The quality of 'Golden delicious' apples by colour computer vision

peer reviewedA colour machine vision system was developed to form a basis for colour grading and defect inspection of 'Golden delicious' apples. The criteria were based on European Union standards and took into account commercial practices which add subclasses to the basic categories. The system was able to grade correctly more than 90% of the apple for colour (94% by using three colorimetric parameter R, G, B or H,S,I and 91% by using the single canonical variate) and ensured good defect detection (russet, scab, fungi attack, tec.)

Quality fruit grading by colour machine vision: defect recognition.

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Bayesian methods for predicting LAI and soil water content

peer reviewedLAI of winter wheat (Triticum aestivum L.) and soil water content of the topsoil (200 mm) and of the subsoil (500 mm) were considered as state variables of a dynamic soil-crop system. This system was assumed to progress according to a Bayesian probabilistic state space model, in which real values of LAI and soil water content were daily introduced in order to correct the model trajectory and reach better future evolution. The chosen crop model was mini STICS which can reduce the computing and execution times while ensuring the robustness of data processing and estimation. To predict simultaneously state variables and model parameters in this non-linear environment, three techniques were used: Extended Kalman Filtering (EKF), Particle Filtering (PF), and Variational Filtering (VF). The significantly improved performance of the VF method when compared to EKF and PF is demonstrated. The variational filter has a low computational complexity and the convergence speed of states and parameters estimation can be adjusted independently. Detailed case studies demonstrated that the root mean square error (RMSE) of the three estimated states (LAI and soil water content of two soil layers) was smaller and that the convergence of all considered parameters was ensured when using VF. Assimilating measurements in a crop model allows accurate prediction of LAI and soil water content at a local scale. As these biophysical properties are key parameters in the crop-plant system characterization, the system has the potential to be used in precision farming to aid farmers and decision makers in developing strategies for site-specific management of inputs, such as fertilizers and water irrigation.Filtering method-based state and parameter estimation for crop model

Selection of the most efficient wavelength bands for discriminating weeds from crop

peer reviewedThe aim of this study was to select the best combination of filters for detecting various weed species located within carrot rows. In-field images were taken under artificial lighting with a multispectral device consisting of a black and white camera coupled with a rotating wheel holding 22 interference filters in the VIS-NIR domain. Measurements were performed over a period of 19 days, starting 1 week after crop emergence (early weeding can increase yields) and seven different weeds species were considered. The selection of the best filter combination was based on a quadratic discriminant analysis. The best combination of filters included three interference filters, respectively centred on 450, 550 and 700 nm. With this combination, the overall classification accuracy (CA) was 72%. When using only two filters, a slight degradation of the CA was noticed. When the classification results were reported on field images, a systematic misclassification of carrot cotyledons appears. Better results were obtained with a more advanced growth stage. (c) 2007 Elsevier B.V. All rights reserved

Automation in agriculture and in food industry. An example : fruit grading according to their external characteristics

Séminaire de vulgarisation sur l'utilisation des techniques d'analyse d'image pour l'évaluation de la forme de fruit et pour la recherche de défauts

Assessing the potential of an algorithm based on mean climatic data to predict wheat yield

The real-time non-invasive determination of crop biomass and yield prediction is one of the major challenges in agriculture. An interesting approach lies in using process-based crop yield models in combination with real-time monitoring of the input climatic data of these models, but unknown future weather remains the main obstacle to reliable yield prediction. Since accurate weather forecasts can be made only a short time in advance, much information can be derived from analyzing past weather data. This paper presents a methodology that addresses the problem of unknown future weather by using a daily mean climatic database, based exclusively on available past measurements. It involves building climate matrix ensembles, combining different time ranges of projected mean climate data and real measured weather data originating from the historical database or from real-time measurements performed in the field. Used as an input for the STICS crop model, the datasets thus computed were used to perform statistical within-season biomass and yield prediction. This work demonstrated that a reliable predictive delay of 3-4 weeks could be obtained. In combination with a local micrometeorological station that monitors climate data in real-time, the approach also enabled us to (i) predict potential yield at the local level, (ii) detect stress occurrence and (iii) quantify yield loss (or gain) drawing on real monitored climatic conditions of the previous few days.Suivi en temps réel de l’environnement d’une parcelle agricole par un réseau de micro-capteurs en vue d’optimiser l’apport en engrais azoté

Apple shape quantification by using machine vision

peer reviewedGolden delicious apples were characterised automatically by machine vision, taking into account six view of the cheeks and one of the stem end. The Fourier transform of the fruit boundaries were computed and normalised. All the relevant information about the shape was included in the first fifteen harmonics. Moreover, particular harmonics were found to characterise the shape of the Golden delicious variety. By using the amplitude of particular harmonics in a discriminant analysis, apple were sorted into categories defined by EU, with a precision around 96%. The proposed method may also be used to compare varieties objectively, in order to identify relation between physiology and shape.Il est possible de caractériser la forme des pommes Golden delicious de manière automatique par vision artificielle, en prenant six vues des joues des fruits (espacées de 60°) et une vue du pôle pédonculaire. La transformée de Fourier du contour est normalisée; toute l'information relative à la forme d'une pomme est comprise dans les quinze premières harmoniques. Par ailleurs, des harmoniques particulières mettent en évidence des caractéristiques variétales de forme des Golden delicious. En utilisant les amplitudes de ces harmoniques particulières comme variables dans une analyse discriminante, on peut classer les pommes en catégories, telles que définies par les normes de l'Union européenne, avec une précision de l'ordre de 96%. La méthode proposée convient également pour comparer des fruits entre eux de manière objective, en vue de rechercher des relations entre la physiologie et la forme

Yield variability linked to climate uncertainty and nitrogen fertilisation

peer reviewedAt the parcel scale, crop models such as STICS are powerful tools to study the effects of variable inputs such as management practices (e.g. nitrogen (N) fertilisation). In combination with a weather generator, we built up a general methodology that allows studying the yield variability linked to climate uncertainty, in order to assess the best N practice. Our study highlighted that, applying the Belgian farmer current N practice (60-60-60 kg N/ha), the yield distribution was found to be very asymmetric with a skewness of -1.02 and a difference of 5% between the mean (10.5 t/ha) and the median (11.05 t/ha) of the distribution. This implies that, under such practice, the probability for farmers to achieve decent yields, in comparison to the mean of the distribution, was the highest.Suivi en temps réel de l’environnement d’une parcelle agricole par un réseau de micro-capteurs en vue d’optimiser l’apport en engrais azoté

Defining the fine structure of promoter activity on a genome-wide scale with CISSECTOR

Classic promoter mutagenesis strategies can be used to study how proximal promoter regions regulate the expression of particular genes of interest. This is a laborious process, in which the smallest sub-region of the promoter still capable of recapitulating expression in an ectopic setting is first identified, followed by targeted mutation of putative transcription factor binding sites. Massively parallel reporter assays such as survey of regulatory elements (SuRE) provide an alternative way to study millions of promoter fragments in parallel. Here we show how a generalized linear model (GLM) can be used to transform genome-scale SuRE data into a high-resolution genomic track that quantifies the contribution of local sequence to promoter activity. This coefficient track helps identify regulatory elements and can be used to predict promoter activity of any sub-region in the genome. It thus allows in silico dissection of any promoter in the human genome to be performed. We developed a web application, available at cissector.nki.nl, that lets researchers easily perform this analysis as a starting point for their research into any promoter of interest.</p

Development of a multi-spectral vision system for the detection of defects on apples

A method to sort 'Jonagold' apples based on the presence of defects was proposed. A multi-spectral vision system including four wavelength bands in the visible/NIR range was developed. Multi-spectral images of sound and defective fruits were acquired tending to cover the whole colour variability of this bicolour apple variety. Defects were grouped into four categories: slight defects, more serious defects, defects leading to the rejection of the fruit and recent bruises. Stem-ends/calyxes were detected using a correlation pattern matching algorithm. The efficiency of this method depended on the orientation of the stem-end/calyx according to the optical axis of the camera. Defect segmentation consisted in a pixel classification procedure based on the Bayes' theorem and non-parametric models of the sound and defective tissue. Fruit classification tests were performed in order to evaluate the efficiency of the proposed method. No error was made on rejected fruits and high classification rates were reached for apples presenting serious defects and recent bruises. Fruits with slight defects presented a more important misclassification rate but those errors fitted however the quality tolerances of the European standard. Considering an actual ratio of sound fruits of 90%, less than 2% of defective fruits were classified into the sound ones. (c) 2004 Elsevier Ltd. All rights reserved