Water management is paramount in countries with water scarcity. This also affects agriculture, as a large amount of water is dedicated to that use. The possible consequences of global warming lead to the consideration of creating water adaptation measures to ensure the availability of water for food production and consumption. Thus, studies aimed at saving water usage in the irrigation process have increased over the years. Typical commercial sensors for agriculture irrigation systems are very expensive, making it impossible for smaller farmers to implement this type of system. However, manufacturers are currently offering low-cost sensors that can be connected to nodes to implement affordable systems for irrigation management and agriculture monitoring. Due to the recent advances in IoT and WSN technologies that can be applied in the development of these systems, we present a survey aimed at summarizing the current state of the art regarding smart irrigation systems. We determine the parameters that are monitored in irrigation systems regarding water quantity and quality, soil characteristics and weather conditions. We provide an overview of the most utilized nodes and wireless technologies. Lastly, we will discuss the challenges and the best practices for the implementation of sensor-based irrigation systems.

https://www.mdpi.com/1424-8220/20/4/1042/pdf

IoT-Based Smart Irrigation Systems: An Overview on the Recent Trends on Sensors and IoT Systems for Irrigation in Precision Agriculture

IoT based low cost and intelligent module for smart irrigation system

Model output statistics downscaling using support vector machine for the projection of spatial and temporal changes in rainfall of Bangladesh

The energy harvesting (EH) from unused natural waste energy sources is common nowadays because of rising power demand. The sources have the potential of producing micro to milliwatts power depending on the ambient conditions. Many researchers have been concentrating on micro-level energy harvesting to provide power to the micro-devices in a remote area. The concept leads to a drastic reduction in cost. Once the structure is established, it can generate electricity with minimal cost or effort like renewable sources. This paper reviews the two key areas of the piezoelectric energy harvesting system (PEHS), namely, mechanical and electronic approaches, developed by several researchers. From the thorough review, it is realized that the existing technologies more or less can capable to EH by using the piezoelectric elements; however, the consistency and stability of the systems are not up to the mark yet. In this study, vibration-based PEHS has been considered for the application of the optimization technique to enhance its performance. This review has been focused on numerous challenges and recommendations for next-generation EH by utilizing vibration-based piezoelectric elements.

Review of piezoelectric energy harvesting system and application of optimization techniques to enhance the performance of the harvesting system

A B S T R A C T In this paper the K-means (KM) and the Fuzzy C-means (FCM) algorithms were compared for their computing performance and clustering accuracy on different shaped cluster structures which are regularly and irregularly scattered in two dimensional space. While the accuracy of the KM with single pass was lower than those of the FCM, the KM with multiple starts showed nearly the same clustering accuracy with the FCM. Moreover the KM with multiple starts was extremely superior to the FCM in computing time in all datasets analyzed. Therefore, when well separated cluster structures spreading with regular patterns do exist in datasets the KM with multiple starts was recommended for cluster analysis because of its comparable accuracy and runtime performances.

/pdf/comparison-of-k-means-and-fuzzy-c-means-algorithms-on-oabl97v4cy.pdf

Comparison of K-Means and Fuzzy C-Means Algorithms on Different Cluster Structures

Segmentation of an image entails the division or separation of the image into regions of similar attribute. The most basic attribute for segmentation of an image is its luminance amplitude for a monochrome image and color components for a color image. Clustering is one of the methods used for segmentation. The objective of this paper is to compare the performance of various segmentation techniques for color images. K-means clustering and Fuzzy C-Means clustering techniques are compared for their performance in segmentation of color images. Keywords— K-Means clustering, Fuzzy CMeans Clustering.

/pdf/a-comparative-analysis-of-fuzzy-c-means-clustering-and-k-58h0u86n17.pdf

"A Comparative Analysis of Fuzzy C-Means Clustering and K Means Clustering Algorithms"

The techniques of machine vision and digital image Processing are extensively applied to agricultural science and it have great perspective especially in the plant protection field, which ultimately leads to crops management. The paper proposes a software prototype system for early pest detection on the infected crops in greenhouse. Images of the infected leaf are captured by a camera with pan tilt and zoom and processed using image processing techniques to detect presence of pests. SVM classifier is used for classification. SVM classifier helps to detect the pests and in the classification of pest based on their features. Results show more precision in identifying the presence of pest at early stage.

/pdf/detection-and-classification-of-pests-in-greenhouse-using-3g7w279fn5.pdf

Detection and Classification of Pests in Greenhouse Using Image Processing

This paper presents design of precision agriculture system infrastructure aiming at a multi-parameter monitoring system using wireless sensor network. Proposed infrastructure is based on low-power Intel’s Galileo Gen-2 platform for monitoring, controlling and decision-making support using Internet of Things (IoT). Collection of different farm field parameters is to be done using sensor nodes deployed in the farmland. Each node is connected wirelessly to the base station for the collection of data using wireless transreciever hardware platform. Data is then fed to the personal computer and displayed on screen, e.g. temperature, humidity, sprinkler water flow and soil moisture. From the collected data, decision-making and controlling action can be taken by the use of Internet of Things.

Precision Agriculture System Design Using Wireless Sensor Network

In recent years due to globalization and increase in competition to capture market share, the customer is having many options of cars at much affordable prices. This has led to tremendous amount of cars on the road. The big cities in India are normally crowded and the city's existing infrastructure falls short to satisfy the growing needs. This problem is also combined with the inefficient use of parking space and uneven distribution of traffic which leads to congestion in vehicular traffic. This paper introduces a novel technique based on Internet of Things platform to improve efficiency of the existing parking systems. It guides driver to find nearest parking slots. The prototype uses cloud based architecture of Internet of things. There is minimum delay in finding an optimal parking slot which helps to ease the traffic congestion. The proposed system also helps users automatically find a free parking space at the smallest distance based on Haversine formula if nearest parking region is full.

Parking guidance system using Internet of Things

Early pest detection and identification in agriculture is necessary for good quality and quantity of crop production. This will help to reduce the effect of pest and the use of pesticides. Detection of pest is necessary to get the information and location of the pest and this can be achieved only if temperature is controlled in the room like in the greenhouse. There are various conventional techniques which are used to detect the pest, but the proposed method is very fast, easy and convenient. This project proposes pest detection and movement by using the video processing. Video processing involves capturing the video and processing on it to find the presence and location of pest.

V. V. Gohokar

Papers

"A Comparative Analysis of Fuzzy C-Means Clustering and K Means Clustering Algorithms"

Detection and Classification of Pests in Greenhouse Using Image Processing

Precision Agriculture System Design Using Wireless Sensor Network

Parking guidance system using Internet of Things

Agriculture pest detection using video processing technique