OCM 2021 - Optical Characterization of Materials : Conference Proceedings

The state of the art in the optical characterization of materials is advancing rapidly. New insights have been gained into the theoretical foundations of this research and exciting developments have been made in practice, driven by new applications and innovative sensor technologies that are constantly evolving. The great success of past conferences proves the necessity of a platform for presentation, discussion and evaluation of the latest research results in this interdisciplinary field

OCM 2021 - Optical Characterization of Materials

The state of the art in the optical characterization of materials is advancing rapidly. New insights have been gained into the theoretical foundations of this research and exciting developments have been made in practice, driven by new applications and innovative sensor technologies that are constantly evolving. The great success of past conferences proves the necessity of a platform for presentation, discussion and evaluation of the latest research results in this interdisciplinary field

Segmentation of field grape bunches via an improved pyramid scene parsing network

With the continuous expansion of wine grape planting areas, the mechanization and intelligence of grape harvesting have gradually become the future development trend. In order to guide the picking robot to pick grapes more efficiently in the vineyard, this study proposed a grape bunches segmentation method based on Pyramid Scene Parsing Network (PSPNet) deep semantic segmentation network for different varieties of grapes in the natural field environments. To this end, the Convolutional Block Attention Module (CBAM) attention mechanism and the atrous convolution were first embedded in the backbone feature extraction network of the PSPNet model to improve the feature extraction capability. Meanwhile, the proposed model also improved the PSPNet semantic segmentation model by fusing multiple feature layers (with more contextual information) extracted by the backbone network. The improved PSPNet was compared against the original PSPNet on a newly collected grape image dataset, and it was shown that the improved PSPNet model had an Intersection-over-Union (IoU) and Pixel Accuracy (PA) of 87.42% and 95.73%, respectively, implying an improvement of 4.36% and 9.95% over the original PSPNet model. The improved PSPNet was also compared against the state-of-the-art DeepLab-V3+ and U-Net in terms of IoU, PA, computation efficiency and robustness, and showed promising performance. It is concluded that the improved PSPNet can quickly and accurately segment grape bunches of different varieties in the natural field environments, which provides a certain technical basis for intelligent harvesting by grape picking robots

Implementasi Keamanan Rumah Cerdas Menggunakan Internet of Things dan Biometric Sistem

Pintu adalah salah satu fitur pertahanan pertama untuk menjaga keamanan fisik rumah. Dalam sebuah rumah, pintu memiliki peranan penting dalam masalah keamanan. Terkadang kelalaian penghuni rumah dalam menjaga keamanan rumah membuat keamanan pintu rumah menjadi tidak terkontrol seperti, lupa mengunci pintu saat keluar rumah, kehilangan kunci rumah, dan mungkin lupa apakah sudah mengunci pintu atau belum. Kelemahan keamanan lainnya adalah mudahnya pencuri membobol pintu rumah yang masih menggunakan kunci manual. Dari masalah-masalah tersebut dalam penelitian ini mengusulkan sebuat sistem keamanan pintu rumah menggunakan sistem kontrol biometric dan sistem kontrol manual berbasis Internet of Things untuk mengendalikan pintu dan meningkatkan keamanan rumah. Dalam penelitian ini menerapkan dua mekanisme pengontrolan pintu rumah yaitu dengan memanfaatkan sensor sidik jari sebagai sistem kontrol biometric, dan sistem kontrol manual berbasis Internet of Things untuk langkah alternatif, apabila terdapat masalah dengan kondisi fisik jari penghuni rumah. Metode implementasi perancangan sistem menggunakan mikrokontroler NodeMCU ESP8266 dengan bahasa pemrograman Arduino dan aplikasi mobile blynk berbasis android. Hasil evaluasi menunjukan bahwa adanya integrasi yang baik antara kontrol menggunakan sensor sidik jari dan kontrol manual berbasis Internet of Things dimana kedua kontrol melakukan respon membuka dan mengunci pintu rumah selama 5 detik sehingga tidak ada celah kelalaian yang dapat berdampak buruk bagi keamanan rumah

Automatic plant features recognition using stereo vision for crop monitoring

Machine vision and robotic technologies have potential to accurately monitor plant parameters which reflect plant stress and water requirements, for use in farm management decisions. However, autonomous identification of individual plant leaves on a growing plant under natural conditions is a challenging task for vision-guided agricultural robots, due to the complexity of data relating to various stage of growth and ambient environmental conditions. There are numerous machine vision studies that are concerned with describing the shape of leaves that are individually-presented to a camera. The purpose of these studies is to identify plant species, or for the autonomous detection of multiple leaves from small seedlings under greenhouse conditions. Machine vision-based detection of individual leaves and challenges presented by overlapping leaves on a developed plant canopy using depth perception properties under natural outdoor conditions is yet to be reported. Stereo vision has recently emerged for use in a variety of agricultural applications and is expected to provide an accurate method for plant segmentation and identification which can benefit from depth properties and robustness. This thesis presents a plant leaf extraction algorithm using a stereo vision sensor. This algorithm is used on multiple leaf segmentation and overlapping leaves separation using a combination of image features, specifically colour, shape and depth. The separation between the connected and the overlapping leaves relies on the measurement of the discontinuity in depth gradient for the disparity maps. Two techniques have been developed to implement this task based on global and local measurement. A geometrical plane from each segmented leaf can be extracted and used to parameterise a 3D model of the plant image and to measure the inclination angle of each individual leaf. The stem and branch segmentation and counting method was developed based on the vesselness measure and Hough transform technique. Furthermore, a method for reconstructing the segmented parts of hibiscus plants is presented and a 2.5D model is generated for the plant. Experimental tests were conducted with two different selected plants: cotton of different sizes, and hibiscus, in an outdoor environment under varying light conditions. The proposed algorithm was evaluated using 272 cotton and hibiscus plant images. The results show an observed enhancement in leaf detection when utilising depth features, where many leaves in various positions and shapes (single, touching and overlapping) were detected successfully. Depth properties were more effective in separating between occluded and overlapping leaves with a high separation rate of 84% and these can be detected automatically without adding any artificial tags on the leaf boundaries. The results exhibit an acceptable segmentation rate of 78% for individual plant leaves thereby differentiating the leaves from their complex backgrounds and from each other. The results present almost identical performance for both species under various lighting and environmental conditions. For the stem and branch detection algorithm, experimental tests were conducted on 64 colour images of both species under different environmental conditions. The results show higher stem and branch segmentation rates for hibiscus indoor images (82%) compared to hibiscus outdoor images (49.5%) and cotton images (21%). The segmentation and counting of plant features could provide accurate estimation about plant growth parameters which can be beneficial for many agricultural tasks and applications

Yield components detection and image-based indicators for non-invasive grapevine yield prediction at different phenological phases

Forecasting vineyard yield with accuracy is one of the most important trends of research in viticulture today. Conventional methods for yield forecasting are manual, require a lot of labour and resources and are often destructive. Recently, image-analysis approaches have been explored to address this issue. Many of these approaches encompass cameras deployed on ground platforms that collect images in proximal range, on-the-go. As the platform moves, yield components and other image-based indicators are detected and counted to perform yield estimations. However, in most situations, when image acquisition is done in non-disturbed canopies, a high fraction of yield components is occluded. The present work’s goal is twofold. Firstly, to evaluate yield components’ visibility in natural conditions throughout the grapevine’s phenological stages. Secondly, to explore single bunch images taken in lab conditions to obtain the best visible bunch attributes to use as yield indicators. In three vineyard plots of red (Syrah) and white varieties (Arinto and Encruzado), several canopy 1 m segments were imaged using the robotic platform Vinbot. Images were collected from winter bud stage until harvest and yield components were counted in the images as well as in the field. At pea-sized berries, veraison and full maturation stages, a bunch sample was collected and brought to lab conditions for detailed assessments at a bunch scale. At early stages, all varieties showed good visibility of spurs and shoots, however, the number of shoots was only highly and significantly correlated with the yield for the variety Syrah. Inflorescence and bunch occlusion reached high percentages, above 50 %. In lab conditions, among the several bunch attributes studied, bunch volume and bunch projected area showed the highest correlation coefficients with yield. In field conditions, using non-defoliated vines, the bunch projected area of visible bunches presented high and significant correlation coefficients with yield, regardless of the fruit’s occlusion. Our results show that counting yield components with image analysis in non-defoliated vines may be insufficient for accurate yield estimation. On the other hand, using bunch projected area as a predictor can be the best option to achieve that goal, even with high levels of occlusioninfo:eu-repo/semantics/publishedVersio

Hyperspectral Imaging from Ground Based Mobile Platforms and Applications in Precision Agriculture

This thesis focuses on the use of line scanning hyperspectral sensors on mobile ground based platforms and applying them to agricultural applications. First this work deals with the geometric and radiometric calibration and correction of acquired hyperspectral data. When operating at low altitudes, changing lighting conditions are common and inevitable, complicating the retrieval of a surface's reflectance, which is solely a function of its physical structure and chemical composition. Therefore, this thesis contributes the evaluation of an approach to compensate for changes in illumination and obtain reflectance that is less labour intensive than traditional empirical methods. Convenient field protocols are produced that only require a representative set of illumination and reflectance spectral samples. In addition, a method for determining a line scanning camera's rigid 6 degree of freedom (DOF) offset and uncertainty with respect to a navigation system is developed, enabling accurate georegistration and sensor fusion. The thesis then applies the data captured from the platform to two different agricultural applications. The first is a self-supervised weed detection framework that allows training of a per-pixel classifier using hyperspectral data without manual labelling. The experiments support the effectiveness of the framework, rivalling classifiers trained on hand labelled training data. Then the thesis demonstrates the mapping of mango maturity using hyperspectral data on an orchard wide scale using efficient image scanning techniques, which is a world first result. A novel classification, regression and mapping pipeline is proposed to generate per tree mango maturity averages. The results confirm that maturity prediction in mango orchards is possible in natural daylight using a hyperspectral camera, despite complex micro-illumination-climates under the canopy