Computer Systems

En esta investigación se realizó un experimento factorial completo con los 3 macro nutrimentos principales como factores y 3 niveles de contenido por cada uno, cuando las plantas alcanzaron la madurez se cortaron hojas para capturar las imágenes digitales. La cámara se colocó perpendicularmente a la hoja de lechuga y con los datos técnicos del sensor CCD y la distancia conocida entre este y la hoja, se calculó el área que representa cada píxel de la imagen y así conocer el área que ocupa la hoja. Cuando conocimos el área ocupada por cada hoja de lechuga, se relacionó con el tipo y cantidad de nutrimentos que contenía y estadísticamente se encontró que diferentes combinaciones afectan el tamaño, pero el elemento común en ellas es el fósforo seguido del potasio, el nitrógeno no afecta tanto en el tamaño si no al color. Entonces el método es válido para predecir carencia de fósforo y potasio.

In this paper, a novel method that uses both area and feature based  information as similarity measures for stereo matching is proposed. Area-based information is suited for non-homogeneous regions while feature information helps in homogeneous areas. In order to define a conjugate pair, a fuzzy logic approach that combines the similarity information is used. The proposed method preserves discontinuities while reducing matching errors in homogeneous regions. This proposal is suited for real-time processing using dedicated hardware. We demonstrate and discuss performance using synthetic stereo pairs.

This article describes the design and efficient implementation of a Takagi Sugeno multivariable Fuzzy Logic Controller. The application selected is a temperature and humidity controller  for a chicken incubator. This design was elaborated using VHDL applying intermediate simulations in order to check for functional verification of all modules integrating the controller. The created circuit was implemented on FPGA Cyclone II EP2C35F672C6 assembled in breadboard Altera DE2. Precision of the model on FPGA was calculated by contrasting the obtained results with the results of simulation using MATLAB & Fuzzy Logic Tool Box.

Nutrimental state of vegetables foods, is a more studied parameter to defined quality them. A way to predict the deficit of phosphorus and potassium in hydroponic lettuce crops is presented. The method is based on the leaf area sizes at different stages of growth, measured with digital images. For acquisition, the camera was placed perpendicularly to the lettuce leaf, considering technical data of sensor and the known distance between this and the leaf, the area represented by each pixel of the image and the area occupied by the leaf is computed. Nutriment types was related with leaf area size, ANOVA table determined that phosphorus and potassium are the nutrients statistically related to plant growth.

This work introduces a new vision-based approach for estimating chlorophyll contents in a plant leaf using reflectance and transmittance as base parameters. Images of the top and underside of the leaf are captured. To estimate the base parameters (reflectance/transmittance), a novel optical arrangement is proposed. The chlorophyll content is then estimated by using linear regression where the inputs are the reflectance and transmittance of the leaf. Performance of the proposed method for chlorophyll content estimation was compared with a spectrophotometer and a Soil Plant Analysis Development (SPAD) meter. Chlorophyll content estimation was realized for Lactuca sativa L., Azadirachta indica, Canavalia ensiforme, and Lycopersicon esculentum. Experimental results showed that—in terms of accuracy and processing speed—the proposed algorithm outperformed many of the previous vision-based approach methods that have used SPAD as a reference device. On the other hand, the accuracy reached is 91% for crops such as Azadirachta indica, where the chlorophyll value was obtained using the spectrophotometer. Additionally, it was possible to achieve an estimation of the chlorophyll content in the leaf every 200 ms with a low-cost camera and a simple optical arrangement. This non-destructive method increased accuracy in the chlorophyll content estimation by using an optical arrangement that yielded both the reflectance and transmittance information, while the required hardware is cheap.