A multimodality test outperforms three machine learning classifiers for identifying and mapping paddocks using time series satellite imagery

O’Hara, Rob; Zimmermann, Jesko; Green, Stuart

doi:10.1080/10106049.2021.2024278

Cited by 3 publications

(2 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are also a number of studies that use data-driven approaches to detect lameness (Shahinfar et al, 2021 ; Altay and Albayrak Delialioğlu, 2022 ; Zhou et al, 2022 ) and ketosis (a metabolic disease in cows; Bauer and Jagusiak, 2022 ; Wang et al, 2023 ) using machine learning. As well as cow health, there are other aspects of dairy farming where data-centric advanced systems are being employed, including predicting herbage yield and composition (O'Hara et al, 2021 ; Albert et al, 2022 ), and estimating greenhouse gas emission (Chianese et al, 2009 ; Martin et al, 2017 ; Kadam and Vijayumar, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

CowMesh: a data-mesh architecture to unify dairy industry data for prediction and monitoring

Pakrashi,

Wallace,

Mac Namee

et al. 2023

Front. Artif. Intell.

View full text Add to dashboard Cite

Dairy is an economically significant industry that caters to the huge demand for food products in people's lives. To remain profitable, farmers need to manage their farms and the health of the dairy cows in their herds. There are, however, many risks to cow health that can lead to significant challenges to dairy farm management and have the potential to lead to significant losses. Such risks include cow udder infections (i.e., mastitis) and cow lameness. As automation and data recording become more common in the agricultural sector, dairy farms are generating increasing amounts of data. Recently, these data are being used to generate insights into farm and cow health, where the objective is to help farmers manage the health and welfare of dairy cows and reduce losses from cow health issues. Despite the level of data generation on dairy farms, this information is often difficult to access due to a lack of a single, central organization to collect data from individual farms. The prospect of such an organization, however, raises questions about data ownership, with some farmers reluctant to share their farm data for privacy reasons. In this study, we describe a new data mesh architecture designed for the dairy industry that focuses on facilitating access to data from farms in a decentralized fashion. This has the benefit of keeping the ownership of data with dairy farmers while bringing data together by providing a common and uniform set of protocols. Furthermore, this architecture will allow secure access to the data by research groups and product development groups, who can plug in new projects and applications built across the data. No similar framework currently exists in the dairy industry, and such a data mesh can help industry stakeholders by bringing the dairy farms of a country together in a decentralized fashion. This not only helps farmers, dairy researchers, and product builders but also facilitates an overview of all dairy farms which can help governments to decide on regulations to improve the dairy industry at a national level.

show abstract

Section: Introductionmentioning

confidence: 99%

CowMesh: a data-mesh architecture to unify dairy industry data for prediction and monitoring

Pakrashi,

Wallace,

Mac Namee

et al. 2023

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…With the rapid development of computer application technology, chemometrics methods, statistical theory, and the high integration of multi-disciplinary technologies have been put forward, and their feasibility for NIR detection has been verified [ 20 , 21 , 22 , 23 , 24 ]. At the same time, random forest (RF) is a common machine learning method that is usually used to deal with classification [ 25 , 26 , 27 ] and regression [ 28 , 29 , 30 ] problems. This method improves the prediction accuracy without significantly increasing the computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Soil Nitrogen Content Detection Based on Near-Infrared Spectroscopy

Tan

You

Tian

et al. 2022

Sensors

View full text Add to dashboard Cite

Traditional soil nitrogen detection methods have the characteristics of being time-consuming and having an environmental pollution effect. We urgently need a rapid, easy-to-operate, and non-polluting soil nitrogen detection technology. In order to quickly measure the nitrogen content in soil, a new method for detecting the nitrogen content in soil is presented by using a near-infrared spectrum technique and random forest regression (RF). Firstly, the experiment took the soil by the Xunsi River in the area of Hubei University of Technology as the research object, and a total of 143 soil samples were collected. Secondly, NIR spectral data from 143 soil samples were acquired, and chemical and physical methods were used to determine the content of nitrogen in the soil. Thirdly, the raw spectral data of soil samples were denoised by preprocessing. Finally, a forecast model for the soil nitrogen content was developed by using the measured values of components and modeling algorithms. The model was optimized by adjusting the changes in the model parameters and Gini coefficient (∆Gini), and the model was compared with the back propagation (BP) and support vector machine (SVM) models. The results show that: the RF model modeling set prediction R2C is 0.921, the RMSEC is 0.115, the test set R2P is 0.83, and the RMSEP is 0.141; the detection of the soil nitrogen content can be realized by using a near-infrared spectrum technique and random forest algorithm, and its prediction accuracy is better than that of the BP and SVM models; using ∆ Gini to optimize the RF modeling data, the spectral information of the soil nitrogen content can be extracted, and the data redundancy can be reduced effectively.

show abstract

Land Use and Land Cover Mapping Using Sentinel-2, Landsat-8 Satellite Images, and Google Earth Engine: A Comparison of Two Composition Methods

et al. 2022

View full text Add to dashboard Cite

Accurate and real-time land use/land cover (LULC) maps are important to provide precise information for dynamic monitoring, planning, and management of the Earth. With the advent of cloud computing platforms, time series feature extraction techniques, and machine learning classifiers, new opportunities are arising in more accurate and large-scale LULC mapping. In this study, we aimed at finding out how two composition methods and spectral–temporal metrics extracted from satellite time series can affect the ability of a machine learning classifier to produce accurate LULC maps. We used the Google Earth Engine (GEE) cloud computing platform to create cloud-free Sentinel-2 (S-2) and Landsat-8 (L-8) time series over the Tehran Province (Iran) as of 2020. Two composition methods, namely, seasonal composites and percentiles metrics, were used to define four datasets based on satellite time series, vegetation indices, and topographic layers. The random forest classifier was used in LULC classification and for identifying the most important variables. Accuracy assessment results showed that the S-2 outperformed the L-8 spectral–temporal metrics at the overall and class level. Moreover, the comparison of composition methods indicated that seasonal composites outperformed percentile metrics in both S-2 and L-8 time series. At the class level, the improved performance of seasonal composites was related to their ability to provide better information about the phenological variation of different LULC classes. Finally, we conclude that this methodology can produce LULC maps based on cloud computing GEE in an accurate and fast way and can be used in large-scale LULC mapping.

show abstract

A multimodality test outperforms three machine learning classifiers for identifying and mapping paddocks using time series satellite imagery

Cited by 3 publications

References 50 publications

CowMesh: a data-mesh architecture to unify dairy industry data for prediction and monitoring

CowMesh: a data-mesh architecture to unify dairy industry data for prediction and monitoring

Soil Nitrogen Content Detection Based on Near-Infrared Spectroscopy

Land Use and Land Cover Mapping Using Sentinel-2, Landsat-8 Satellite Images, and Google Earth Engine: A Comparison of Two Composition Methods

Contact Info

Product

Resources

About