Near Real Time Crop Loss Estimation using Remote Sensing Observations

Sawant, Suryakant; Mohite, Jayantrao; Sakkan, Mariappan; Pappula, Srinivasu

doi:10.1109/agro-geoinformatics.2019.8820217

Cited by 15 publications

(8 citation statements)

References 8 publications

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“…In disaster loss assessment and forecasting, scholars have mainly focused on earthquakes (Jena et al, 2020;Kim et al, 2020;Pulinets et al, 2021), tropical cyclones (Qi and Du, 2018;Sawant et al, 2019;Giffard-Roisin et al, 2020;Zeng et al, 2021), and floods (Zhi et al, 2020;Soltani et al, 2021), but few studies have been conducted on storm surge. With the background and impact of global climate change (Hao et al, 2021), the problem of marine disasters (Fang et al, 2017;Yan et al, 2020) is becoming increasingly obvious.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling of Direct Economic Losses of Storm Surge Disasters Based on a Novel Hybrid Forecasting System

2022

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Modeling the direct economic losses of storm surge disasters can assess the disaster situation in a timely manner and improve the efficiency of post-disaster management in practice, which is acknowledged as one of the most significant issues in clean production. However, improving the forecasting accuracy of direct economic losses caused by storm surge disasters remains challenging, which is also a major concern in the field of disaster risk management. In particular, most of the previous studies have mainly focused on individual models, which ignored the significance of reduction and optimization. Therefore, a novel direct economic loss forecasting system for storm surge disasters is proposed in this study, which includes reduction, forecasting, and evaluation modules. In this system, a forecasting module based on an improved machine learning technique is proposed, which improves the generalization ability and robustness of the system. In addition, the key attributes and samples are selected by the proposed reduction module to further improve the forecasting performance from the two innovative perspectives. Moreover, an evaluation module is incorporated to comprehensively evaluate the superiority of the developed forecasting system. Data on the storm surge disasters from three typical provinces are utilized to conduct a case study, and the performance of the proposed forecasting system is analyzed and compared with eight comparison models. The experimental results show that the mean absolute percentage error (MAPE) predicted by the Extreme Learning Machine (ELM) model was 16.5293%, and the MAPE predicted by the proposed system was 1.0313%. Overall, the results show that the performance of the proposed forecasting system is superior compared to other models, and it is suitable for the forecasting of direct economic losses resulting from storm surge disasters.

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Section: Literature Reviewmentioning

confidence: 99%

Modeling of Direct Economic Losses of Storm Surge Disasters Based on a Novel Hybrid Forecasting System

2022

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“…In addition, agriculture remains one of the most researched EO data applications, with exponential growth occurring since 2013 [3]. Some of the most notable agricultural applications include crop areas and monitoring [4][5][6][7][8], yield estimates [9][10][11], irrigation mapping [12][13][14], and crop loss assessment [15][16].…”

Section: Summary (Required)mentioning

confidence: 99%

Collecting and Disseminating Ground Truth Data for Generation of Agriculture Statistics

Dan¹,

Frank²,

Karunanayake³

et al. 2022

Preprint

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Over the last few years, Earth Observation (EO) data has shifted towards increased use to produce official statistics, particularly in the agriculture sector. National statistics offices worldwide, including in Asia and the Pacific, are expanding their use of EO data to produce agricultural statistics such as crop classification, yield estimation, irrigation mapping, and crop loss estimation. The advances in image classification, such as pixel-based and phenology-based classifications, and machine learning create new opportunities for researchers to analyze EO data applied to agriculture statistics. However, it requires the ground truth (GT) data because classification result mainly depends on the quality of GT. Therefore, in this study, we introduced a random sampling approach to design and collect GT data using EO imagery and ancillary data. As a result of data collection, GT data improve the algorithms and validates classification results. Nevertheless, despite the importance of GT data, they are rarely disseminated as a data product in themselves. Thus, this results in an untapped opportunity to share GT data as a global public good, and improved use of survey and census data as a source of GT data.

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“…The state houses a population of 67.9 million people. of which 70% are engaged in agricultural activities [33]. Compared to Kerala, Tamil Nadu accounts for a larger variety of crops and the major crops include rice, jowar, ragi, bajra, maize, pulses, mango, banana, cotton, sugarcane, tea, coffee, and coconut.…”

Section: Introductionmentioning

confidence: 99%

Impact of COVID-19 on the Agriculture Sector: Survey Analysis of Farmer Responses from Kerala and Tamil Nadu States in India

et al. 2022

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The global COVID-19 pandemic has hit the agriculture sector hard around the world. A study was conducted to assess the impact of the pandemic on cropping patterns, crop management, usage of chemical inputs and their organic alternatives, harvesting, and marketing avenues through a survey approach in the two states of Kerala and Tamil Nadu in India. A total of 250 farmers participated in the study, the data was analyzed by Chi-square test and Kruskal–Wallis test. The assessment of the impact of COVID-19 on some aspects was undertaken by dividing the study period into three phases. Though a smaller number of people were infected with COVID-19 in the initial phase of the pandemic compared to the later phases, farm operations and the procurement of inputs were significantly affected at this phase as there was a sudden disruption in transportation due to COVID-19-induced movement restrictions. During the entire study period, commodities such as rice, bananas, vegetables, coconuts, and flowers suffered maximum crop loss compared to pulses, groundnuts, cotton, and rubber. Among fertilizers, the maximum shortage was observed for chemical fertilizers (46%) and biofertilizers (30%) compared to cow dung (18%) and poultry manure (6%), indicating that farmers tended to use more local materials that could be easily procured and accessed compared to shop-based inputs. A rise in the cost of cultivation, scarcity of farm workforce, and difficulty in hiring farm machinery all have contributed to the loss of profit during the pandemic period. As a response to COVID-19, growers initiated post-harvest processing of commodities, and cropping systems remained the same during the period. The paper also discusses some remedial measures to be adopted by households in the future, to minimize the impacts of such pandemics in the agrarian sector.

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Near Real Time Crop Loss Estimation using Remote Sensing Observations

Cited by 15 publications

References 8 publications

Modeling of Direct Economic Losses of Storm Surge Disasters Based on a Novel Hybrid Forecasting System

Modeling of Direct Economic Losses of Storm Surge Disasters Based on a Novel Hybrid Forecasting System

Collecting and Disseminating Ground Truth Data for Generation of Agriculture Statistics

Impact of COVID-19 on the Agriculture Sector: Survey Analysis of Farmer Responses from Kerala and Tamil Nadu States in India

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