A novel invasive plant detection approach using time series images from unmanned aerial systems based on convolutional and recurrent neural networks

Guo, Yanhui; Yun, Zhao; Rothfus, Thomas A.; Avalos, Adam S.

doi:10.1007/s00521-022-07560-3

Cited by 10 publications

(9 citation statements)

References 39 publications

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“…By utilizing multiple sensors to collaborate with each other, the environmental feature information is extracted, and a sufficient number of learning datasets with precise calibration are established (Balestrieri et al, 2021 ). Then, building an intelligent learning model (Jafari and Xu, 2018 ; Eski and Kus, 2019 ; Guo et al, 2022 ; Kang et al, 2022 ) can achieve complete recognition of scene information and accurate recognition of designated targets by intelligent unmanned systems.…”

Section: Introductionmentioning

confidence: 99%

A novel associative memory model based on semi-tensor product (STP)

Hou,

Tian,

Wang

2024

Front. Comput. Neurosci.

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A good intelligent learning model is the key to complete recognition of scene information and accurate recognition of specific targets in intelligent unmanned system. This study proposes a new associative memory model based on the semi-tensor product (STP) of matrices, to address the problems of information storage capacity and association. First, some preliminaries are introduced to facilitate modeling, and the problem of information storage capacity in the application of discrete Hopfield neural network (DHNN) to associative memory is pointed out. Second, learning modes are equivalently converted into their algebraic forms by using STP. A memory matrix is constructed to accurately remember these learning modes. Furthermore, an algorithm for updating the memory matrix is developed to improve the association ability of the model. And another algorithm is provided to show how our model learns and associates. Finally, some examples are given to demonstrate the effectiveness and advantages of our results. Compared with mainstream DHNNs, our model can remember learning modes more accurately with fewer nodes.

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Section: Introductionmentioning

confidence: 99%

A novel associative memory model based on semi-tensor product (STP)

Hou,

Tian,

Wang

2024

Front. Comput. Neurosci.

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“…The fundamental management and intervention of invasive species require a detailed understanding of their invasion and spread processes, such as their spatial distribution extent and cover [19,20]. The latest maps that describe the current status of invasive species are extremely valuable for the decision-making and planning of preintervention and quickresponse conservation measures in their early invasion stage [6,21]. However, the evolution process of invasive species is commonly complex and spatially and temporally dynamic; the diversity of corresponding anthropogenic and natural drivers makes it harder to detect [20].…”

Section: Introductionmentioning

confidence: 99%

Mapping the Continuous Cover of Invasive Noxious Weed Species Using Sentinel-2 Imagery and a Novel Convolutional Neural Regression Network

Xing,

An,

Guo

et al. 2024

Remote Sensing

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Invasive noxious weed species (INWS) are typical poisonous plants and forbs that are considered an increasing threat to the native alpine grassland ecosystems in the Qinghai–Tibetan Plateau (QTP). Accurate knowledge of the continuous cover of INWS across complex alpine grassland ecosystems over a large scale is required for their control and management. However, the cooccurrence of INWS and native grass species results in highly heterogeneous grass communities and generates mixed pixels detected by remote sensors, which causes uncertainty in classification. The continuous coverage of INWS at the pixel level has not yet been achieved. In this study, objective 1 was to test the capability of Senginel-2 imagery at estimating continuous INWS cover across complex alpine grasslands over a large scale and objective 2 was to assess the performance of the state-of-the-art convolutional neural network-based regression (CNNR) model in estimating continuous INWS cover. Therefore, a novel CNNR model and a random forest regression (RFR) model were evaluated for estimating INWS continuous cover using Sentinel-2 imagery. INWS continuous cover was estimated directly from Sentinel-2 imagery with an R2 ranging from 0.88 to 0.93 using the CNNR model. The RFR model combined with multiple features had a comparable accuracy, which was slightly lower than that of the CNNR model, with an R2 of approximately 0.85. Twelve green band-, red-edge band-, and near-infrared band-related features had important contributions to the RFR model. Our results demonstrate that the CNNR model performs well when estimating INWS continuous cover directly from Sentinel-2 imagery, and the RFR model combined with multiple features derived from the Sentinel-2 imager can also be used for INWS continuous cover mapping. Sentinel-2 imagery is suitable for mapping continuous INWS cover across complex alpine grasslands over a large scale. Our research provides information for the advanced mapping of the continuous cover of invasive species across complex grassland ecosystems or, more widely, terrestrial ecosystems over large spatial areas using remote sensors such as Sentinel-2.

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“…The study of annual and long-term patterns in biological invasion is complicated by spatial heterogeneity. However, because of its wide scope, remote sensing has the ability to provide the necessary data 27 . Maps of the distribution of different plant and animal species, as well as of their ecosystems, landscapes, bio-climatic conditions, and invasion-promoting factors, have already been created using GIS and remote sensing 13,28 .…”

Section: Introductionmentioning

confidence: 99%

Evaluating the habitat suitability modeling of Aceria alhagi and Alhagi maurorum in their native range using machine learning techniques

Dastres

Bijani

Naderi

et al. 2023

Preprint

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Spatial locational modeling techniques are increasingly used in species distribution modeling. However, the implemented techniques differ in their modeling performance. In this study, we tested the predictive accuracy of three algorithms, namely "random forest (RF)," "support vector machine (SVM)," and "boosted regression trees (BRT)" to prepare habitat suitability mapping of an invasive species, Alhagi maurorum, and its potential biological control agent, Aceria alhagi. Location of this study was in Fars Province, southwest of Iran. The spatial distributions of the species were forecasted using GPS devices and GIS software. The probability values of occurrence were then checked using three algorithms. The predictive accuracy of the machine learning (ML) techniques was assessed by computing the “area under the curve (AUC)” of the “receiver-operating characteristic” plot. When the Aceria alhagi was modeled, the AUC values of RF, BRT and SVM were 0.89, 0.81, and 0.79, respectively. However, in habitat suitability models (HSMs) of Alhagi maurorum the AUC values of RF, BRT and SVM were 0.89, 0.80, and 0.73, respectively. The RF model provided significantly more accurate predictions than other algorithms. The importance of factors on the growth and development of Alhagi maurorum and Aceria alhagi was also determined using the partial least squares (PLS) algorithm, and the most crucial factors were the road and slope. Habitat suitability modeling based on algorithms may significantly increase the accuracy of species distribution forecasts, and thus it shows considerable promise for different conservation biological and biogeographical applications.

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A novel invasive plant detection approach using time series images from unmanned aerial systems based on convolutional and recurrent neural networks

Cited by 10 publications

References 39 publications

A novel associative memory model based on semi-tensor product (STP)

A novel associative memory model based on semi-tensor product (STP)

Mapping the Continuous Cover of Invasive Noxious Weed Species Using Sentinel-2 Imagery and a Novel Convolutional Neural Regression Network

Evaluating the habitat suitability modeling of Aceria alhagi and Alhagi maurorum in their native range using machine learning techniques

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