Precision agriculture is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in the agricultural industry moderates the usage of resources and can increase the quality of food in the post-pandemic world. Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and harvesting. Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation. Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation. The automation in image analysis with computer vision and deep learning models provides precise field and yield maps. In this review, it has been observed that the application of deep learning techniques has provided a better accuracy for smart farming. The crops taken for the study are fruits such as grapes, apples, citrus, tomatoes and vegetables such as sugarcane, corn, soybean, cucumber, maize, wheat. The research works which are carried out in this research paper are available as products for applications such as robot harvesting, weed detection and pest infestation. The methods which made use of conventional deep learning techniques have provided an average accuracy of 92.51%. This paper elucidates the diverse automation approaches for crop yield detection techniques with virtual analysis and classifier approaches. Technical hitches in the deep learning techniques have progressed with limitations and future investigations are also surveyed. This work highlights the machine vision and deep learning models which need to be explored for improving automated precision farming expressly during this pandemic.

Heartbeat is visualized as a series of electrical waves on the display. ECG signal conveys information about structure and functions of heart. To check the hear

As per the statistics taken in the year 2020, a measurable audit shows that over 35% of the Indian population is influenced with foot ulcer. Foot ulcer is an issue looked by the greater part of the patient who have gone through diabetes mellitus (DM). Around 20% of the diabetes patients is influenced by foot ulcer, another 20% is affected by diabetic neuropathy, 30% patient are influenced with both the conditions. Generally the foot ulcer can be inspected with x-beams, bone output, MRI, CT, Bacterial culture of the ulcer, and even with blood tests. These methods are inspected by obtrusive method of estimations that will hurt the patient more. In order to reduce the discomfort during the interaction and furthermore for the early identification of this condition, this arrangement has been created. This pre-recognition measure is a non-intrusive method for estimation with the thermal imager. The temperature scope of ordinary foot is under 30 degree Celsius. It very well may be fluctuated (expanded) least of 2 degree Celsius for ulcered foot. These ulcered foot pictures has been procured with the assistance of thermal imager (AMG8833) and handled utilizing MATLAB and result is shown in a histogram. This histogram helps us to differentiate the typical and ulcered foot. The classification is done using neural network.

Objectives: In the present scenario, modern Industrial and Commercial Systems require communication networks to exchange information between spatially distributed communicating equipments. The challenge for researches in this area is to compensate for transmission delay, packet loss and manage network traffic. This paper focuses on design of controllers for one such application where the process and the controller communicate through Ethernet via TCP/IP protocol. Methods/Analysis: Different control strategies for temperature control in Networked control System, is analyzed and the performance of Conventional and Model based controllers over the network was compared in terms of performance metrics like settling time and peak-overshoot. The transmission delay was deliberately introduced into the system and the controller performance was analysed for different traffic levels by introducing imaginary nodes in the network. The simulation was done using LabVIEW. Findings: The obtained results prove that Model based controllers perform better in networked environment, offering faster settling time and reduced peak overshoots. Conclusion: The network delay and traffic introduced into the process disturbs the stability of the system, but the control strategies adopted in this work offers better performance and maintain the stability of the system.