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

The Design and Implementation of a Yield Monitor for Sweetpotatoes

A study of the soil characteristics, weather conditions, and effect of management skills on the yield of the agricultural crop requires site-specific details, which involves large amount of labor and resources, compared to the traditional whole field based analysis. This thesis discusses the design and implemention of yield monitor for sweetpotatoes grown in heavy clay soil. A data acquisition system is built and image segmentation algorithms are implemented. The system performed with an R-Square value of 0.80 in estimating the yield. The other main contribution of this thesis is to investigate the effectiveness of statistical methods and neural networks to correlate image-based size and shape to the grade and weight of the sweetpotatoes. An R-Square value of 0.88 and 0.63 are obtained for weight and grade estimations respectively using neural networks. This performance is better compared to statistical methods with an R-Square value of 0.84 weight analysis and 0.61 in grade estimation

ADVANCEMENT IN HARVESTING, PRE- COOLING AND GRADING OF FRUITS.

Generally, quality grading includes outer parameters (size, color intensity, color homogeneity, bruises, shape, stem identification surface texture and mass), inner parameters (sweetness, acidity or inner diseases) and freshness. All horticultural crops are high in water content and are subjected to desiccation and to mechanical injury. That is why these perishable commodities need very careful handling at every stage so that deterioration of produce is restricted as much as possible during the period between harvest and consumption.  Horticultural maturity is the stage of development when plant and plant part possesses the pre- requisites for use by consumers for a particular purpose i.e, ready to harvest. Post harvest handling is the final stage in the process of producing high quality fresh produce. Being able to maintain a level of freshness from the field to the dinner table presents many challenges. A grower who can meet these challenges will be able to expand his or her marketing opportunities and be better able to compete in the market place. Â

Intelligent non-destructive classification of josapine pineapple maturity using artificial neural network

The pineapple maturity level also referred as pineapple maturity index is based on the percentage of yellowish that appears on the pineapple’s skin. In pineapple industry to determine the level of maturity, human experts adopt methods based on their subjective assessment of skin color. To this day, the pineapple maturity sorting process is still performed manually by expert human grader. So in order to reduce errors caused by human factors, there is a need to automate this process to an automated inspection system. The matured fruit harvested for the purposes of local sale or export is complete fruit with crown, fruit body and stump. However, in determining the pineapple maturity index, the main thing to be considered is only the pineapple fruit without crown. Fruit without crown also represents the actual size of the pineapple. Therefore the percentage of yellowish must be proportional to the size of the pineapple. Having extensive search of literatures found that studies of the size of the fruit, especially pineapple are very limited and only been started in recent years. To obtain the actual size of the fruit, the detection Region of Interest (ROI) is using segmentation method called minimum symmetrical edge distance. This minimum symmetrical edge distance algorithm wills geometrical rotated the pineapple images which to align with horizontal axis. Then the shortest vertical distances of the edge is calculated and converted to a background pixel, the largest region (fruit body) is maintained and the small region (crown) was abolished. The performance of segmentation algorithms are calculated using misclassification error that provides the rate of image pixels are incorrectly misclassified into the wrong segment. The results reveal that the algorithm used to achieve overall accuracy up to 99.05%. ROI that has been identified lengthened for feature extraction on the skin color of pineapple. Statistical based features namely minimum, maximum, arithmetic average and standard deviation were extracted from each image channels within detected ROI to represent pineapple skin color's tendency and dispersion. Next, classification index to determine the pineapple maturity level has been applied which are linear classification using thresholding value and artificial neural network adopting pattern recognition method. The results show that the classification using artificial neural network (pattern recognition) involving feature vectors arithmetic average and standard deviation for all channels R, G and B give the average correct classification rate of 88.89%

Hyperspectral Imaging and Their Applications in the Nondestructive Quality Assessment of Fruits and Vegetables

Over the past decade, hyperspectral imaging has been rapidly developing and widely used as an emerging scientific tool in nondestructive fruit and vegetable quality assessment. Hyperspectral imaging technique integrates both the imaging and spectroscopic techniques into one system, and it can acquire a set of monochromatic images at almost continuous hundreds of thousands of wavelengths. Many researches based on spatial image and/or spectral image processing and analysis have been published proposing the use of hyperspectral imaging technique in the field of quality assessment of fruits and vegetables. This chapter presents a detailed overview of the introduction, latest developments and applications of hyperspectral imaging in the nondestructive assessment of fruits and vegetables. Additionally, the principal components, basic theories, and corresponding processing and analytical methods are also reported in this chapter

Monitoring Potato Waste in Food Manufacturing Using Image Processing and Internet of Things Approach

Approximately one-third of the food produced globally is spoiled or wasted in the food supply chain (FSC). Essentially, it is lost before it even reaches the end consumer. Conventional methods of food waste tracking relying on paper-based logs to collect and analyse the data are costly, laborious, and time-consuming. Hence, an automated and real-time system based on the Internet of Things (IoT) concepts is proposed to measure the overall amount of waste as well as the reasons for waste generation in real-time within the potato processing industry, by using modern image processing and load cell technologies. The images captured through a specially positioned camera are processed to identify the damaged, unusable potatoes, and a digital load cell is used to measure their weight. Subsequently, a deep learning architecture, specifically the Convolutional Neural Network (CNN), is utilised to determine a potential reason for the potato waste generation. An accuracy of 99.79% was achieved using a small set of samples during the training test. We were successful enough to achieve a training accuracy of 94.06%, a validation accuracy of 85%, and a test accuracy of 83.3% after parameter tuning. This still represents a significant improvement over manual monitoring and extraction of waste within a potato processing line. In addition, the real-time data generated by this system help actors in the production, transportation, and processing of potatoes to determine various causes of waste generation and aid in the implementation of corrective actions