Color characterization comparison for machine vision-based fruit recognition

In this paper we present a comparison between three color characterizations methods applied for fruit recognition, two of them are selected from two related works and the third is the authors’ proposal; in the three works, color is represented in the RGB space. The related works characterize the colors considering their intensity data; but employing the intensity data of colors in the RGB space may lead to obtain imprecise models of colors, because, in this space, despite two colors with the same chromaticity if they have different intensities then they represent different colors. Hence, we introduce a method to characterize the color of objects by extracting the chromaticity of colors; so, the intensity of colors does not influence significantly the color extraction. The color characterizations of these two methods and our proposal are implemented and tested to extract the color features of different fruit classes. The color features are concatenated with the shape characteristics, obtained using Fourier descriptors, Hu moments and four basic geometric features, to form a feature vector. A feed-forward neural network is employed as classifier; the performance of each method is evaluated using an image database with 12 fruit classes

Simple Vision System for Apple Varieties Classification

AbstractEvery variety of apple has its particular physical characteristics, which are affected by different pre-harvest factors. Manual classification of these varieties by human labor has several weaknesses, such as the inconsistency, subjectivity, fatigue and different accuracy due to different level of experience of the inspector. This study was aimed to design and evaluate a simple computer-based vision system for recognizing and grading several varieties of apples based on their physical characteristics. Images of apples were taken and were used as training data with different algorithms to extract the particular characteristics of each variety, such as color and shape. The extracted Hue color channels and contour vector were recorded as the reference data and were used to recognize the similar characteristic of those images from the testing data group. The k-nearest neighbors algorithm was used to decide whether an apple belongs to a particular variety. The results show that the recognition rate based on color only was between 84–97% and it was between 5–77% it is based on the shape only. Rotating the image significantly increases the recognition rate (to be between 5 - 69% based on the shape only). Moreover, combining both color and shape characteristics significantly improves the recognition rate.Keywords: apple’s varieties classification, color signatures, combined color-morphology signatures, morphology signature, vision system AbstrakSetiap jenis buah apel memiliki penciri fisik spesifik, yang dipengaruhi oleh berbagai faktor pra-panen. Teknik klasifikasi manual memiliki banyak kelemahan, antara lain adalah subjektifitas, ketidakkonsistenan, kelelahan fisik dan psikologis, serta tingkat pengalaman dari petugas yang melakukannya. Tujuan studi ini adalah melakukan proses desain dan pengujian suatu sistem visi sederhana berbasis komputer untuk mengenali dan mengklasifikasi berbagai jenis buah apel berdasarkan penciri spesifiknya. Citra buah apel dari sampel latih diproses dengan berbagai algoritma untuk mengekstraksi berbagai parameter pencirinya, yaitu parameter warna dan bentuk. Informasi histogram kanal warna Hue dan vektor kontur hasil ekstraksi kemudian disimpan sebagai data referensi dan digunakan sebagai pembanding terhadap parameter serupa dari citra data uji. Keputusan diambil menggunakan algoritma K-Nearest Neighbors. Hasil menunjukkan bahwa laju pengenalan berbasis fitur tunggal warna berkisar antara 84–97%, sementara berbasis fitur tunggal morfologi berkisar antara 5–77%. Perubahan orientasi sampel sebagai data training akan meningkatkan laju pengenalan berbasis fitur tunggal morfologi secara signifikan, yaitu dari 5% menjadi 69%. Penggabungan dua fitur penciri warna dan morfologi dapat meningkatkan laju pengenalan lebih baik lagi.Kata Kunci: klasifikasi jenis buah apel, penciri warna, penciri morfologi, gabungan penciri warna dan morfologi, sistem vis

Real-Time Visual Inspection System for Identification of Fruits and Veggies Using Computer Vision

This paper presents the framework for identifying fruits and veggies with a Fused descriptor-based approach by applying computer vision techniques. The construction of the proposed system is isolated into three phases: 1) Derivation, 2) extraction 3) portrayal. From the start, K-infers gathering techniques were done for establishment derivation. The subsequent step applies the variety, surface, and shape-based highlight extraction strategy. At last, A "consolidating" combination highlight is investigated with a C4.5, SVM, and KNN. In general, the acknowledgment framework creates a sufficient exhibition exactness with upsides of 97.89, 94.60, and 90.25 rates by using C4.5, SVM, and KNN separately. The trial and error bring up that the proposed combination plan can uphold precisely perceiving different soil products

Flexible system of multiple RGB-D sensors for measuring and classifying fruits in agri-food Industry

The productivity of the agri-food sector experiences continuous and growing challenges that make the use of innovative technologies to maintain and even improve their competitiveness a priority. In this context, this paper presents the foundations and validation of a flexible and portable system capable of obtaining 3D measurements and classifying objects based on color and depth images taken from multiple Kinect v1 sensors. The developed system is applied to the selection and classification of fruits, a common activity in the agri-food industry. Being able to obtain complete and accurate information of the environment, as it integrates the depth information obtained from multiple sensors, this system is capable of self-location and self-calibration of the sensors to then start detecting, classifying and measuring fruits in real time. Unlike other systems that use specific set-up or need a previous calibration, it does not require a predetermined positioning of the sensors, so that it can be adapted to different scenarios. The characterization process considers: classification of fruits, estimation of its volume and the number of assets per each kind of fruit. A requirement for the system is that each sensor must partially share its field of view with at least another sensor. The sensors localize themselves by estimating the rotation and translation matrices that allow to transform the coordinate system of one sensor to the other. To achieve this, Iterative Closest Point (ICP) algorithm is used and subsequently validated with a 6 degree of freedom KUKA robotic arm. Also, a method is implemented to estimate the movement of objects based on the Kalman Filter. A relevant contribution of this work is the detailed analysis and propagation of the errors that affect both the proposed methods and hardware. To determine the performance of the proposed system the passage of different types of fruits on a conveyor belt is emulated by a mobile robot carrying a surface where the fruits were placed. Both the perimeter and volume are measured and classified according to the type of fruit. The system was able to distinguish and classify the 95% of fruits and to estimate their volume with a 85% of accuracy in worst cases (fruits whose shape is not symmetrical) and 94% of accuracy in best cases (fruits whose shape is more symmetrical), showing that the proposed approach can become a useful tool in the agri-food industry.This project has been supported by the National Commission for Science and Technology Research of Chile (Conicyt) under FONDECYT grant 1140575 and the Advanced Center of Electrical and Electronic Engineering - AC3E (CONICYT/FB0008)

Vision-Based Cranberry Crop Ripening Assessment

Agricultural domains are being transformed by recent advances in AI and computer vision that support quantitative visual evaluation. Using drone imaging, we develop a framework for characterizing the ripening process of cranberry crops. Our method consists of drone-based time-series collection over a cranberry growing season, photometric calibration for albedo recovery from pixels, and berry segmentation with semi-supervised deep learning networks using point-click annotations. By extracting time-series berry albedo measurements, we evaluate four different varieties of cranberries and provide a quantification of their ripening rates. Such quantification has practical implications for 1) assessing real-time overheating risks for cranberry bogs; 2) large scale comparisons of progeny in crop breeding; 3) detecting disease by looking for ripening pattern outliers. This work is the first of its kind in quantitative evaluation of ripening using computer vision methods and has impact beyond cranberry crops including wine grapes, olives, blueberries, and maize

Sensors for product characterization and quality of specialty crops—A review

This review covers developments in non-invasive techniques for quality analysis and inspection of specialty crops, mainly fresh fruits and vegetables, over the past decade up to the year 2010. Presented and discussed in this review are advanced sensing technologies including computer vision, spectroscopy, X-rays, magnetic resonance, mechanical contact, chemical sensing, wireless sensor networks and radiofrequency identification sensors. The current status of different sensing systems is described in the context of commercial application. The review also discusses future research needs and potentials of these sensing technologies. Emphases are placed on those technologies that have been proven effective or have shown great potential for agro-food applications. Despite significant progress in the development of non-invasive techniques for quality assessment of fruits and vegetables, the pace for adoption of these technologies by the specialty crop industry has been slow