Agriculture remains a vital sector for most countries. It presents the main source of food for the population of the world. However, it faces a big challenge: producing more and better while increasing the sustainability with a reasonable use of natural resources, reducing environmental degradation as well as adapting to climate change. Hence, it is extremely important to switch from traditional agricultural methods to modern agriculture. Smart Agriculture is one of the solutions to deal with the growing demand for food while meeting sustainability requirements. In Smart Agriculture, the role of information is increasing. Information on weather conditions, soils, diseases, insects, seeds, fertilizers, etc. constitutes an important contribution to the economic and sustainable development of this sector. Smart management consists of collecting, transmitting, selecting and analyzing data. As the amount of agricultural data increases significantly, robust analytical techniques capable of processing and analyzing large amounts of data to obtain more reliable information and much more accurate predictions are essential. Data Mining is expected to play an important role in Smart Agriculture for managing real-time data analysis with massive data. The aim of this paper is to review ongoing studies and research on smart agriculture using the recent practice of Data Mining, to solve a variety of agricultural problems.

A comprehensive review of Data Mining techniques in smart agriculture

Technological advancements have led to an increased use of the internet of things (IoT) to enhance the resource use efficiency, productivity, and cost-effectiveness of agricultural production systems, particularly under the current scenario of climate change. Increasing world population, climate variations, and propelling demand for the food are the hot discussions these days. Keeping in view the importance of the abovementioned issues, this manuscript summarizes the modern approaches of IoT and smart techniques to aid sustainable crop production. The study also demonstrates the benefits of using modern IoT approaches and smart techniques in the establishment of smart- and resource-use-efficient farming systems. Modern technology not only aids in sustaining productivity under limited resources, but also can help in observing climatic variations, monitoring soil nutrients, water dynamics, supporting data management in farming systems, and assisting in insect, pest, and disease management. Various type of sensors and computer tools can be utilized in data recording and management of cropping systems, which ensure an opportunity for timely decisions. Digital tools and camera-assisted cropping systems can aid producers to monitor their crops remotely. IoT and smart farming techniques can help to simulate and predict the yield production under forecasted climatic conditions, and thus assist in decision making for various crop management practices, including irrigation, fertilizer, insecticide, and weedicide applications. We found that various neural networks and simulation models could aid in yield prediction for better decision support with an average simulation accuracy of up to 92%. Different numerical models and smart irrigation tools help to save energy use by reducing it up to 8%, whereas advanced irrigation helped in reducing the cost by 25.34% as compared to soil-moisture-based irrigation system. Several leaf diseases on various crops can be managed by using image processing techniques using a genetic algorithm with 90% precision accuracy. Establishment of indoor vertical farming systems worldwide, especially in the countries either lacking the supply of sufficient water for the crops or suffering an intense urbanization, is ultimately helping to increase yield as well as enhancing the metabolite profile of the plants. Hence, employing the advanced tools, a modern and smart agricultural farming system could be used to stabilize and enhance crop productivity by improving resource use efficiency of applied resources i.e., irrigation water and fertilizers.

/pdf/application-of-smart-techniques-internet-of-things-and-data-35zp9q2s.pdf

Application of Smart Techniques, Internet of Things and Data Mining for Resource Use Efficient and Sustainable Crop Production

Machine Learning for Smart Agriculture and Precision Farming: Towards Making the Fields Talk

Evaluation of Machine Learning approaches for precision Farming in Smart Agriculture System - A comprehensive Review

VLSI architecture for Vasanth sorting to denoise image with minimum comparators

Water is an essential element for growth of the plants. The amount of water given to the plants depends on its size, and moisture control of soil. The moisture of soil is affected by temperature of environment, evaporation due to wind velocity and the water budget. Saving water is vital for all irrigation projects. For water saving in the irrigation system, we need to monitor the parameters like atmospheric temperature, humidity, wind speed and direction, water radiation, soil temperature, sunshine and rain fall, etc., Based on these parameters, needed water should to given for the plants, based on its growth. This paper presents a low cost Fuzzy Logic Controller (FLC) based automatic irrigation system to irrigate the crop efficiently with water savings. This system will save the use of water and other nutrients efficiently to improve the yield of the crop. Index - Fuzzy Logic, Irrigation System, Water, Wireless Sensor Networks

https://aeijmr.com/automatic irrigation system using fuzzy logic.pdf

Automatic Irrigation System using Fuzzy Logic

This study presents a fast search algorithm and its Very Large Scale Integration (VLSI) design to implement an Enhanced Diamond Search (EDS) of Block-based Motion estimation for video compression systems. The proposed algorithm reduces the number of search points with a slight increase in average Sum-of-Absolute Difference (SAD) per pixel and significant reduction in Peak Signal-to-Noise Ratio (PSNR) than the Full search (FS). The speed improvement ratio is 11.26%. A novel Motion Estimation (ME) algorithm is proposed in this study that exhibits some properties with potential to facilitate optimized VLSI implementation and to achieve high performance for real video sequences. The main characteristic of this proposed architecture is possessing of only five processing elements (PE) that are used to calculate minimum SAD by using efficient comparators and compressors. Simulation results indicate that our proposed architecture employs low power and processes the video data with high speed than existing architectures without significant change in the video quality.

VLSI Implementation of High Performance Optimized Architecture for Video Coding Standards

Energy efficiency is an important property of mobile and pervasive computing devices. Dynamic voltage scaling (DVS) is an energy saving technique, achieve this property by reducing energy dissipation of the core by lowering the supply voltage and operating frequency. In this paper a simulation environment for testing of different DVS algorithms under EDF and RM have been discussed- as there are no standard simulators readily available in market. The simulator environment provides a framework for objective performance evaluations of different DVS algorithms. Several key DVS algorithms recently proposed for hard real-time periodic task sets have been compared; analyzed for their energy efficiency and discussed the performance differences quantitatively. It is shown through simulations that the real time DVS algorithms closely approach the theoretical lower bound on energy consumption and can easily reduce energy consumption (15-20%) in an embedded real-time system.

A simulation software development for performance analysis of dvs algorithm for low power embedded system

3 Abstract: An eye image has useless parts such as eyelid and eyelash apart from iris. These useless parts are masked and iris region alone is detected by Generalized structure Tensor (GST). Independent Component Analysis (ICA) is used to extract texture features in the iris. The best features (small subset) are then selected from the extracted texture features using Cuckoo Search algorithm which is an evolutionary algorithm. The best features of a person are then compared by calculating their hamming distance with a number of features of different individuals in the database for identification. The effectiveness is proved by comparing the proposed feature selection approach with other feature selection methods and without feature selection on a CASIA v3.0 database. The experimental results on this database show the proposed approach can achieve higher matching rates in iris authentication.

/pdf/person-identification-with-iris-recognition-based-on-34a03tkd1c.pdf

Person Identification with Iris Recognition Based on Generalized Structure Tensor

Feature selection is an optimization technique used in Iris recognition technology. For producing the most accurate recognition of iris from the database, feature selection removes the unrelated, noisy and unwanted data. Parallel Cat Swarm Optimization Algorithm is one of the latest optimization algorithms in the nature league based algorithm. Its enhancement results are better than the PSO and CSO optimization algorithms. The proposal of applying the Parallel Cat Swarm algorithm is mainly used for feature selection in the process of Iris recognition. For human identification iris can be used as it is an integral part of the human body. Biometric iris recognition system compares the two iris images and produces a matching score to determine their degree of equality or inequality. Eyelid and eyelash are considered to be the unwanted parts of the eye apart from iris. By using Structure Tensor Analysis we can mask the unwanted parts of iris by taking the iris as region of interest. By using Independent Component Analysis, we can extract the texture feature in the iris from the eye. The best features are then selected using Parallel Cat Swarm algorithm from the extracted texture features. For identification purpose we need to compare the best feature with a number of features of various individuals in the database.

Iris recognition optimized by ica using parallel cat swarm optimization

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