Red LED Light Improves Pepper (Capsicum annuum L.) Seed Radicle Emergence and Growth through the Modulation of Aquaporins, Hormone Homeostasis, and Metabolite Remobilization.

Red LED light (R LED) is an efficient tool to improve seed germination and plant growth under controlled environments since it is more readily absorbed by photoreceptors' phytochromes compared to other wavelengths of the spectrum. In this work, the effect of R LED on the radicle emergence and growth (Phase III of germination) of pepper seeds was evaluated. Thus, the impact of R LED on water transport through different intrinsic membrane proteins, via aquaporin (AQP) isoforms, was determined. In addition, the remobilization of distinct metabolites such as amino acids, sugars, organic acids, and hormones was analysed. R LED induced a higher germination speed index, regulated by an increased water uptake. PIP2;3 and PIP2;5 aquaporin isoforms were highly expressed and could contribute to a faster and more effective hydration of embryo tissues, leading to a reduction of the germination time. By contrast, TIP1;7, TIP1;8, TIP3;1 and TIP3;2 gene expressions were reduced in R LED-treated seeds, pointing to a lower need for protein remobilization. NIP4;5 and XIP1;1 were also involved in radicle growth but their role needs to be elucidated. In addition, R LED induced changes in amino acids and organic acids as well as sugars. Therefore, an advanced metabolome oriented to a higher energetic metabolism was observed, conditioning better seed germination performance together with a rapid water flux.

Defect Detection in Textures through the Use of Entropy as a Means for Automatically Selecting the Wavelet Decomposition Level.

This paper presents a robust method for defect detection in textures, entropy-based automatic selection of the wavelet decomposition level (EADL), based on a wavelet reconstruction scheme, for detecting defects in a wide variety of structural and statistical textures. Two main features are presented. One of the new features is an original use of the normalized absolute function value (NABS) calculated from the wavelet coefficients derived at various different decomposition levels in order to identify textures where the defect can be isolated by eliminating the texture pattern in the first decomposition level. The second is the use of Shannon's entropy, calculated over detail subimages, for automatic selection of the band for image reconstruction, which, unlike other techniques, such as those based on the co-occurrence matrix or on energy calculation, provides a lower decomposition level, thus avoiding excessive degradation of the image, allowing a more accurate defect segmentation. A metric analysis of the results of the proposed method with nine different thresholding algorithms determined that selecting the appropriate thresholding method is important to achieve optimum performance in defect detection. As a consequence, several different thresholding algorithms depending on the type of texture are proposed.

A sensor system for detection of hull surface defects.

This paper presents a sensor system for detecting defects in ship hull surfaces. The sensor was developed to enable a robotic system to perform grit blasting operations on ship hulls. To achieve this, the proposed sensor system captures images with the help of a camera and processes them in real time using a new defect detection method based on thresholding techniques. What makes this method different is its efficiency in the automatic detection of defects from images recorded in variable lighting conditions. The sensor system was tested under real conditions at a Spanish shipyard, with excellent results.

Development of a sensor node for precision horticulture.

This paper presents the design of a new wireless sensor node (GAIA Soil-Mote) for precision horticulture applications which permits the use of precision agricultural instruments based on the SDI-12 standard. Wireless communication is achieved with a transceiver compliant with the IEEE 802.15.4 standard. The GAIA Soil-Mote software implementation is based on TinyOS. A two-phase methodology was devised to validate the design of this sensor node. The first phase consisted of laboratory validation of the proposed hardware and software solution, including a study on power consumption and autonomy. The second phase consisted of implementing a monitoring application in a real broccoli (Brassica oleracea L. var Marathon) crop in Campo de Cartagena in south-east Spain. In this way the sensor node was validated in real operating conditions. This type of application was chosen because there is a large potential market for it in the farming sector, especially for the development of precision agriculture applications.