Agricultural tools guidance assistance by using machine vision

in Proceedings of the NCTAM 2006 7th National Congress on Theoretical and Applied Mechanics (2006, May 30)

This paper presents a regulation mechanism aiming to position agricultural tools relatively to the previous lines, while sowing or harvesting. The sowing rows were revealed by a background correction, the ... [more ▼]

This paper presents a regulation mechanism aiming to position agricultural tools relatively to the previous lines, while sowing or harvesting. The sowing rows were revealed by a background correction, the background being obtained thanks to a median rank filter. The method was found efficient in eliminating the shadows. For the crop rows (chicory rows), a neural network was used to localise the plants. While the petiole and the leaves were easily separated from the soil, the chicory root and the soil having about the same colour and the lighting condition varying widely, it was more difficult to obtain a good contrast between those parts, which leaves place for some improvements. The adapted Hough transform consisted in computing one transform for each line in the cluster with, for reference, the position and direction of the theoretical position of the row. The different transforms were then added. The position was used in a feedback regulation loop. An articulated mechanism was used to ensure the lateral displacement of the tool relatively to the tractor. The behaviour of the whole outfit was studied during several field tests. The standard deviation of the error, measured as the difference between the observed inter-row distance and its theoretica value, was of 23 mm for sowing and 31 mm for harvesting and its amplitude was less than 100 mm for sowing and less than 115 mm during the harvest, which was sufficient to fulfil the requirements of the application. Sources of systematic errors were also identified as linked to the geometric considerations. Their correction requires an accurate mounting of the camera, which may be possible for a serial montage. [less ▲]

Line Cluster Detection Using A Variant Of The Hough Transform For Culture Row Localisation

in Image and Vision Computing (2006), 24(5),

An adaptation of the Hough transform was proposed for the detection of line clusters of known geometry. This method was applied in agriculture for the detection of sowing furrows created by a driller and ... [more ▼]

An adaptation of the Hough transform was proposed for the detection of line clusters of known geometry. This method was applied in agriculture for the detection of sowing furrows created by a driller and of chicory plant rows during harvesting process. The sowing rows were revealed by a background correction, the background being obtained thanks to a median rank filter. The method was found efficient in eliminating the shadows. For the crop rows, a neural network was used to localise the plants. While the petiole and the leaves were easily separated from the soil, the chicory root and the soil having about the same colour and the lighting condition varying widely, it was more difficult to obtain a good contrast between those parts, which leaves place for some improvements. The adapted Hough transform consisted in computing one transform for each line in the cluster with, for reference, the position and direction of the theoretical position of the row. The different transforms were then added. It was found effective for both the sowing rows and the chicory rows. Results remained good even in very noisy conditions, when the rows were incomplete or when artefacts would lead its classical counter part to show several alignments other than the expected ones. The culture rows were localised with a precision of a few centimetres which was compatible with the proposed applications. [less ▲]

A guidance assistance method for precision sugar beet sowing using machine vision.

in Pr. Josse De Baerdemaeker (Ed.) AgEng 2004 Conference - Engineering the future - Book of abstracts (2004, September)

This paper presents a method for seed drill guidance by using machine vision

Selection of the most efficient wavelength bands for 'Jonagold' apple sorting

in Postharvest Biology & Technology (2003), 30(3), 221-232

This paper presents a method based on quadratic discriminant analysis to select the best filters for detecting a wide range of defects in 'Jonagold' apple fruit using a multi-spectral vision system ... [more ▼]

This paper presents a method based on quadratic discriminant analysis to select the best filters for detecting a wide range of defects in 'Jonagold' apple fruit using a multi-spectral vision system. Reflectance spectra of damaged and sound tissue were recorded using a visible/NIR spectrometer. Analysed defects consisted of scald, hail damage (with and without skin perforation), limb rubs, russets, scab tissue, frost damage, rot, visible flesh damage and recent bruises. Camera filter effects were approximated by summing the reflectances of all the wavelengths within the filter bandwidth. Combinations of three and four filters were tested and evaluated for discriminating damaged tissues from healthy ones. If a three-filter combination appeared sufficient to detect most of the damaged tissue, a four-filter combination should be considered for the complete sorting automation of this bicolour apple variety. A fourth filter was necessary to quantify the ratio between the blush and ground colours. Regarding recent bruise defects which represented the major difficulty, an image segmentation algorithm based on local contrast variations can enhance their detection. (C) 2003 Elsevier B.V. All rights reserved. [less ▲]

Contrôle des produits agroalimentaires par analyse d'images

in Nouveaux capteurs pour la maîtrise de la qualité des produits agroalimentaires - Etat de la recherche européenne (2002, November 22)

Présentation de méthodes utilisées dans un processus de contrôle de qualité basé sur l'analyse d'image, en se focalisant sur la couleur et la forme des produits et leur classification

Application of image analysis to the identification and rating of road surface distress

in Correia; Branco (Eds.) Bearing Capacity of Roads, Railways and Airfields (2002)

Numerical image analysis is used to detect narrow cracks on bituminous pavement. This problem is complicated because of the variable road aspect, which depends on coarseness textures, changing ambient ... [more ▼]

Numerical image analysis is used to detect narrow cracks on bituminous pavement. This problem is complicated because of the variable road aspect, which depends on coarseness textures, changing ambient lighting, presence of humidity and because of the poor contrast of the cracks with regard to road texture. The paper presents algorithms suited to detect random cracks edges in a noisy environment in three stages. The pre-treatment consisted mainly in applying a background correction to eliminate the heterogeneity due to humidity, shade, ... In the treatment, a threshold value was applied to segment the 'object' from the rest of the image. As these objects may be cracks, part cracks, or some noise erroneously segmented as defect, a post-treatment was applied to appreciate more accurately if a pixel belonged to an object or to the background. It aimed also to assembly parts of the cracks in continuous structure. When compared to visual detection, efficient detection of cracks is obtained. Further work needs to be done to get an automatic detection of the cracks whatever the road texture. [less ▲]

Développement de méthodes pour apprécier 'en ligne' la qualité externe de pommes par vision artificielle

Book published by Ministère des Classes moyennes et de l'Agriculture - DG6 -Service Recherche subventionnée (2001)

L'ouvrage présente les résultats des recherches effectuées pour mettre au point un système de tri automatisé pour pommes, en fonction des critères de qualité externe, la couleur, la forme et la présence ... [more ▼]

L'ouvrage présente les résultats des recherches effectuées pour mettre au point un système de tri automatisé pour pommes, en fonction des critères de qualité externe, la couleur, la forme et la présence de défauts. [less ▲]

Contribution au classement des fruits par analyse d'images numériques. Application au tri en ligne des pommes Golden delicious et Jonagold.

Doctoral thesis (2000)

This work aims to set up an automatic quality fruit grading method based on external characteristics. Special attention is drawn to various defects such as wounds, bruises, physiological diseases, fungi ... [more ▼]

This work aims to set up an automatic quality fruit grading method based on external characteristics. Special attention is drawn to various defects such as wounds, bruises, physiological diseases, fungi attack, etc. Two 3-CCD cameras mounted on a test rig were used for the image acquisition. Golden delicious apples are characterised by their uniform colour. This later was modelled by a multivariate Gaussian distribution and the defect detection was carried out by computing the Mahalanobis distance separating a pixel's colour and the mean colour of the fruit. For Jonagold apples, having a multimodal colour frequency distribution, the defect location was based on a non-parametric model of the fruit colour and on Bayes' theorem. In both cases, the development of an algorithm, taking into account local information, enhanced the segmentation precision. The calyx and stem ends, which appear as defects on the image, were detected by a pattern correlation technique. The segmented areas (poles, defects and over-segmentation zones) were characterised with shape, colour and texture descriptors. The fruit grading into four classes (Extra, A, B and cull) according to European standards is obtained by using a cluster analysis on the segmented regions. The results obtained are favourable and make it possible to envisage the transfer of developed algorithms onto a commercial sorting machine. [less ▲]

Defect segmentation on 'Jonagold' apples using colour vision and a Bayesian classification method

in Computers & Electronics in Agriculture (1999), 23(1), 43-53

This paper shows how the information enclosed in a colour image of a bi-colour apple can be used to segment defects. A method to segment pixels, based on a Bayesian classification process, is proposed ... [more ▼]

This paper shows how the information enclosed in a colour image of a bi-colour apple can be used to segment defects. A method to segment pixels, based on a Bayesian classification process, is proposed. The colour frequency distributions of the healthy tissue and of the defects were used to estimate the probability distribution of each class. The results showed that most defects, namely bitter pit, fungi attack, scar tissue, frost damages, bruises, insect attack and scab, are segmented. However, russet was sometimes confused with the transition area between ground and blush colour. (C) 1999 Elsevier Science B.V. All rights reserved. [less ▲]

Application of neural networks in quality control of 'Jonagold' apples

in Bellon-Maurel, Véronique (Ed.) Sensoral 98 - International workshop on sensing quality of agricultural products (1998, February)

This paper studies the possibilities to grade the 'Jonagold', a bicolour apple, using neural networks and Fisher"s linear discriminant analysis. In a first step the pixels are sorted into three areas, the ... [more ▼]

This paper studies the possibilities to grade the 'Jonagold', a bicolour apple, using neural networks and Fisher"s linear discriminant analysis. In a first step the pixels are sorted into three areas, the blush colour area, the intermediate colour area and the ground colour area; the accuracy reached 95% with the neural networks. Int the second step the fruit are graded into four categories on their ground colour basis; the accuracy reached 68%whatever the method used. It si also shown that there is no need to separate the ground colour and the intermediate colour to compute the ground colour classification parameters. [less ▲]