International Journal of Computer and Applications

U Hrvatskoj nikada nije bilo oplemenjivackih programa za povrce, vec su sorte kreirane entuzijazmom autora kao "sporedni" proizvod znanstveno-istraživackog rada na povrcu. Na Sortnoj listi poljoprivrednog bilja RH, zakljucno s 2010. godinom, ima 17 sorti povrca koje su kreirane i upisane u listu od 1964. godine.

The vegetable crops

Deep learning based image recognition for crack and leakage defects of metro shield tunnel

The utilization of machine vision and its associated algorithms improves the efficiency, functionality, intelligence, and remote interactivity of harvesting robots in complex agricultural environments. Machine vision and its associated emerging technology promise huge potential in advanced agricultural applications. However, machine vision and its precise positioning still have many technical difficulties, making it difficult for most harvesting robots to achieve true commercial applications. This article reports the application and research progress of harvesting robots and vision technology in fruit picking. The potential applications of vision and quantitative methods of localization, target recognition, 3D reconstruction, and fault tolerance of complex agricultural environment are focused, and fault-tolerant technology designed for utilization with machine vision and robotic systems are also explored. The two main methods used in fruit recognition and localization are reviewed, including digital image processing technology and deep learning-based algorithms. The future challenges brought about by recognition and localization success rates are identified: target recognition in the presence of illumination changes and occlusion environments; target tracking in dynamic interference-laden environments, 3D target reconstruction, and fault tolerance of the vision system for agricultural robots. In the end, several open research problems specific to recognition and localization applications for fruit harvesting robots are mentioned, and the latest development and future development trends of machine vision are described.

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Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review.

Random forest based intelligent fault diagnosis for PV arrays using array voltage and string currents

Scheme for predictive fault diagnosis in photo-voltaic modules using thermal imaging

Design of early fault detection technique for electrical assets using infrared thermograms

Railway track monitoring is a challenging task to avoid railway accidents due to track failures. There are lot many issues involved in the railway accidents but the major involvement is the use of defective railway tracks. The aim of this paper is to present a novel visual inspection technique for detection, marking, and visualization of defected portion in railway tracks. In this paper, maximally stable extremal region technique is used to identify and visualize the geometrical features of the defected regions on the rail head surface in railway track images. Here, three classes of surface defects have been taken. The results are very promising and comprise all the aspects.

Maximally Stable Extremal Region Marking-Based Railway Track Surface Defect Sensing

This paper elaborated the fruit quality detection technique which was based on external properties of fruits such as shape, size and color. Due to large demand, manual monitoring of fruits are ineffectual for agriculture industry. So, it requires a competent technique which will help agriculture industry to full fill the demand of consumer. The proposed method is based on the use of speeded up robust feature. The method extracts the local feature of the segmented image and describes the object recognition. The objective is to design the defect detection algorithm which will be used for feature extraction and descriptor having less processing time.

Fruit defect detection based on speeded up robust feature technique

Presently, the fruit industry requires a fast and efficient method for classification and recognition of the quality of fruits in bulk processing. Fruit recognition based on computer vision is quite challenging as it is based on the intensity, size, contour, and texture features extraction from fruits along with their suitable classifier selection. In this paper, the pixels containing the defected regions are segmented and their features are extracted. Further, a support vector machine (SVM) classifier is used to identify the defects and recognizes the cause with its stage. During the process of classification, fruits are categorized into two groups, defected and no-defect. The sample image observed defected further classified into three categories as the first, second and final stage of fruit defect. The sample testing at an early stage helps one to further proceed with the production or halt based on the outcome of a computer vision-based recognition system.

Ashwani Kumar Dubey

Papers

Scheme for predictive fault diagnosis in photo-voltaic modules using thermal imaging

Design of early fault detection technique for electrical assets using infrared thermograms

Maximally Stable Extremal Region Marking-Based Railway Track Surface Defect Sensing

Fruit defect detection based on speeded up robust feature technique

Computer vision based analysis and detection of defects in fruits causes due to nutrients deficiency