Minimalist AdaBoost for blemish identification in potatoes

We present a multi-class solution based on minimalist Ad- aBoost for identifying blemishes present in visual images of potatoes. Using training examples we use Real AdaBoost to rst reduce the fea- ture set by selecting ve features for each class, then train binary clas- siers for each class, classifying each testing example according to the binary classier with the highest certainty. Against hand-drawn ground truth data we achieve a pixel match of 83% accuracy in white potatoes and 82% in red potatoes. For the task of identifying which blemishes are present in each potato within typical industry dened criteria (10% coverage) we achieve accuracy rates of 93% and 94%, respectively

Visual detection of blemishes in potatoes using minimalist boosted classifiers

This paper introduces novel methods for detecting blemishes in potatoes using machine vision. After segmentation of the potato from the background, a pixel-wise classifier is trained to detect blemishes using features extracted from the image. A very large set of candidate features, based on statistical information relating to the colour and texture of the region surrounding a given pixel, is first extracted. Then an adaptive boosting algorithm (AdaBoost) is used to automatically select the best features for discriminating between blemishes and non-blemishes. With this approach, different features can be selected for different potato varieties, while also handling the natural variation in fresh produce due to different seasons, lighting conditions, etc. The results show that the method is able to build ``minimalist'' classifiers that optimise detection performance at low computational cost. In experiments, blemish detectors were trained for both white and red potato varieties, achieving 89.6\% and 89.5\% accuracy, respectively

An Algorithm to Extract the Defective Areas of Potato Tubers Infected with Black Scab Disease Using Fuzzy C Means Clustering for Automatic Grading

Estimating the surface area of defects of diseased potatoes is a key factor in the automatic grading of this product. In this article, an algorithm has been developed using fuzzy clustering method and image processing functions to estimate the defective areas of potato tubers infected with black scab disease. Fuzzy clustering, which is an unsupervised method, was used to segment color images and extract defective areas of potatoes, and image processing functions have been used to extract the total area of potatoes. In the segmentation method based on fuzzy clustering, the data matrix related to potato images were divided into separate clusters in a fuzzy way, in which the boundaries of the clusters are defined in a fuzzy way instead of being definite and specific. The results showed that this algorithm is very efficient for extracting black scab disease and can be used to extract the amount of diseases that can be used for automatic grading of this product based on the American standards

Modeling and characterization of potato quality by active thermography

This research focuses on characterizing a potato with extra sugar content and identifying the location and depth of the extra sugar content using the active thermography imaging technique. The extra sugar content of the potato is an important problem for potato growers and potato chip manufacturers. Extra sugar content could result in diseases or wounds in the potato tuber. In general, potato tubers with low sugar content are considered as having a higher quality. The inspection system and general methodologies characterizing extra sugar content will be presented in this study. The average heating rate obtained from the thermal image analysis is the major factor in characterization procedures. Using information on the average heating rate, the probability of achieving a potato with extra sugar content may be predicted using the logistic regression model. In addition, neural networks are also used to identify the potato with extra sugar contents. The correct rate for identifying a potato with extra sugar content in it can reach 85%. The location of extra sugar content can also be found using the logistic regression model. Results show the overall correct rate predicting the extra sugar content location with a resolution of 20 by 20 pixels is 91%. In predicting the extra sugar content depth, amounts exceeds 2/3 inches are not detectable by analyzing thermal images. The depth of extra sugar content can be discriminated in 0.3 inch increments with a high rate of accuracy (87.5%)

Artificial intelligence machine vision grading system

Thesis (M. Tech.) -- Central University of Technology, Free State, 201

Using infrared spectroscopy to evaluate physiological ageing in stored potatoes (Solanum tuberosum)

The potato tuber is one of world’s largest food crops and in most growing regions is only harvested once a year. A proportion of tubers must therefore be stored efficiently to ensure there are enough provisions to last until the next harvest. Dormancy break during storage causes reduced tuber quality and potentially considerable losses. The aim of this work has been to determine whether Vis/NIR Spectroscopy can be used to monitor tuber dormancy, and further, to predict the onset of sprouting within a potato tuber. Small changes in Chlorophyll (Chl) production can be tracked in the tissue under the surface skin of a potato tuber, using a Vis/NIR spectrometer equipped with a fibre-optic probe. A static experimental setup yielded precise measurements of these subtle changes when the tuber was stimulated with light, long before visible greening occurred. It was found that there is a greater capacity for Chl production around the apical buds or “eyes” of a tuber compared with the surrounding tissue. These results held true for several cultivars from multiple harvests over the four years of the project. The technique however is very sensitive to the exact positioning of the tuber-probe alignment, due to the highly localised area of increased activity in the Chl production under an eye and the shape of the tuber itself. Although Chl is not produced in tubers whilst kept in cold dark storage, a tuber’s capacity to produce Chl once removed was found to change over the course of long-term storage. This behaviour was well fitted by a generalised logistic function. Prediction of the onset of dormancy break could be made from the shape of the curve from individual tuber batches. A proviso throughout is that sufficient tubers need to be analysed to obtain a meaningful batch average. The large tuber-to-tuber variance in behaviour remains the greatest challenge to translating this work into real world settings