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.

International audienceConvolution is an important operation in image processing applications, such as edge detection, sharpening , adding blurring and so on. Convolving video streams in real-time is a challenging task for PC systems, however, FPGA devices can successfully be used in these tasks. In this article, the design and implementation of a reconfigurable FPGA architecture for 2D-convolution filtering is described. The filtered frames are calculated at a rate of 103 frames per second for images up to 1200×720 pixel resolution. By using a shift-based arithmetic and circular buffers, the developed FPGA architecture allows to reduce the hardware resources consumption up to 98% compared to the conventional convolution implementations , provides high speed processing and enables to manage large number of different convolution kernels. On the other hand, by using the CAPH language it is possible to reduce the design time up to 75% compared to the plain VHDL design. Furthermore, to maintain high flexibility in concordance with the input video, the developed hardware allows to configure the resolution of the input images with values of 3 × Y up to 1200 × Y , and allows scalability for different sizes of convolution kernels of simple and systematic form. Finally , the developed FPGA architecture for the proposed method was implemented and validated in an FPGA Cyclone II EP2C35F672C6 embedded in an Altera development board DE2

This paper presents the development and implementation of neural control systems in mobile robots in obstacle avoidance in real time using ultrasonic sensors with complex strategies of decision-making in development (Matlab and Processing). An Arduino embedded platform is used to implement the neural control for field results.

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