Computer Vision Algorithms For An Automated Harvester

Image classification and segmentation are the two main important parts in the 3D vision system of a harvesting robot. Regarding the first part, the vision system aids in the real time identification of contaminated areas of the farm based on the damage identified using the robot’s camera. To solve the problem of identification, a fast and non-destructive method, Support Vector Machine (SVM), is applied to improve the recognition accuracy and efficiency of the robot. Initially, a median filter is applied to remove the inherent noise in the colored image. SIFT features of the image are then extracted and computed forming a vector, which is then quantized into visual words. Finally, the histogram of the frequency of each element in the visual vocabulary is created and fed into an SVM classifier, which categorizes the mushrooms as either class one or class two. Our preliminary results for image classification were promising and the experiments carried out on the data set highlight fast computation time and a high rate of accuracy, reaching over 90% using this method, which can be employed in real life scenario. As pertains to image Segmentation on the other hand, the vision system aids in real time identification of mushrooms but a stiff challenge is encountered in robot vision as the irregularly spaced mushrooms of uneven sizes often occlude each other due to the nature of mushroom growth in the growing environment. We address the issue of mushroom segmentation by following a multi-step process; the images are first segmented in HSV color space to locate the area of interest and then both the image gradient information from the area of interest and Hough transform methods are used to locate the center position and perimeter of each individual mushroom in XY plane. Afterwards, the depth map information given by Microsoft Kinect is employed to estimate the Z- depth of each individual mushroom, which is then being used to measure the distance between the robot end effector and center coordinate of each individual mushroom. We tested this algorithm under various environmental conditions and our segmentation results indicate this method provides sufficient computational speed and accuracy

Early detection of cobweb disease infection on Agaricus bisporus sporocarps using hyperspectral imaging

From the nineteen-nineties, cobweb disease caused serious losses for the mushroom sector in Europe, in the USA, and in Australia (Fletcher & Gaze, 2008), so it is one of the most notable fungal infections of cultivated white button mushroom (Agaricus bisporus). The aim of this study was to identify cobweb disease (Cladobortyum dendroides) caused cap spotting and brownish rot on the mushroom sporocarp, and to find a proper discrimination method in the case of this infection.Fruiting body samples were divided into 4 groups, a control one and three others treated with different chemicals that are tested against fungal infections. The groups were subdivided into 2 portions and the first was infected with cobweb disease. Images of the caps were recorded and their hyperspectral images were acquired in the wavelength range of 900–1700 nm.On the hyperspectral images infected and healthy areas were selected, on these average spectra differences were found around the known water peaks (1200 and 1450 nm). The spatial distribution of the water content can be used for the detection of the spoilage, because the infected areas showed different reflection values at these water absorption peaks.Support Vector Machine method was applied successfully to discriminate between the infected and control groups and Monte Carlo cross-validation was carried out

A review of optical nondestructive visual and near-infrared methods for food quality and safety

This paper is a review of optical methods for online nondestructive food quality monitoring. The key spectral areas are the visual and near-infrared wavelengths. We have collected the information of over 260 papers published mainly during the last 20 years. Many of them use an analysis method called chemometrics which is shortly described in the paper. The main goal of this paper is to provide a general view of work done according to different FAO food classes. Hopefully using optical VIS/NIR spectroscopy gives an idea of how to better meet market and consumer needs for high-quality food stuff.©2013 the Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioitu|en=peerReviewed

Meat Quality Assessment by Electronic Nose (Machine Olfaction Technology)

Over the last twenty years, newly developed chemical sensor systems (so called “electronic noses”) have made odor analyses possible. These systems involve various types of electronic chemical gas sensors with partial specificity, as well as suitable statistical methods enabling the recognition of complex odors. As commercial instruments have become available, a substantial increase in research into the application of electronic noses in the evaluation of volatile compounds in food, cosmetic and other items of everyday life is observed. At present, the commercial gas sensor technologies comprise metal oxide semiconductors, metal oxide semiconductor field effect transistors, organic conducting polymers, and piezoelectric crystal sensors. Further sensors based on fibreoptic, electrochemical and bi-metal principles are still in the developmental stage. Statistical analysis techniques range from simple graphical evaluation to multivariate analysis such as artificial neural network and radial basis function. The introduction of electronic noses into the area of food is envisaged for quality control, process monitoring, freshness evaluation, shelf-life investigation and authenticity assessment. Considerable work has already been carried out on meat, grains, coffee, mushrooms, cheese, sugar, fish, beer and other beverages, as well as on the odor quality evaluation of food packaging material. This paper describes the applications of these systems for meat quality assessment, where fast detection methods are essential for appropriate product management. The results suggest the possibility of using this new technology in meat handling

Visible and Hyperspectral Imaging Systems for the Detection and Discrimination of Mechanical and Microbiological Damage of Mushrooms

Horticultural products such as mushrooms are exposed to environmental conditions during their postharvest life, which may affect product quality. Loss of whiteness during storage is particularly important in the mushroom industry. Rough handling and distribution, fruiting body senescence and bacterial infections are among the main causes of mushroom discolouration. The aim of this work was to study the use of visible and hyperspectral imaging (HSI) systems for the detection and discrimination of mechanical and microbiological damage of mushrooms. This piece of research involved a) monitoring the browning of mushroom with visible computer imaging systems, b) investigating the effect of mechanical damage on the kinetics of enzymes responsible for mushroom browning, c) exploring the potential use of Vis-NIR HSI to predict PPO activity in mushroom caps and d) studying the potential application of Vis-NIR HSI for microbial and viral detection on mushroom caps and for their discrimination from mechanical damage. Results presented in this thesis show that the efficacy of commercial webcams was limited in the detection of mechanical damage on mushroom caps. Damage increased the activity of PPOs on mushroom pileipellis, but the effect of the extent of damage was not significant at the levels of study. Vis-NIR HSI showed some potential as a tool to estimate the activity of PPO enzymes on mushroom caps. The combination of HSI with chemometric tools allowed for the differentiation of mechanically and microbiologically damaged mushroom classes. Results from this study could be used for developing non-destructive monitoring systems for mechanical and microbiological damage detection and discrimination. The potential application of such systems as on-line process analytical tools would facilitate rapid assessment of mushroom quality.

Recent Advances in Reducing Food Losses in the Supply Chain of Fresh Agricultural Produce

Fruits and vegetables are highly nutritious agricultural produce with tremendous human health benefits. They are also highly perishable and as such are easily susceptible to spoilage, leading to a reduction in quality attributes and induced food loss. Cold chain technologies have over the years been employed to reduce the quality loss of fruits and vegetables from farm to fork. However, a high amount of losses (≈50%) still occur during the packaging, transportation, and storage of these fresh agricultural produce. This study highlights the current state-of-the-art of various advanced tools employed to reducing the quality loss of fruits and vegetables during the packaging, storage, and transportation cold chain operations, including the application of imaging technology, spectroscopy, multi-sensors, electronic nose, radio frequency identification, printed sensors, acoustic impulse response, and mathematical models. It is shown that computer vision, hyperspectral imaging, multispectral imaging, spectroscopy, X-ray imaging, and mathematical models are well established in monitoring and optimizing process parameters that affect food quality attributes during cold chain operations. We also identified the Internet of Things (IoT) and virtual representation models of a particular fresh produce (digital twins) as emerging technologies that can help monitor and control the uncharted quality evolution during its postharvest life. These advances can help diagnose and take measures against potential problems affecting the quality of fresh produce in the supply chains. Plausible future pathways to further develop these emerging technologies and help in the significant reduction of food losses in the supply chain of fresh produce are discussed. Future research should be directed towards integrating IoT and digital twins in order to intensify real-time monitoring of the cold chain environmental conditions, and the eventual optimization of the postharvest supply chains. This study gives promising insight towards the use of advanced technologies in reducing losses in the postharvest supply chain of fruits and vegetables

Food Quality

The book discusses the novel scientific approaches for the improvement of the food quality and offers food scientists valuable assistance for the future. The detailed methodologies and their practical applications could serve as a fundamental reference work for the industry and a requisite guide for the research worker, food scientist and food analyst. It will serve as a valuable tool for the analysts improving their knowledge with new scientific data for quality evaluation. Two case study chapters provide data on the improvement of food quality in marine and land organisms in the natural environment

Metabolic Profiling and Fingerprinting for the Detection and Discrimination of Mechanical Damage in Mushrooms (Agaricus bisporus) during Storage

Horticultural products such as mushrooms are exposed to external agents during their postharvest life, which are going to affect product quality. Loss of whiteness during storage is particularly important in the mushroom industry. Rough handling and distribution of crops, fruiting body senescence, bacterial and viral infection are among the causes of mushroom discolouration. The aim of this work was to study the use of metabolic fingerprinting and metabolic profiling tools for the detection and discrimination of mechanical damage on mushrooms. This research involved: 1. Investigating whether the chemical changes induced by mechanical damage and ageing of mushrooms could be (a) detected in the mid-infrared absorption region using FTIR spectroscopy as a fingerprinting tool and (b) identified using chemometric data analysis. 2. Investigating metabolites in mushroom tissues using GC/MS as a metabolic profiling technique. The method was used to profile mushroom samples to identify metabolic markers for damage and to gain understanding of the many metabolic processes that occur. 3. Studying low levels of damage in mushrooms using NMR spectroscopy as a fingerprinting technique coupled with chemometrics to identify markers and determine metabolite structure. The results from this study show the usefulness of FTIR spectroscopy coupled with chemometric data analysis for evaluating damage in mushrooms with specific wavenumbers identified. Metabolic profiling using GC/MS has led to a library of metabolites being built. Specific metabolites have been identified as markers for damage

7th International ISEKI-Food Conference: next-generation of food research, education and industry. Book of abstracts

As part of its mission, ISEKI-Food Association establishes and maintains a network among universities, research institutions, and companies in the food chain in addition to working to ensure that food studies are of high quality. However, we must also begin planning how to gear science, education, and the food industry to meet the needs of future generations as well as how to contribute to the sustainability of our planet by these food actors. In light of this, the 7th International ISEKI-Food Conference, which had as main theme “NEXT-GENERATION OF FOOD RESEARCH, EDUCATION AND INDUSTRY”, focused on future challenges in education on food science and technology, in research activities related to processing, quality and safety, packaging of foods and in societal engagements in the field divided in three main sections: EDUCATION: CHALLENGES OF EDUCATION IN A CHANGING WORLD; RESEARCH: NEXT GENERATION OF FOODS; and SOCIETY ENGAGEMENT: SOCIETY AND FOOD INDUSTRY. The conference was dedicated to all food actors, creating bridges among them. The delegates had the opportunity to exchange new ideas and experiences face to face, to establish business or research relations, and find global partners for future collaborations.info:eu-repo/semantics/publishedVersio