GeoFIS: An Open Source, Decision-Support Tool for Precision Agriculture Data

Leroux, C.; Jones, Hazaël; Pichon, L.; Guillaume, Serge; Lamour, Julien; Taylor, James A.; Naud, Olivier; Crestey, Thomas; Lablee, Jean-Luc; Tisseyre, Bruno

doi:10.3390/agriculture8060073

Cited by 41 publications

(11 citation statements)

References 32 publications

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“…Maps were obtained using point kriging interpolation. Kriging was performed with the GeoFis 1.0 software [29], which was used for: (1) the modelling of semivariograms and calculations of their featured parameters, C 0 (nugget effect), C 1 (sill) and r (range), and (2) the kriging interpolation. The latter was performed on a grid of regularly spaced points Semivariogram features were also used to compute the Cambardella Index (I c ) [30] (Equation ( 1)):…”

Section: Mapping and Spatial Analysismentioning

confidence: 99%

Is It Possible to Assess Heatwave Impact on Grapevines at the Regional Level with Time Series of Satellite Images?

et al. 2022

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Unexpected climatic conditions or extreme climatic events in vineyards are a worldwide problem that requires accurate spatial and temporal monitoring. Satellite-based remote sensing is an important source of data to assess this challenge in a climate-change context. This paper provides a first insight into the capacity of a multiway analysis method applied to Sentinel-2 time series to assess heatwave impacts in vineyards at a regional scale. Multi-way partial least squares (N-PLS) regression was used as a supervised technique to predict the intensity of damage caused to vineyards by the heatwave phenomenon that impacted the vineyards in the south of France in 2019. The model was developed based on available ground truth data of yield losses for 107 vineyard blocks in the Languedoc-Roussillon region and multispectral time-series predictor data for the period May to August 2019. The model showed a performance accuracy (R2) of 0.56 in the calibration set and of 0.66 in the validation set, with a standard error of cross-validation in the calibration set of 12.4% and a standard error of the prediction of yield losses in the validation set of 10.7. The model was applied at a regional scale on 4978 vineyard blocks to predict yield losses using spectral and temporal attributes. The prediction of the yield loss due to heat stress at a regional scale was related to the spatial pattern of maximum temperatures recorded during the extreme weather event. This relation was confirmed by a chi-square test (p < 5%). The introduction of N-PLS insights into the analysis enables the characterisation of heat stress responses in vineyards and the identification of spectro-temporal profiles relevant for understanding the effects of heatwaves on vine blocks at a regional scale.

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Section: Mapping and Spatial Analysismentioning

confidence: 99%

Is It Possible to Assess Heatwave Impact on Grapevines at the Regional Level with Time Series of Satellite Images?

et al. 2022

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“…The interpolation method used in this study was based on universal kriging. Semivariogram analysis and interpolation were performed with GeoFIS (Leroux et al, 2018).…”

Section: Data Processing and Mappingmentioning

confidence: 99%

Empirical mapping for evaluating an LPWAN (LoRa) wireless network sensor prior to installation in a vineyard

et al. 2021

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The main aim of this study was to use Empirical mapping to test the efficiency of local low cost wireless network sensors (LPWAN - Low-Power Wide Area Network) before being applied in real wine-growing conditions. The second aim was to obtain information on the communication distances to be expected from a LPWAN, taking into account the specific needs and real conditions of a vineyard. A hand-held autonomous end-device was specifically built to simulate short messages sent by sensors via a locally designed LPWAN. This device was used to test the quality of the network from different locations within an entire vineyard and also inside the cellar. Two parameters were used to test the quality of reception of the messages: i) The Received Signal Strength Indication (RSSI), which is the received signal power measured in decibels (dB or dBm), and ii) the Signal-to-Noise Ratio (SNR), which is the ratio of the received signal power to the ambient noise power. Maps of signal reception and errors between the observed and the theoretical signal highlighted how vineyard environment (e.g., hedges, topography, and buildings) affects the signal. The results show that the maximum communication distance differed considerably from distances published in the literature. In the open field, the signal, although attenuated by the distance, was received up to 600 meters away, or even more in favourable conditions. Meanwhile, in urban areas the signal was attenuated by buildings and the electro-magnetic environment and therefore communication distances were very short (< 50 m). Empirical mapping has great potential for determining how the local environment affects signal quality and as a decision support tool for identifying the optimal location for the sensors and gateway. With a single well-positioned gateway, such low cost wireless sensor networks (LPWAN-LoRa) could be used by small to medium-sized vineyards to collect information from sensors either outside in the fields or indoors in the vineyard cellar. This paper proposes a very cheap method (< 40 €) for testing and spatialising the quality of a low cost wireless sensor network before its implementation, and it also provides information on zones with low quality reception.

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“…The mean annual precipitation and temperature in the study area are 440 mm and 15.8 o C, respectively, The primary and most important procedure for assessing soil quality status is soil analysis. Zonation through remote sensing techniques (RSTs) and geographical information systems (GISs) have played a key role for a) optimizing the number of sampling sites for the development of representative sampling networks based on the detection of spatial soil differences and crop growth anomalies [26][27][28], b) the identification of the possible origin of such differences/anomalies through in situ observations/samplings, and c) the design of faster and more targeted interventions. On the other hand, it is very important to stress the high cost of such actions in large agricultural areas and the need for additional indirect methods in order to further reduce their cost through even more targeted soil samplings and soil management interventions.…”

Section: Study Areamentioning

confidence: 99%

An Integrated Approach to Assessing the Soil Quality and Nutritional Status of Large and Long-Term Cultivated Rice Agro-Ecosystems

Aschonitis¹,

Karydas²,

Iatrou³

et al. 2019

Agriculture

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The aim of this study is to develop an integrated approach to soil quality and fertility assessment in high-yielding rice agro-ecosystems threatened due to overexploitation of soil resources by intensive agriculture. The proposed approach is implemented considering representative pilot fields allocated throughout a study area based on the assumption that soils of similar general properties present a similar nutritional status due to common long-term management practices. The analysis includes (a) object-based image analysis for land zonation, (b) hot-spot analysis for sampling scheme evaluation, (c) setting of critical thresholds in soil parameters for detecting nutrient deficiencies and soil quality problems, and (d) Redundancy Analysis, TITAN analysis, and multiple regression for identifying individual or combined effects of general soil properties (e.g., organic matter, soil texture, pH, salinity) or non-soil parameters (e.g., topographic parameters) on soil nutrients. The approach was applied using as a case study the large rice agro-ecosystem of Thessaloniki plain in Greece considering some site specificities (e.g., high rice yields, calcareous soils) when setting the critical thresholds in soil parameters. The results showed that (a) 62.5% of the pilot fields’ coverage has a simultaneous deficiency in Zn, Mn, and B, (b) organic matter (OM) was the most significant descriptor of nutrients’ variance, and its cold spots (clustered regions of low OM values) showed important overlapping with the cold spots of K, Mg, Zn, Mn, Cu, and B, (c) a higher rate of availability increase in P, K, Mg, Mn, Zn, Fe, Cu, and B was observed when the OM ranged between 2 and 3%, and (d) the multiple regression models that assess K and P concentrations based on general soil properties showed an adequate performance, allowing their use for general assessment of their soil concentrations in the fields of the whole agro-ecosystem.

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GeoFIS: An Open Source, Decision-Support Tool for Precision Agriculture Data

Cited by 41 publications

References 32 publications

Is It Possible to Assess Heatwave Impact on Grapevines at the Regional Level with Time Series of Satellite Images?

Is It Possible to Assess Heatwave Impact on Grapevines at the Regional Level with Time Series of Satellite Images?

Empirical mapping for evaluating an LPWAN (LoRa) wireless network sensor prior to installation in a vineyard

An Integrated Approach to Assessing the Soil Quality and Nutritional Status of Large and Long-Term Cultivated Rice Agro-Ecosystems

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