An embedded system integrated with sensors based on nanomaterial is proposed for closely monitoring and control microclimate parameters 24 hours a day to maximise production over the whole crop growth season by introducing greenhouse for the cultivation of plants or specific plant species. The system will also eliminate errors in human intervention to optimise production of crops. This system consists of sensors and actuators, an Analogue to Digital Converter (ADC) and a Raspberry Pi. The system will determine whether a defined threshold is passed by any climate parameter and systematically changes via the controller. The current work reduces human input through automated irrigation to optimally utilize a scarce resource, namely water. Climatic parameters for plant growth such as, moisture, humidity, temperature, water pressure in drip pipe, soil salinity etc. are monitored and optimized. Furthermore, work was extended to include GSM to control the entire farm remotely. For its success, it is very important to choose a greenhouse location. For instance, the problems are quite different when choosing an adjoining greenhouse, for instance a sunroom or greenhouse. The greenhouse location should be chosen for sunlight, proximity to power and water sources, wind, drain and freeze pockets, and the proximity of the garden and house. The intention behind accomplishment and devise of GSM based Fertigation System is to construct and evaluate the requirement of water in the yield as farming is the major resource of production which habitually depends on the water accessibility. Irrigation of water is usually done by manual method. To ease the work of the farmer GSM based automatic Fertigation (includes chemigation too) system can be implemented so that water wastage can be reduced and also the fertilizer can be added accordingly. Also the Soil Salinity can be checked and reduced if exceeds certain limit. By using GSM, only GSM command via GSM mobile can control the start and stop action of a motor that feeds the field with the water. GSM is used for controlling the entire process and the entire system backbone. It can be used from any distance to control irrigation. The results are assessed by electronic simulator PROTEUS using the desired optimised parameters, the design of this automated greenhouse system with PIC controller. As the inputs to the microcontroller and as an LCD screen record the respective outputs, the model produces a soil moisture sensor, light sensor and temperature sensor. The system performance is accurate and repeatable for measuring and controlling the four parameters that are crucial for plant growth - temperature, humidity, soil moisture and light intensity. With the reduction in electricity consumption, maintenance and complexity, and a flexible and precise environment control form for agriculture, the new system successfully cured quite a couple of defects in existing systems. Nano composite film sensors (Graphene and Graphene mixed in order to optimise the input of fertilisers for chemical composition determination. Using nano technology in agriculture enforces the firm bond between the engineer and farmer. Nano material film-based gas sensors were used to measure the presence of oxygen and CO2.using graphene nano composite sensors integrated into an embedded system, to detect the presence and levels of gases. Improve crop growth with combined red and blue light for lighting under the leavened and solar-powered LED lighting modules. This was achieved by graph/solar cells. The light was measured at the photosynthesis flux (PPFD) of 165 μmol m-2 s-1 by 10 cm of its LED module. LED lights were provided between 4:00 a.m. and 4:00 p.m. in the daytime treatments and night treatments from 10 to 10 hours. The use of the nighttime interlumination of LEDs was also economical than the interlumination of charts. Thus, nightlighting LEDs can effectively improve plant growth and output with less energy than the summer and winter times. Solar panels are best functioning during times of strong sunlight today, but begin to wan when they become too hot and cloudy. By allowing Solar Panels to produce electricity during harsh weather conditions and increase efficiency, a breakthrough in graphene-based solar panels can change everything. Ultimately with a fully autonomous system, agricultural productivity and efficiency, the length of the growing season, energy consumption and water consumption were recorded and monitored by exporting the data over GSM environment. With the steady decrease in the cost of high-performing hardware and software, the increased acceptance of self-employed farming systems, and the emerging agricultural system industry, the results will be reliable control systems covering various aspects of quality and production quantity.

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Summary Non‐rigid moving multiple objects detection and tracking play an important role in intelligent video surveillance system, autonomous navigation, and activity analysis. Closed Circuit Television (CCTV) systems are deployed in numerous areas such as airports, traffic intersections, underground stations, mass events, mall, schools, and organisations for security and public surveillance. Although these cameras record continuous video 24x7, it is a human constraint to manually monitor all events such as crime, terrorism, hideous, suspicious activities, the positioning of the vehicle, and fire recorded from a number of cameras. Moreover, problems like dynamic background, the creation of ghost, sensor noise, varying illumination, and colour and compression artefacts affect effective detection of multiple moving objects. This study presents an effective approach named as enhanced Fractal Texture Analysis with KNN classifier (FTAKC) for tracking and detection of multiple objects from a video sequence. The proposed approach comprises three main phases, namely, detection of moving object, tracking of the object (enhanced Fractal Texture Analysis), and behaviour analysis for activity recognition (KNN classifier). The image feature has been extracted based on colour, texture, and geometry were used to identify and track multiple objects in video frames, and Problem domain knowledge rules were applied to distinguish normal or anomalous activities as well as behaviours. Edge detection algorithm (Intersection over Union (IoU) threshold to determine possible edge connections) was applied toward enhancing the illumination variation by multi‐block Local Binary Pattern (LBP) temporal‐analysis to do the segmentation. Finally, the efficiency and effectiveness of the proposed approach has been estimated based on the measure of average PSNR, precision, recall, f‐measure, accuracy, and execution time. The Laboratory for Image and Media Understanding (LIMU) dataset has been utilised toward illustrating the robustness of the proposed approach. Furthermore, it evaluated the performance based on the measure of precision, recall, and F‐measure metrics. It has been tentatively demonstrated that the proposed approach is suitable for recognizing multiple moving object with detection accuracy up to 93.56%. The simulated results show that suggested approach is robust, flexible, as well as able to outperform the traditional methods than the present object detection method.

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