Integration of Precision Farming Data and Spatial Statistical Modelling to Interpret Field-Scale Maize Productivity

Jiang, Guopeng; Grafton, Miles; Pearson, Diane; Bretherton, Mike; Holmes, Allister

doi:10.3390/agriculture9110237

Cited by 13 publications

(8 citation statements)

References 39 publications

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“…The R 2 values of the inversion models were between 0.63 and 0.66, the RMSE was between 0.0082 and 0.0084, and the MAE was 0.56-0.58%. To improve the fitting degree of the model and the accuracy of the predicted value, this paper used the multivariate linear method for gradual regression to establish the inversion models [27]. Table 5 shows that the R 2 of the multiple linear regression equations increased to 0.75 compared with the simple linear regression equations.…”

Section: Results Of Inversion Modelsmentioning

confidence: 99%

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Research on Inversion Model of Cultivated Soil Moisture Content Based on Hyperspectral Imaging Analysis

Jian

Jing-xia

et al. 2020

Agriculture

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Based on hyperspectral imaging technology, rapid and efficient prediction of soil moisture content (SMC) can provide an essential basis for the formulation of precise agricultural programs (e.g., forestry irrigation and environmental management). To build an efficient inversion model of SMC, this paper collected 117 cultivated soil samples from the Chair Hill area and tested them using the GaiaSorter hyperspectral sorter. The collected soil reflectance dataset was preprocessed by wavelet transform, before the combination of competitive adaptive reweighted sampling algorithm and successive projections algorithm (CARS-SPA) was used to select the bands optimally. Seven wavelengths of 695, 711, 736, 747, 767, 778, and 796 nm were selected and used as the factors of the SMC inversion model. The popular linear regression algorithm was employed to construct this model. The result indicated that the inversion model established by the multiple linear regression algorithm (the predicted R2 was 0.83 and the RMSE was 0.0078) was feasible and highly accurate, indicating it could play an important role in predicting SMC of cultivated soils over a large area for agricultural irrigation and remote monitoring of crop yields.

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Section: Results Of Inversion Modelsmentioning

confidence: 99%

“…. , n (n represents the total number of experimental datapoints); ε j represents random parameter [27]. In this research, the value of k was set as 2, and the MLR model was combined with independent variables selected from 7 characteristic wavelengths.…”

Section: Multiple Linear Regression Modelmentioning

confidence: 99%

Research on Inversion Model of Cultivated Soil Moisture Content Based on Hyperspectral Imaging Analysis

Jian

Jing-xia

et al. 2020

Agriculture

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“…Bordogna et al [ 33 ] proposes an architecture for managing a spatial data infrastructure to create, collect, and analyze heterogeneous geospatial value sets from multiple sources and time series using web services. Furthermore, the work of Jiang et al [ 34 ] demonstrates that there are various possibilities to integrate statistical modeling techniques and Spatio-temporal data for area-specific crop management. Quinta-Nova et al [ 35 ] propose to identify new areas that can exploit for fruit tree production.…”

Section: Related Workmentioning

confidence: 99%

Climate-Aware and IoT-Enabled Selection of the Most Suitable Stone Fruit Tree Variety

López-Morales

Martı́nez²,

Caro³

et al. 2021

Sensors

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The application of new technologies such as the Internet of Things offers the opportunity to improve current agricultural development, facilitate daily tasks, and turn farms into efficient and sustainable production systems. The use of these new technologies enables the digital transformation process demanded by the sector and provides agricultural collectives with more optimized analysis and prediction tools. Due to climate change, one of the farm industry’s problems is the advance or decay in the cycle of stone fruit trees. The objective is to recommend whether a specific area meets the minimum climatic requirements for planting certain stone fruit trees based on climatic data and bioclimatic indicators. The methodology used implements a large amount of meteorological data to generate information on specific climatic conditions and interactions on crops. In this work, a pilot study has been carried out in the Region of Murcia using an IoT platform. We simulate scenarios for the development of stone fruit varieties better adapted to the environment. Based on the standard, open interfaces, and protocols, the platform integrates heterogeneous information sources and interoperability with other third-party solutions to exchange and exploit such information.

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“…Precision agriculture offers the ability to irrigate and apply chemicals with far greater accuracy and where they are needed most. Increasingly, farm machinery is supplied with onboard sensors that can provide spatial and temporal data useful for determining production [47]. Precision agriculture also offers opportunities for more sustainable management of inputs and thus has potential implications for more effective environmental management [2,47].…”

Section: Industrial Processes 7%mentioning

confidence: 99%

Key Roles for Landscape Ecology in Transformative Agriculture Using Aotearoa—New Zealand as a Case Example

Pearson

2020

Land

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Aotearoa—New Zealand (NZ) is internationally renowned for picturesque landscapes and agricultural products. Agricultural intensification has been economically beneficial to NZ but has implications for its clean green image. Contaminated waterways, high carbon emissions, and extensive soil erosion demonstrate the downside of high stocking rates and land clearing. Transformative farming systems are required to address the challenge of balancing production with the environment. Whilst navigating through the process of change, farmers need to be supported to make informed decisions at the farm and landscape scale. Landscape ecology (LE) is ideally positioned to inform the development of future farming landscapes and provide a scientific context to the criteria against which land-related information can be evaluated. However, to do this effectively, LE needs to demonstrate that it can link theory with practice. Using NZ as a case example, this paper discusses the key roles for LE in future farming systems. It looks at the way LE can help quantify the state of the landscape, provide support towards the co-creation of alternative futures, and assist with the inclusion of land-related information into design and planning to ensure mitigation and adaption responses assist in the transformation of farming systems for sustainable outcomes.

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Integration of Precision Farming Data and Spatial Statistical Modelling to Interpret Field-Scale Maize Productivity

Cited by 13 publications

References 39 publications

Research on Inversion Model of Cultivated Soil Moisture Content Based on Hyperspectral Imaging Analysis

Research on Inversion Model of Cultivated Soil Moisture Content Based on Hyperspectral Imaging Analysis

Climate-Aware and IoT-Enabled Selection of the Most Suitable Stone Fruit Tree Variety

Key Roles for Landscape Ecology in Transformative Agriculture Using Aotearoa—New Zealand as a Case Example

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