuracy rate of 96%

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Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data

Cited by 16 publications

References 42 publications

Multi-Annual Evaluation of Time Series of Sentinel-1 Interferometric Coherence as a Tool for Crop Monitoring

Multi-Annual Evaluation of Time Series of Sentinel-1 Interferometric Coherence as a Tool for Crop Monitoring

Opportunities in farming research from an operations management perspective

Design of a Sugarcane Diseases Recognition System Based on GoogLeNet for a Web Application

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