Luis Miguel Contreras-Medina

A Review of Methods for Sensing the Nitrogen Status in Plants: Advantages, Disadvantages and Recent Advances

Nitrogen (N) plays a key role in the plant life cycle. It is the main plant mineral nutrient needed for chlorophyll production and other plant cell components (proteins, nucleic acids, amino acids). Crop yield is affected by plant N status. Thus, the optimization of nitrogen fertilization has become the object of intense research due to its environmental and economic impact. This article focuses on reviewing current methods and techniques used to determine plant N status. Kjeldahl digestion and Dumas combustion have been used as reference methods for N determination in plants, but they are destructive and time consuming. By using spectroradiometers, reflectometers, imagery from satellite sensors and digital cameras, optical properties have been measured to estimate N in plants, such as crop canopy reflectance, leaf transmittance, chlorophyll and polyphenol fluorescence. High correlation has been found between optical parameters and plant N status, and those techniques are not destructive. However, some drawbacks include chlorophyll saturation, atmospheric and soil interference, and the high cost of instruments. Electrical properties of plant tissue have been used to estimate quality in fruits, and water content in plants, as well as nutrient deficiency, which suggests that they have potential for use in plant N determination.

Novel Methodology for Online Half-Broken-Bar Detection on Induction Motors

The relevance of the development of monitoring systems for rotating machines is not only the ability to detect failures but is also how early these failures can be detected. Squirrel-cage induction motors are the most popular motors used in industry, consuming around 85% of the power in industrial plants. Broken rotor bars in induction motors are among the major failures that are desirable to detect at early stages because this failure significantly increases power consumption and is responsible for further damage to the machinery. Previously reported works base their analysis on current or vibration monitoring for broken-bar detection up to one broken bar under mechanically loaded motor conditions. The contribution of this paper presents a novel methodology for half-broken-bar detection, which combines current and vibration analysis by correlating the signal spectra to enhance detectability for mechanically loaded and unloaded operating conditions of the motor, which the other isolated techniques are unable to detect. The proposed methodology is implemented in a low-cost field-programmable gate array (FPGA), giving a special-purpose system-on-a-chip (SoC) solution for online operation, with the development of a complex postprocessing decision-making unit. Several cases of study are presented to demonstrate the performance of the implementation.

Instrumentation in developing chlorophyll fluorescence biosensing: a review.

Chlorophyll fluorescence can be defined as the red and far-red light emitted by photosynthetic tissue when it is excited by a light source. This is an important phenomenon which permits investigators to obtain important information about the state of health of a photosynthetic sample. This article reviews the current state of the art knowledge regarding the design of new chlorophyll fluorescence sensing systems, providing appropriate information about processes, instrumentation and electronic devices. These types of systems and applications can be created to determine both comfort conditions and current problems within a given subject. The procedure to measure chlorophyll fluorescence is commonly split into two main parts; the first involves chlorophyll excitation, for which there are passive or active methods. The second part of the procedure is to closely measure the chlorophyll fluorescence response with specialized instrumentation systems. Such systems utilize several methods, each with different characteristics regarding to cost, resolution, ease of processing or portability. These methods for the most part include cameras, photodiodes and satellite images.

Smart Sensor for Real-Time Quantification of Common Symptoms Present in Unhealthy Plants

Plant responses to physiological function disorders are called symptoms and they are caused principally by pathogens and nutritional deficiencies. Plant symptoms are commonly used as indicators of the health and nutrition status of plants. Nowadays, the most popular method to quantify plant symptoms is based on visual estimations, consisting on evaluations that raters give based on their observation of plant symptoms; however, this method is inaccurate and imprecise because of its obvious subjectivity. Computational Vision has been employed in plant symptom quantification because of its accuracy and precision. Nevertheless, the systems developed so far lack in-situ, real-time and multi-symptom analysis. There exist methods to obtain information about the health and nutritional status of plants based on reflectance and chlorophyll fluorescence, but they use expensive equipment and are frequently destructive. Therefore, systems able of quantifying plant symptoms overcoming the aforementioned disadvantages that can serve as indicators of health and nutrition in plants are desirable. This paper reports an FPGA-based smart sensor able to perform non-destructive, real-time and in-situ analysis of leaf images to quantify multiple symptoms presented by diseased and malnourished plants; this system can serve as indicator of the health and nutrition in plants. The effectiveness of the proposed smart-sensor was successfully tested by analyzing diseased and malnourished plants.

FPGA-based Fused Smart Sensor for Real-Time Plant-Transpiration Dynamic Estimation

Plant transpiration is considered one of the most important physiological functions because it constitutes the plants evolving adaptation to exchange moisture with a dry atmosphere which can dehydrate or eventually kill the plant. Due to the importance of transpiration, accurate measurement methods are required; therefore, a smart sensor that fuses five primary sensors is proposed which can measure air temperature, leaf temperature, air relative humidity, plant out relative humidity and ambient light. A field programmable gate array based unit is used to perform signal processing algorithms as average decimation and infinite impulse response filters to the primary sensor readings in order to reduce the signal noise and improve its quality. Once the primary sensor readings are filtered, transpiration dynamics such as: transpiration, stomatal conductance, leaf-air-temperature-difference and vapor pressure deficit are calculated in real time by the smart sensor. This permits the user to observe different primary and calculated measurements at the same time and the relationship between these which is very useful in precision agriculture in the detection of abnormal conditions. Finally, transpiration related stress conditions can be detected in real time because of the use of online processing and embedded communications capabilities.

An analysis of electrical impedance measurements applied for plant N status estimation in lettuce (Lactuca sativa).

Nitrogen plays a key role in crop yields. Hence, farmers may apply excessive N fertilizers to crop fields, inducing environmental pollution. Crop N monitoring methods have been developed to improve N fertilizer management, most of them based on leaf or canopy optical-property measurements. However, sensitivity to environmental interference remains an important drawback. Electrical impedance has been applied to determine the physiological and nutritional status of plant tissue, but no studies related to plant-N contents are reported. The objective of this article is to analyze how the electrical impedance response of plants is affected by their N status. Four sets of lettuce (Lactuca sativa L.) with a different N-source concentrations per set were used. Total nitrogen and electrical impedance spectra (in a 1 to 100 kHz frequency range) were measured five times per set, three times every other day. Minimum phase angles of impedance spectra were detected and analyzed, together with the frequency value in which they occurred, and their magnitude at that frequency. High and positive correlation was observed between plant N content and frequency values at minimum phase angle with no significant variations detected between days of measurement. These results suggest that electrical impedance can be sensitive to plant N status.

FPGA based multiple-channel vibration analyzer embedded system for industrial applications in automatic failure detection

Machine monitoring is one of the major concerns in modern industry in order to guarantee the overall efficiency during the production process. Several monitoring techniques for machinery failure detection have been developed, being vibration analysis one of the most important techniques. The typical equipment used for vibration analysis is a general purpose single channel spectrum analyzer that most of the cases is not well suited for the specific task and lacks from the capability of simultaneous multiple channel analysis. The contribution of this work is to present the development of a low-cost FPGA based 3-axis simultaneous vibration analyzer for embedded machinery monitoring with the novelty of a post-processing stage that can be designed and implemented into the same FPGA for automatic online detection of specific machinery failures. Two cases of study are presented to show the development performance and capabilities of the system where specific post-processing units are designed. From the results it can be seen that several mechanical failures can be automatically detected by reconfiguring the postprocessing algorithm, embedded in the system.

FPGA Implementation of a Novel Algorithm for on-line Bar Breakage Detection on Induction Motors

Preventive maintenance is one of the major concerns in modern industry where failure detection on motors increases the useful life cycle on the machinery. Bar breakage is one of the most common failures on motors and monitoring of this condition is a mandatory task for industries. Previous works on bar breakage detection are based on off-line current or vibration analysis through the spectrum, but their detectability is compromised under certain operating conditions. The novelty of this work is the proposal of a correlation algorithm that combines current and vibration spectra to enhance detectability where other works fail. The contribution of this work is that the proposed algorithm, despite of complexity in computational load, is implemented into a low-cost FPGA giving a special purpose SOC solution for on-line operation, thanks to the development of a special purpose hardware signal processing unit. Several cases of study are presented to demonstrate the implementation performance.

Embedded FPGA based induction motor monitoring system with speed drive fed using multiple wavelet analysis

Preventive maintenance on machinery is one of the major concerns in industry. Induction motors represent 85% of the total power consumption in the world and several faults related to these motors that increase the power consumption are not easily detected; therefore, periodic fault monitoring is mandatory. There are several techniques for fault detection in induction motors being current analysis the most popular among others. This techniques are focused on the analysis of induction motors that are directly fed by the line and do not consider the induced effects due to speed drives. The novelty of this research is the proposal of a new technique, based on discrete wavelet transform for transient analysis at the motor start, for broken rotor bar detection on induction motors fed in both: directly and with speed drive. The contribution of the present work is the development of a field programmable gate array embedded system implementation of a broken rotor bar on-line monitoring system for a low cost embedded system approach. Results from experimentation show the overall system performance. It is demonstrated that the proposed methodology is efficient to detect the motor faults when directly or speed drive fed.

FPGA implementation of a novel algorithm for on-line bar breakage detection on induction motors

Preventive maintenance is one of the major concerns in modern industry where failure detection on motors increases the useful life cycle on the machinery. Bar breakage is one of the most common failures on motors and monitoring of this condition is a mandatory task for industries. Previous works on bar breakage detection are based on off-line current or vibration analysis through the spectrum, but their detectability is compromised under certain operating conditions. The novelty of this work is the proposal of a correlation algorithm that combines current and vibration spectra to enhance detectability where other works fail. The contribution of this work is that the proposed algorithm, despite of complexity in computational load, is implemented into a low-cost FPGA giving a special purpose SOC solution for on-line operation, thanks to the development of a special purpose hardware signal processing unit. Several cases of study are presented to demonstrate the implementation performance.