Biocomposites with polylactic acid (PLA), nanosilica parts, and water hyacinth fibres have been developed in this experimental study. By changing the weight percentage of nanosilica particulate matter (0, 2, 4, 6, and 8 percent) with PLA and water hyacinth fibres, five composite mates were produced through a double screw extruder and compression moulding machine. According to the ASTM standards, the process to machine, the composite specimens have been adopted from the water jet machining process. The tensile, compression, flexural, impact, hardness, and water absorption tests were performed on the composite specimens to assess various mechanical properties and absorbance behaviour. The test findings reveal the significant improvement in the tensile and flexural properties of the composites. Composites contain 6 percent of the fine nanosilica particles by weight. Concerning adding the growing weight percentage (4 percent) of nanosilica particles to the composites, the water absorption properties of the composites have significantly improved. The tensile strength of 6% nanosilica mixed specimens showed the highest tensile stress rate as 36.93 MPa; the value was nearly 3.5% higher than the 4% nanosilica mixed composite specimens.

Internet of Things (IoT) is the most considerable medium for all smart applications, in which it provides a huge support to agricultural industry in fine manner. In literature, there are lots of smart devices are available for monitoring the crops and agricultural field, but all are strucked under certain limitations such as power problem, cost expensiveness and so on. This paper is intended to design a new machine learning enabled Smart Internet of Things medium to support agricultural field in proper way. In this paper, a Intelligent Crop Monitoring Device (ICMD) is introduced to monitor the crops over the agricultural field in 24x7 manner. This kind of monitoring devices enhances the production and quality-of-service of the agriculture as well as related products. This paper associates an innovative technology to the Smart Device called Machine Learning, but instead of using the classical learning schemes, this approach introduced a new scheme called Modified Learning based Field Analysis Strategy (MLFAS). This approach is inspired from the classical machine learning scheme called Convolutional Neural Network (CNN), in which the proposed Smart Device called ICMD accumulates the real-time agricultural field details and pass it to the monitoring unit for manipulation. The manipulation end maintains the data into the server unit, in which the machine learning model called MLFAS acquires the received field data and process it based on the training samples. The training samples are nothing but data collected from the agriculture field, the collection of received data are maintained into the server end for processing, the proposed MLFAS model manipulates the data and created as a model for further testing. The newly arrived data from the field is considered as a testing data and cross-validate that data into the trained model. The data acquired from the agriculture fields are temperature, humidity and soil moisture level, in which these records are passed to the server unit by using IoT module associated with the ICMD. The data available into the server can easily be monitored by the farmer from anywhere at any time. The learning model predicts the status of the crop in the field by means of analyzing the input acquired from the real-time testing input and report that to the respective farmer for taking an appropriate action. For all this system is useful to the agricultural field and provides good support to farmers to monitor the crops over the agricultural field from the remote place even. By using this proposed scheme, the farmers can make accurate and efficient crop management decisions with the use of results obtained by using the Smart Device called ICMD.

In this paper, dimensional coding of projection has been developed with the hardware design for RS algorithms. The result of simulation of Memory Interface Unit for basic and dimensional projection coding with scale and Implemented Video Interface module using VHDL and Target on to the FPGA implemented (XCV300-bg432-6). It provided operating speed of 732.5 MHz, power consumption of 1249 BEL and 128.3 mW in FPGA.

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