Agriculture is the backbone of the Indian economy and the production rate of agricultures is based on detection and classification of plant leaf diseases. The conventional machine learning methods are failed to meet the maximum classification accuracy. Therefore, this article is focused on implementation of transfer learning models for feature extraction. Initially, non-local means filters (NLMF) are used to perform the preprocessing operation, which removed the different types of noises and also enhances the region of plant diseased region. Then, hybrid k-means clustering (HKMC) is used to localize the diseased region by segmentation operation. In addition, log of gradients descriptor (LOG) is used to extract the diverse features from the dataset. Finally, deep convolutional neural network (DCNN)is used to classify the different types of plant diseases. Further, the simulation results show that the proposed method resulted in superior performance as compared to existing approaches for both subjective and objective analysis.

Nowadays, power consumption is one of the primary considerations in the design of VLSI (Very Large Scale Integration) circuits based on complementary metal oxide semiconductors (CMOS) and carbon nanotube field effect transistors (CNTFET). This is because of the present circumstance. The fundamental reason for this is that power utilisation has been raised to the status of a top priority due to the improvements in integration and scaling as well as the constant increases in operating frequency. Additional power consumption from circuits and designs makes them challenging to implement in portable devices. The quantity of power lost during operation has an immediate effect on the cost of packaging the IC and systems. A variety of power dissipation sources and low power VLSI design strategies for CMOS and CNTFET-based circuits are discussed in this article.

The quantity of food that must be provided is being affected by the progressively deteriorating state of India’s agricultural industry. Many Indian farmers have shifted their focus away from farming and into other industries. This research is useful because it evaluates alternative approaches to selecting crops, planting them, spotting weeds, and keeping tabs on the system. All of these factors add up to a productive output, but they are hampered by things like a lack of workers and unfavorable environmental conditions. This research looked at the system from multiple angles, primarily focusing on image processing, artificial intelligence,machine learning and the internet of things. The research includes a comparison of the current framework with its most up-to-date counterpart.

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