Growth, yield and nutrient uptake of guava (Psidium Guavaja L.) affected by soil matric potential, fertigation and mulching under drip irrigation

Our objective was to examine the effect of plastic mulching, three soil matric potentials (SMP) treatments    {I1(-20 kPa), I2(-40 kPa), and I3(-60 kPa)} and three fertigation levels {F1(100%), F2(80%), and F3(60%) recommended dose of fertilizer} under drip irrigation conditions for nutrient uptake, growth parameters and yield in guava plants.  The experiments were set up in factorial randomized block design with eighteen treatment combinations.  The experiments were conducted during the year 2012-13.  The investigation indicated that the plant canopy spread in (N/S and E/W) directions was greatly affected by different treatments.  However, non-significant effects of interaction parameters were found on plant height, crop volume and plant girth.  The maximum yield was obtained in MI2F2 (68.66 kg per plant and 22.86 t ha-1) followed by NMI2F2 (66.50 kg per plant and 22.14 t ha-1) treatments.  The maximum percentage of high quality (fruit levels A and B) were 48.2% and 50.1% in -40 kPa  irrigation treatment for mulch and no mulch conditions under 100% application of recommended dose of fertilizers.  The varying range of leaf nutrients observed for different treatments of irrigation, fertigation and mulch is  1.26-1.74% N, 0.14-0.26% P, 0.44-0.88% K, 36.33-74.23 ppm Zn, 11.33-32.76 ppm Cu, 415.6- 557.3 ppm Fe, 26.80- 39.06 ppm Mn, 0.533-0.762 % Mg and 3.42-5.06% Ca.  Based on the results above, it is recommended that controlling SMP between -40 kPa to -45 kPa at 0.2 m depth immediately under the drip emitter and fertilizer dose of 80% recommended dose of fertilizer can be used as an indicator for drip irrigation scheduling in semi-arid region of northwest India.   Keywords: fertilizer application, irrigation strategies, pressure head, tensiometer, leaf uptak

Implementation of machine learning on an innovative processor for IoT

The “Internet of Things” (IoT) is a very rapidly increasing market segment for electronics, and it holds the promise to be one of the most significant drivers for innovation in the semiconductor industry in the near future. “IoT” is providing new and different specifications to the design of embedded systems, and such specifications are likely to change the constraints that drive embedded systems design. In particular, “IoT” is introducing a wave of innovation on the design of embedded microprocessors that are the heart and soul of such systems. This report took place in the context of larger investigation on innovative embedded processor architectures for “IoT”. The work started from an existing processor design, developed in Simon Fraser University in form of a Hardware Description Language (HDL) open source library. Such processor design advantages on the RISC-V instruction set distributed since 2011 by the University of California at Berkley. This work focused on analyzing a reference algorithmic application of relevance for the “IoT” (Linear Discriminant Analysis, a well-known Machine Learning tool for data classification), that is currently being utilized in two different research projects in Simon Fraser University. This report contributed to the larger project by: 1) Porting a C version of the LDA algorithm developed for ARM cores on the newly proposed processor architecture. 2) Evaluating the performance of the LDA algorithm on the proposed architecture in terms of available data sample rates and required energy consumption. 3) Profiling the LDA algorithm on the proposed processor in order to determine the critical operation kernels that mostly affect the performance. 4) Defining the hardware configuration for the proposed processor that leads to the most efficient implementation of LDA