Internet of Things and Machine Learning Applications for Smart Precision Agriculture

Agriculture forms the major part of our Indian economy. In the current world, agriculture and irrigation are the essential and foremost sectors. It is a mandatory need to apply information and communication technology in our agricultural industries to aid agriculturalists and farmers to improve vice all stages of crop cultivation and post-harvest. It helps to enhance the country’s G.D.P. Agriculture needs to be assisted by modern automation to produce the maximum yield. The recent development in technology has a significant impact on agriculture. The evolutions of Machine Learning (ML) and the Internet of Things (IoT) have supported researchers to implement this automation in agriculture to support farmers. ML allows farmers to improve yield make use of effective land utilisation, the fruitfulness of the soil, level of water, mineral insufficiencies control pest, trim development and horticulture. Application of remote sensors like temperature, humidity, soil moisture, water level sensors and pH value will provide an idea to on active farming, which will show accuracy as well as practical agriculture to deal with challenges in the field. This advancement could empower agricultural management systems to handle farm data in an orchestrated manner and increase the agribusiness by formulating effective strategies. This paper highlights contribute to an overview of the modern technologies deployed to agriculture and suggests an outline of the current and potential applications, and discusses the challenges and possible solutions and implementations. Besides, it elucidates the problems, specific potential solutions, and future directions for the agriculture sector using Machine Learning and the Internet of things

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

IMPLEMENTATION OF SWITCHING CONTROLLER FOR THE INTERNET ROUTER

ABSTRACT The greedy scheduling (GS) algorithm is a scalable maximal matching algorithm. This algorithm was conceptually proposed and well received since it provides non-blocking in an Internet router with input buffers and a cross-bar, unlike other existing implementations. In this paper, I implent a new design of the GS algorithm, and determine its exact behaviour, performance and QoS that it provides. I design and measure the performance of their implementations in terms of their scalability and speed. It will be shown that multiple scheduler modules of a terabit Internet router can be implemented on a low-cost fieldprogrammable gate-array (FPGA) device, and that the processing can be performed within the desired time slot duration. The scheduler modules designed on one low-cost Altera FPGA may control router with hundreds of ports. The output selection time remains below 60 ns in high-capacity Internet routers

ACM International Conference Proceeding Series

Personnel working in health services and food preparation industries maintain hand hygiene by washing their hands. Often this follows the WHO hand hygiene guidelines. Systems exist to monitor and detect compliance with all stages of the guidelines. However, the critical step of verifying whether the subject has correctly lathered soap on the hands can only be monitored using wrist worn sensors. This comes with several disadvantages: the wearable sensors may become contaminated, require battery, the user forgets to wear or misplaces the device. We address these problems by proposing Wash In Depth, a system which uses a fixed contactless depth sensor mounted above the wash basin and is activated using a wireless trigger. As the system is fixed in position and contactless, there is reduced chance of contamination, the option to not require a battery and no possibility of forgetting or misplacement of the device. This system provides the potential ability to warn a person, after they have washed their hand, if improper application of soap was detected based on their hand gestures. We evaluate the ges- ture detection accuracy with 15 subjects and achieve 94% gesture detection accuracy. We deploy the system on a low power Compute Stick and demonstrate that it can keep track of hand gestures in real-time when video is recorded at 20 FPS

Catalytic Microwave Preheated Co-pyrolysis of lignocellulosic biomasses: A study on biofuel production and its characterization

In this present study, microwave pre-treatment has been used for sustainable biofuel production from three different biowastes through catalytic aided co-pyrolysis techniques. The experimental investigations have been carried out to develop biofuel at temperature (350-550℃), heating rate (15-50℃/min) and particle size (0.12-0.38mm). The resultant biofuels were characterized using TGA, DTA, FE-SEM, FTIR spectroscopy and NMR spectrum. The pyrolysis process of biomasses without and with catalyst resulted in the yield rate of 29-37% and 39-51% respectively. Moreover, the CaO catalytic co-pyrolysis process of pomegranate peel, groundnut shell and palmcone wastes with a ratio of 50:50 at 0.25mm particle size has resulted in the highest yield rate of 51.6%. The NMR result of bio-oil samples produced hydroxyl group and aliphatics which clearly state the suitability of bio-oils for automotive application. The bio-oil had promising fuel characteristics consisting more energy density (29.1MJ/kg), less oxygen content and free of nitrogen

The Hematopoietic Transcription Factor AML1 (RUNX1) Is Negatively Regulated by the Cell Cycle Protein Cyclin D3

The family of cyclin D proteins plays a crucial role in the early events of the mammalian cell cycle. Recent studies have revealed the involvement of AML1 transactivation activity in promoting cell cycle progression through the induction of cyclin D proteins. This information in combination with our previous observation that a region in AML1 between amino acids 213 and 289 is important for its function led us to investigate prospective proteins associating with this region. We identified cyclin D3 by a yeast two-hybrid screen and detected AML1 interaction with the cyclin D family by both in vitro pull-down and in vivo coimmunoprecipitation assays. Furthermore, we demonstrate that cyclin D3 negatively regulates the transactivation activity of AML1 in a dose-dependent manner by competing with CBFβ for AML1 association, leading to a decreased binding affinity of AML1 for its target DNA sequence. AML1 and its fusion protein AML1-ETO have been shown to shorten and prolong the mammalian cell cycle, respectively. In addition, AML1 promotes myeloid cell differentiation. Thus, our observations suggest that the direct association of cyclin D3 with AML1 functions as a putative feedback mechanism to regulate cell cycle progression and differentiation