Last generation instrument for agriculture multispectral data collection

In recent years, the acquisition and analysis of multispectral data are gaining a growing interest and importance in agriculture. On the other hand, new technologies are opening up for the possibility of developing and implementing sensors with relatively small size and featuring high technical performances. Thanks to low weights and high signal to noise ratios, such sensors can be transported by different type of means (terrestrial as well as aerial vehicles), giving new opportunities for assessment and monitoring of several crops at different growing stages or health conditions. The choice and specialization of individual bands within the electromagnetic spectrum ranging from the ultraviolet to the infrared, plays a fundamental role in the definition of the so-called vegetation indices (eg. NDVI, GNDVI, SAVI, and dozens of others), posing new questions and challenges in their effective implementation. The present paper firstly discusses the needs of low-distance based sensors for indices calculation, then focuses on development of a new multispectral instrument specially developed for agricultural multispectral analysis. Such instrument features high frequency and high resolution imaging through nine different sensors (1 RGB and 8 monochromes with relative band-pass filters, covering the 390 to 950 nm range). The instrument allows synchronized multiband imaging thanks to integrated global shutter technology, with a frame rate up to 5 Hz; exposure time can be as low as 1/5000 s. An applicative case study is eventually reported on an area featuring different materials (organic and non-organic), to show the new instrument potential. Last generation instrument for agriculture multispectral data collection. Available from: https://www.researchgate.net/publication/317596952_Last_generation_instrument_for_agriculture_multispectral_data_collection [accessed Jul 11, 2017]

Optimum Illuminant Determination Based on Reduced and Optimized Multispectral Spectroscopy to Enhance Vein Detection

Venepuncture as a mode of gaining intravenous access has been a prime practice in surgical procedures and other conventional drug administering into a patient. Biomedical engineering has stressed relatively high scale of importance in the spectroscopic analysis of vein imaging as a sparky approach to promote a non-invasive catheterization. However, medical personnel are challenged by the physiological circumstances of skin tone, presence of scars and irregularity of the epidermal topology, when performing subcutaneous vein localization, which led them to increase number of insertion attempts. Hence, this paper proposes an optimized solution to provide enhanced visual aids for personnel to achieve successful vein catheterization at first attempt

Multispectral photography for earth resources

A guide for producing accurate multispectral results for earth resource applications is presented along with theoretical and analytical concepts of color and multispectral photography. Topics discussed include: capabilities and limitations of color and color infrared films; image color measurements; methods of relating ground phenomena to film density and color measurement; sensitometry; considerations in the selection of multispectral cameras and components; and mission planning

Plant Localization and Discrimination using 2D+3D Computer Vision for Robotic Intra-row Weed Control

Weed management is vitally important in crop production systems. However, conventional herbicide based weed control can lead to negative environmental impacts. Manual weed control is laborious and impractical for large scale production. Robotic weed control offers a possibility of controlling weeds precisely, particularly for weeds growing near or within crop rows. A computer vision system was developed based on Kinect V2 sensor, using the fusion of two-dimensional textural data and three-dimensional spatial data to recognize and localized crop plants different growth stages. Images were acquired of different plant species such as broccoli, lettuce and corn at different growth stages. A database system was developed to organize these images. Several feature extraction algorithms were developed which addressed the problems of canopy occlusion and damaged leaves. With our proposed algorithms, different features were extracted and used to train plant and background classifiers. Finally, the efficiency and accuracy of the proposed classification methods were demonstrated and validated by experiments

Multiplexed Illumination for Classifying Visually Similar Objects

Distinguishing visually similar objects like forged/authentic bills and healthy/unhealthy plants is beyond the capabilities of even the most sophisticated classifiers. We propose the use of multiplexed illumination to extend the range of objects that can be successfully classified. We construct a compact RGB-IR light stage that images samples under different combinations of illuminant position and colour. We then develop a methodology for selecting illumination patterns and training a classifier using the resulting imagery. We use the light stage to model and synthetically relight training samples, and propose a greedy pattern selection scheme that exploits this ability to train in simulation. We then apply the trained patterns to carry out fast classification of new objects. We demonstrate the approach on visually similar artificial and real fruit samples, showing a marked improvement compared with fixed-illuminant approaches as well as a more conventional code selection scheme. This work allows fast classification of previously indistinguishable objects, with potential applications in forgery detection, quality control in agriculture and manufacturing, and skin lesion classification.Comment: Submitted to Computer Vision and Image Understanding (CVIU