Exploring Google Street View with deep learning for crop type mapping

Yan, Yulin; Ryu, Youngryel

doi:10.1016/j.isprsjprs.2020.11.022

Cited by 51 publications

(26 citation statements)

References 50 publications

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“…It can help government authorities and farmers to have efficient information about their crops that could be used to improve their abilities of decision-making. Abundant research has been carried out on precise crop classification from satellite-based remote sensing imagery using different machine learning and deep learning algorithms achieving remarkable results [ 2 , 56 , 70 , 117 , 120 ]. However, they have many drawbacks such as low spatial/temporal resolutions that should have a harmful impact on data quality, and different weather conditions that should make data collection very hard.…”

Section: Introductionmentioning

confidence: 99%

Deep learning techniques to classify agricultural crops through UAV imagery: a review

Bouguettaya

Zarzour

Kechida

et al. 2022

Neural Comput & Applic

136

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During the last few years, Unmanned Aerial Vehicles (UAVs) technologies are widely used to improve agriculture productivity while reducing drudgery, inspection time, and crop management cost. Moreover, they are able to cover large areas in a matter of a few minutes. Due to the impressive technological advancement, UAV-based remote sensing technologies are increasingly used to collect valuable data that could be used to achieve many precision agriculture applications, including crop/plant classification. In order to process these data accurately, we need powerful tools and algorithms such as Deep Learning approaches. Recently, Convolutional Neural Network (CNN) has emerged as a powerful tool for image processing tasks achieving remarkable results making it the state-of-the-art technique for vision applications. In the present study, we reviewed the recent CNN-based methods applied to the UAV-based remote sensing image analysis for crop/plant classification to help researchers and farmers to decide what algorithms they should use accordingly to their studied crops and the used hardware. Fusing different UAV-based data and deep learning approaches have emerged as a powerful tool to classify different crop types accurately. The readers of the present review could acquire the most challenging issues facing researchers to classify different crop types from UAV imagery and their potential solutions to improve the performance of deep learning-based algorithms.

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Section: Introductionmentioning

confidence: 99%

Deep learning techniques to classify agricultural crops through UAV imagery: a review

Bouguettaya

Zarzour

Kechida

et al. 2022

Neural Comput & Applic

136

View full text Add to dashboard Cite

show abstract

“…Secondly, training samples train the classification models with the features above. The most widely used classifiers including random forest (RF), decision tree (DT), support vector machine (SVM), K-nearest neighbor (KNN), and the advance deep learning models [25][26][27]. Then, the trained models can predict the crop classes.…”

Section: Introductionmentioning

confidence: 99%

Automatic Cotton Mapping Using Time Series of Sentinel-2 Images

2021

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Large-scale crop mapping is essential for agricultural management. Phenological variation often exists in the same crop due to different climatic regions or practice management, resulting in current classification models requiring sufficient training samples from different regions. However, the cost of sample collection is more time-consuming, costly, and labor-intensive, so it is necessary to develop automatic crop mapping models that require only a few samples and can be extended to a large area. In this study, a new white bolls index (WBI) based on the unique canopy of cotton at the bolls opening stage was proposed, which can characterize the intensity of bolls opening. The value of WBI will increase as the opening of the bolls increases. As a result, the white bolls index can be used to detect cotton automatically from other crops. Four study areas in different regions were used to evaluate the WBI performance. The overall accuracy (OA) for the four study sites was more than 82%. Additionally, the dates when the opening stage of bolls begins can be determined based on the time series of WBI. The results of this research demonstrated the potential of the proposed approach for cotton mapping using sentinel-2 time series of remotely sensed data.

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“…Figure 6 shows a demonstration of how we used AHI as a way to scale up the estimation of cover crop adoption and biomass at the regional scale. Other sensing solutions, such as mobile vehicle sensing 37 , IoT sensing network and robotics 38,39 , could also achieve a similar function to augment ground truth collection and enable satellite scaling-up to regional scales. was collected from individual ground sampling plots.…”

Section: Scalable Ground Truth Collection and Cross-scale Sensing Of Field-level Informationmentioning

confidence: 99%

A scalable framework for quantifying field-level agricultural carbon outcomes

Guan¹,

Jin²,

DeLucia³

et al. 2022

Preprint

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Agriculture contributes nearly a quarter of global greenhouse gas (GHG) emissions, which is motivating interest in certain farming practices that have the potential to reduce GHG emissions or sequester carbon in soil. The related GHG emission (including N2O and CH4) and changes in soil carbon stock are defined here as “agricultural carbon outcomes”. Accurate quantification of agricultural carbon outcomes is the basis for achieving emission reductions for agriculture, but existing approaches for measuring carbon outcomes (including direct measurements, emission factors, process-based modeling) fall short of achieving the required accuracy and scalability necessary to support credible, verifiable, and cost-effective measurement and improvement of these carbon outcomes. Here we propose a foundational and scalable framework to quantify field-level carbon outcomes for farmland, which is based on the holistic carbon balance of the agroecosystem: Agroecosystem Carbon Outcomes = Crops (C) × Management (M) × Environment (E). Following a comprehensive review of the scientific challenges associated with existing approaches, as well as their tradeoffs between cost and accuracy, we propose that the most viable path for the quantification of field-level carbon outcomes in agricultural land is through an effective integration of various approaches (e.g. diverse observations, sensor/in-situ data, modeling), defined as the “system- of-systems” solution. Such a “system-of-systems” solution should simultaneously comprise the following components: (1) scalable collection of ground truth data and cross-scale sensing of crop conditions (C), management practices (M), and environment (E) at the local field level; (2) advanced modeling with necessary processes to support the quantification of carbon outcomes; (3) systematic Model-Data Fusion (MDF), i.e. robust and efficient methods to integrate sensing data and models at each local farmland level; (4) high computation efficiency and artificial intelligence (AI) to scale to millions of individual fields with low cost; and (5) robust and multi-tier validation systems and infrastructures to ensure solution fidelity and true scalability, i.e. the ability of a solution to perform robustly with accepted accuracy on all targeted fields. In this regard, we provide here the detailed scientific rationale, current progress, and future R&D priorities to achieve different components of the “system-of-systems” solution, thus accomplishing the Crop×Management×Environment framework to quantify field-level agricultural carbon outcomes.

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Exploring Google Street View with deep learning for crop type mapping

Cited by 51 publications

References 50 publications

Deep learning techniques to classify agricultural crops through UAV imagery: a review

Deep learning techniques to classify agricultural crops through UAV imagery: a review

Automatic Cotton Mapping Using Time Series of Sentinel-2 Images

A scalable framework for quantifying field-level agricultural carbon outcomes

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