Agriculture is the primary source of livelihood for a large section of the society in India, and the ever-increasing demand for high quality and high quantity yield calls for highly efficient and effective farming methods. Grow-IoT is a smart analytics app for comprehensive plant health analysis and remote farm monitoring platform to ensure that the farmer is aware of all the critical factors affecting the farm status. The cameras installed on the field facilitate capturing images of the plants to determine plant health based on phenotypic characteristics. Visual feedback is provided by the computer vision algorithm using image segmentation to classify plant health into three distinct categories. The sensors installed on the field relay crucial information to the Cloud for real-time optimized farm status management. All the data relayed can then be viewed using the user-friendly Grow-IoT app to remotely monitor integral aspects of the farm and take the required actions in case of critical conditions. Thus, the mobile platform combined with computer vision for plant health analysis and smart sensor modules gives the farmer a technical perspective. The simplistic design of the application makes sure that the user has the least cognitive load while using it. Overall, the smart module is a significant technical step to facilitate efficient produce across all seasons in a year.

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Introduction: Osteomyelitis is an inflammatory process that affects bone due to the contiguous infection, direct inoculation, or haematogenous spread of microorganisms. It is an infectious disease that is difficult to diagnose and treatment is complex because of its heterogeneity, pathophysiology, clinical presentation and management. Aim: To determine microbiological profile osteomyelitis and antibiotic resistance pattern of bacterial isolates with special reference to Multidrug Resistance (MDR) strains. Materials and Methods: A cross-sectional study was conducted in the Department of Microbiology and Department of Orthopedics Rama Medical College Hospital and Research Centre Kanpur, Uttar Pradesh, India. A total of 100 samples from osteomyelitis cases were aerobically cultured and isolates from culture positives were identified by standard procedures. Antimicrobial Susceptibility Testing (AST) was done following Clinical and Laboratory Standards Institute (CLSI) guidelines. Staphylococcal isolates were screened for methicillin resistance and Gram negative bacilli were screened for MDR production. Results: Out of 100 samples, 76% were culture positive and 24% were culture negative. Males were more affected than females. Staphylococcal spp. (47.3%) was predominant, E. coli (14.4%) and Klebsiella spp. (11.8%), Pseudomonas spp. (9.2%), Proteus spp. (5.2%), Coagulase-Negative Staphylococci (CoNS) (3.9%). Among the MDR strains, Methicillin Resistant Staphylococcus aureus (MRSA) was 44.4%. All the MDR Staphylococcal isolates were 100% sensitive for linezolid. Among the MDR Gram negative bacilli were Extended Spectrum Beta Lactamases (ESBL) (50%), AmpC (17.6%) and Metallo Beta Lactamase (MBL) (14.7%) and they were 100% sensitive for polymixin B and colistin. Conclusion: The microbiological profile of osteomyelitis in the present study showed high prevalence of MRSA44% as the commonest agent, sensitive only to linezolid. E. coli ESBL (50%) and MBL-14.7% were sensitive only to colistin and polymixin B, therefore proper infection control practices and antibiotic policy has to be followed to reduce the incidence of MDR strains.

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