Bruno Tisseyre

A technical opportunity index based on mathematical morphology for site-specific management: an application to viticulture

The aim of this paper is to provide a method that enables a farmer to: (i) decide whether or not the spatial variation of a field is suitable for a reliable variable-rate application, (ii) to determine if a particular threshold (field segmentation) based on the within-field data is technically feasible with respect to the equipment for application, and (iii) to produce an appropriate application map. Our method provides a Technical Opportunity index (TOi). The novelty of this approach is to process yield data (or other within-field sources of information) with a mathematical morphological filter based on erosions and dilations. This filter enables us to take into account how the machine operates in the field and especially the minimum area (kernel) within which it can operate reliably. Tests on theoretical fields obtained by a simulated annealing procedure and on a real vineyard showed that the TOi was appropriate for assessing whether the spatial variation in a field was technically manageable.

Are precision agriculture tools and methods relevant at the whole-vineyard scale?

Precision viticulture (PV) has been mainly applied at the field level, for which the ability of high resolution data to match within-field variability has been already shown. However, the interest of PV for grape growers would be greater if its principles could also apply at a larger scale, as most growers still focus their management on a multi-field scale, not considering each field as an isolated unit. The aim of this study was to analyse whether it is possible and relevant to use PV tools to define meaningful management zones at the whole-vineyard scale. The study was carried out on a 90-ha vineyard made of 27 contiguous fields. The spatial variability of vine vigour, estimated with the Normalized Difference Vegetation Index (NDVI), was analysed at within-field and whole-vineyard scales. The spatial variability of the vigour was significant and spatially organized whatever the considered scale. Besides, vineyard spatial variability was characterised using information on environmental factors (soil apparent conductivity and elevation) and vine response (yield, vigour and grape composition). At both scales, NDVI and measured environmental factors were used to establish a three-level classification, whose agronomic significance was tested comparing the vine response observed for each class. The analysis of high resolution information allowed the definition of classes with agronomic and oenological implications, although there was not a straightforward correspondence between the classes defined and quality. Analysing the variability at the whole-vineyard scale highlighted a trend of spatial variation associated to elevation that was hardly visible at the within-field level.

Management zone delineation using a modified watershed algorithm

Site-specific management (SSM) is a common way to manage within-field variability. This concept divides fields into site-specific management zones (SSMZ) according to one or several soil or crop characteristics. This paper proposes an original methodology for SSMZ delineation which is able to manage different kinds of crop and/or soil images using a powerful segmentation tool: the watershed algorithm. This image analysis algorithm was adapted to the specific constraints of precision agriculture. The algorithm was tested on high-resolution bio-physical images of a set of fields in France.

A technical opportunity index adapted to zone-specific management

Ten years after the introduction of zone-based management to take into account within-field phenomena in agronomic practices, several methodological developments have progressed to the operational level. However, this raises a new scientific question: how can the relevance of this type of management be evaluated? This paper adapts the concept of a technical opportunity index to zone-specific management. Based on the characteristics of machinery, zoning opportunity is introduced through a new index (ZOI) adapted specifically to zone-based management. This index takes into account the operational conditions in which zoning is applied, together with its associated risks. The results obtained on simulated and real field data highlight the relevance of this index.

Mapping Grapevine (Vitis vinifera L.) Water Status during the Season Using Carbon Isotope Ratio (δ13C) as Ancillary Data

Vine water status is a major parameter for vine management because it affects both wine quality and yield. In order to optimize vineyard management and harvesting practices, it is necessary to characterize vineyard water status spatial variability. This work aims at establishing an empirical spatial model for stem water potential (ΨStem) with ancillary data based on vine water status. Carbon isotope ratio (δ13C) measured at harvest was selected as ancillary data because it reflects only the effect of vine water status variations integrated over the season and is not impacted by other factors such as vine nitrogen status. The proposed model was applied at the intrablock level. It is based on the spatial extrapolation of a ΨStem value measured at a reference site using δ13C values collected over the block. Measurements of ΨStem and δ13C were carried out over three consecutive years on 96 locations within the block. ΨStem values obtained with a spatial model were more accurate than ΨStem values obtained with a nonspatial model, indicating the relevancy of δ13C values to account for spatial variability of vine water status. Results show that operational maps of vine water status can be obtained by means of a spatial model, in which δ13C values from a previous season are used as ancillary data. Maps can be updated at any given time during the season by carrying out a limited number of ΨStem measurements in selected locations. This model offers a tool to monitor vine water status and to implement management practices while considering vine water status intrablock variability.

A technical opportunity index based on the fuzzy footprint of a machine for site-specific management: an application to viticulture

This paper describes a method that allows farmers to (i) decide whether or not the spatial variation of a field allows a reliable variable-rate application, (ii) discover if a particular threshold (field segmentation) based on within-field data is technically feasible according to the application machinery and (iii) make an appropriate application map. Our method aims to improve on a previous technical opportunity index (Oi) with a fuzzy technical opportunity index (FTOi). The FTOi considers (i) a fuzzy footprint model of a variable-rate application controller (VRAC), which describes the area within which the VRAC can operate reliably, (ii) the location inaccuracy of the data and (iii) the ability (accuracy) of the VRAC to perform distinct levels of treatments. The originality of our approach is based on the use of a fuzzy estimation process to decide if a level of treatment is reliable or not for each area over which the VRAC operates. A unique feature of the approach is that it does not require data on a regular grid. Only characteristics of the machinery and the treatment to be applied are necessary; interpolation of the data and geostatistics are not required by the end user. Tests on theoretical fields, obtained from a simulated annealing procedure, showed that the FTOi was able to assess the technical manageability of variation in the field. Tests also showed that our approach could take into account problems related to low resolution data. Finally, the approach has been applied to a real situation in a vineyard block. This has highlighted the practical implementation and the ability to generate useful information for managing the within-field variation (optimal thresholding, and application and error maps).

A new approach for zoning irregularly-spaced, within-field data

Abstract Management zones can be defined as homogeneous regions for which specific management decisions are to be considered. The delineation of these management units is important because it enables or at least facilitate growers and practitioners performing site specific management. The delineation of management zones has essentially been performed by (i) clustering techniques or (ii) segmentation algorithms arising from the image processing domain. However, the first approach does not take into account the spatial relationships in the data, and is prone to generate a large number of fragmented zones while he second methodology has only been dedicated to regularly-spaced, within-field data. This work proposes a new approach to generate contiguous management zones from irregularly-spaced within-field observations, e.g. within-field yield, soil conductivity, soil samples, which are a very important source of data in precision agriculture studies. A seeded region growing and merging algorithm has been specifically designed for these irregularly-spaced observations. More specifically, a Voronoi tessellation was implemented to define spatial relationships between neighbouring observations. Seeds were automatically placed at specific locations across the fields and management zones were first expanded from these seeds. The merging procedure aimed at generating more manageable and interpretable zones. The merging algorithm was defined in a way that made it possible to incorporate machinery and technical management constraints. Experiments demonstrated that the proposed methodology was able to generate relatively compact and contiguous management zones. Furthermore, machinery and technical constraints were shown to significantly influence the results of the delineation which proved the importance of accounting for these considerations.

Within-field zoning using a region growing algorithm guided by geostatistical analysis

Region growing methods are of potential interest to define within-field zones and resulting site-specific management. These methods are unsupervised and based on regions which grow from the initial seeds according to homogeneity criteria. However, the determination of seed number and seed locations has strong repercussions on the zoning output. This paper proposes an approach to allow knowledge inherited from geostatistical analysis to guide the seed initialization (seed number and location) of a region growing-based segmentation method. In this study, the segmentation method is a general division/merging procedure which, in this study, is used for merging and region growing. An original point is the possibility to use it either for irregularly located data or data arranged on a regular grid. The initialization of the segmentation method is guided by a prior analysis where a few parameters of the semi-variogram model are used to set: (1) the number of seeds required to initialize the region growing procedure; (2) to decide their relative locations (i.e. minimal distance between seeds); and (3) to identify potential outliers as seeds that may flaw the growing procedure. Both methods were tested on two data sets: (1) three hypothetical fields of known distribution and known spatial organization; (2) a real field where yield monitor data were obtained. A qualitative analysis of the results is presented, as well as the evolution of the variance explained by the model.

Open source software for modelling using agro-environmental georeferenced data.

In Agronomy and Environment, due to the increasing number of automatic sensors and devices, there is an emerging need to integrate georeferenced and temporal data into decision support tools, traditionally based on expert knowledge. Soft computing techniques and software suited to these needs may be very useful for modelling and decision making. This work presents an open source framework designed for that purpose. It is based upon open source toolboxes, and its design is inspired by the fuzzy software capabilities developed in FisPro for ordinary non georeferenced data. A real world application is included, and some perspectives are given to meet the challenge of using soft computing for georeferenced data.

A general method to filter out defective spatial observations from yield mapping datasets

Yield maps are recognized as a valuable tool with regard to managing upcoming crop production but can contain a large amount of defective data that might result in misleading decisions. These anomalies must be removed before further processing to ensure the quality of future decisions. This paper proposes a new holistic methodology to filter out defective observations likely to be present in yield datasets. The notion of spatial neighbourhood has been refined to embrace the specific characteristics of such on-the-go vehicle based datasets. Observations are compared with their newly-defined spatial neighbourhood and the most abnormal ones are classified as defective observations based on a density-based clustering algorithm. The approach was conceived to be as non-parametric and automated as far as possible to pre-process a growing number of datasets without supervision. The proposed approach showed promising results on real yield datasets with the detection of well-known sources of errors such as filling and emptying times, speed changes and non-fully used cutting bar.