eFarm: A Tool for Better Observing Agricultural Land Systems

Yu, Qiangyi; Shi, Yun; Tang, Huajun; Yang, Peng; Xie, Ankun; Liu, Bin; Wu, Wenbin

doi:10.3390/s17030453

Cited by 35 publications

(26 citation statements)

References 57 publications

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“…Group on Earth Observations (GEO), with its Integrated Global Observing Strategy (IGOS), also emphases on an operational system for monitoring global land covers and mapping spatial distribution of crops by using remote sensing imagery. Spatial information of the crop maps has been the main source for crop growth monitoring [7][8][9], water resources management [10], and decision making for policy makers to ensure food security [11].…”

Section: Introductionmentioning

confidence: 99%

Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)

2019

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Understanding the use of current land cover, along with monitoring change over time, is vital for agronomists and agricultural agencies responsible for land management. The increasing spatial and temporal resolution of globally available satellite images, such as provided by Sentinel-2, creates new possibilities for researchers to use freely available multi-spectral optical images, with decametric spatial resolution and more frequent revisits for remote sensing applications such as land cover and crop classification (LC&CC), agricultural monitoring and management, environment monitoring. Existing solutions dedicated to cropland mapping can be categorized based on per-pixel based and object-based. However, it is still challenging when more classes of agricultural crops are considered at a massive scale. In this paper, a novel and optimal deep learning model for pixel-based LC&CC is developed and implemented based on Recurrent Neural Networks (RNN) in combination with Convolutional Neural Networks (CNN) using multi-temporal sentinel-2 imagery of central north part of Italy, which has diverse agricultural system dominated by economic crop types. The proposed methodology is capable of automated feature extraction by learning time correlation of multiple images, which reduces manual feature engineering and modeling crop phenological stages. Fifteen classes, including major agricultural crops, were considered in this study. We also tested other widely used traditional machine learning algorithms for comparison such as support vector machine SVM, random forest (RF), Kernal SVM, and gradient boosting machine, also called XGBoost. The overall accuracy achieved by our proposed Pixel R-CNN was 96.5%, which showed considerable improvements in comparison with existing mainstream methods. This study showed that Pixel R-CNN based model offers a highly accurate way to assess and employ time-series data for multi-temporal classification tasks.

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Section: Introductionmentioning

confidence: 99%

Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)

2019

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“…Furthermore, although the stress of food demand has remained, China has increased its food import from other countries, which could translate into a lower demand for domestic food, and thus less pressure to close the HAG. Site-specific and long-term observations at the field level would provide opportunities for better understanding the interactive process of land management adaptation to climate change (Yu et al 2017b).…”

Section: Discussionmentioning

confidence: 99%

“…The HAG has been introduced in Yu et al (2017b) and indicates the harvested area that could possibly be available when the existing cropland in a region is harvested as frequently as possible according to their potential limit for multi-cropping (figure 1). Given the required days to maturity (Asseng et al 2011), the possibility of increasing the harvest frequency largely depends on local climatic conditions.…”

Section: The Harvested Area Gapmentioning

confidence: 99%

“…In this study, however, we applied the latter approach to PMCI, as it allows for the analysis of small and nuanced changes over time. Conversely, Yu et al (2017b) applied the former meaning of PMCI in their estimation of HAG in China for the year 2005. Specifically, the 'temperature-precipitation' model (He et al 2016) is applied to calculate the PMCI based on temperature and precipitation data.…”

Section: Potential Multi-cropping Intensitymentioning

confidence: 99%

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Harvested area gaps in China between 1981 and 2010: effects of climatic and land management factors

Vliet

Verburg

et al. 2018

Environ. Res. Lett.

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LETTER • OPEN ACCESS AbstractPrevious analyses have shown that cropland in China is intensifying, leading to an increase in crop production. However, these output measures leave the potential for further intensification largely unassessed. This study uses the harvested area gap (HAG), which expresses the amount of harvested area that can be gained if all existing cropland is harvested as frequently as possible, according to their potential limit for multi-cropping. Specifically, we calculate the HAG and changes in the HAG in China between 1981 and 2010. We further assess how climatic and land management factors affect these changes. We find that in China the HAG decreases between the 1980s and the 1990s, and subsequently increases between the 1990s and the 2000s, resulting in a small net increase for the entire study period. The initial decrease in the HAG is the result of an increase in the average multi-cropping index throughout the country, which is larger than the increase in the potential multi-cropping index as a result of the changed climatic factors. The subsequent increase in the HAG is the result of a decrease in average multi-cropping index throughout the country, in combination with a stagnant potential. Despite the overall increase in harvested area in China, many regions, e.g. Northeast and Lower Yangtze, are characterized by an increased HAG, indicating their potential for further increasing the multi-cropping index. The study demonstrates the application of the HAG as a method to identify areas where the harvested area can increase to increase crop production, which is currently underexplored in scientific literature.

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“…50 Among the commercial nanotechnologies achieved, so far, smartphones have gained huge market shares in various sectors, due to their portability, practicality, easy operation, and convenience. 51 Thus, advanced analytical tools are possible using different types of nanomaterials, interfaces and devices as well as their properties through systematic interdisciplinary investigation and effective knowledge transfer. 52 Current nanotechnology-enabled sensors available on the market include optical sensors, electrical or electromagnetic sensors, and electrochemical sensors.…”

Section: Nanotechnology-based Sensing Opportunities In Agriculturementioning

confidence: 99%

Recent developments in nanotechnology transforming the agricultural sector: a transition replete with opportunities

et al. 2017

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The applications and benefits of nanotechnology in the agricultural sector have attracted considerable attention, particularly in the invention of unique nanopesticides and nanofertilisers. The contemporary developments in nanotechnology are acknowledged and the most significant opportunities awaiting the agriculture sector from the recent scientific and technical literature are addressed. This review discusses the significance of recent trends in nanomaterial-based sensors available for the sustainable management of agricultural soil, as well as the role of nanotechnology in detection and protection against plant pathogens, and for food quality and safety. Novel nanosensors have been reported for primary applications in improving crop practices, food quality, and packaging methods, thus will change the agricultural sector for potentially better and healthier food products. Nanotechnology is well-known to play a significant role in the effective management of phytopathogens, nutrient utilisation, controlled release of pesticides, and fertilisers. Research and scientific gaps to be overcome and fundamental questions have been addressed to fuel active development and application of nanotechnology. Together, nanoscience, nanoengineering, and nanotechnology offer a plethora of opportunities, proving a viable alternative in the agriculture and food processing sector, by providing a novel and advanced solutions. © 2017 Society of Chemical Industry.

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eFarm: A Tool for Better Observing Agricultural Land Systems

Cited by 35 publications

References 57 publications

Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)

Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)

Harvested area gaps in China between 1981 and 2010: effects of climatic and land management factors

Recent developments in nanotechnology transforming the agricultural sector: a transition replete with opportunities

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