IEEE Transactions on Pattern Analysis and Machine Intelligence

The advent of Internet of Things (IoT) has shown a new direction of innovative research in agricultural domain. Being at nascent stage, IoT needs to be widely experimented so as to get widely applied in various agricultural applications. In this paper, I review various potential IoT applications, and the specific issues and challenges associated with IoT deployment for improved farming. To focus on the specific requirements the devices, and wireless communication technologies associated with IoT in agricultural and farming applications are analyzed comprehensively. Investigations are made on those sensor enabled IoT systems that provide intelligent and smart services towards smart agriculture. Various case studies are presented to explore the existing IoT based solutions performed by various organizations and individuals and categories according to their deployment parameters. Related difficulties in these solutions, while identifying the factors for improvement and future road map of work using the IoT are also highlighted.

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Internet of things for smart agriculture: Technologies, practices and future direction

The Internet of Things (IoT) has tremendous success in health care, smart city, industrial production and so on. Protected agriculture is one of the fields which has broad application prospects of IoT. Protected agriculture is a mode of highly efficient development of modern agriculture that uses artificial techniques to change climatic factors such as temperature, to create environmental conditions suitable for the growth of animals and plants. This review aims to gain insight into the state-of-the-art of IoT applications in protected agriculture and to identify the system structure and key technologies. Therefore, we completed a systematic literature review of IoT research and deployments in protected agriculture over the past 10 years and evaluated the contributions made by different academicians and organizations. Selected references were clustered into three application domains corresponding to plant management, animal farming and food/agricultural product supply traceability. Furthermore, we discussed the challenges along with future research prospects, to help new researchers of this domain understand the current research progress of IoT in protected agriculture and to propose more novel and innovative ideas in the future.

https://www.mdpi.com/1424-8220/19/8/1833/pdf

State-of-the-Art Internet of Things in Protected Agriculture.

Crop diseases cannot be accurately predicted by merely analyzing individual disease causes. Only through construction of a comprehensive analysis system can users be provided with predictions of highly probable diseases. In this study, cloud-based technology capable of handling the collection, analysis, and prediction of agricultural environment information in one common platform was developed. The proposed Farm as a Service (FaaS) integrated system supports high-level application services by operating and monitoring farms as well as managing associated devices, data, and models. This system registers, connects, and manages Internet of Things (IoT) devices and analyzes environmental and growth information. In addition, the IoT-Hub network model was constructed in this study. This model supports efficient data transfer for each IoT device as well as communication for non-standard products, and exhibits high communication reliability even in poor communication environments. Thus, IoT-Hub ensures the stability of technology specialized for agricultural environments. The integrated agriculture-specialized FaaS system implements specific systems at different levels. The proposed system was verified through design and analysis of a strawberry infection prediction system, which was compared with other infection models.

https://www.mdpi.com/1424-8220/18/11/4051/pdf

IoT-Based Strawberry Disease Prediction System for Smart Farming.

In this paper, we aim to establish a holistic framework that integrates the cyber-physical layers of a cloud-enabled Internet of Controlled Things (IoCT) through the lens of contract theory. At the physical layer, the device uses cloud services to operate the system. The quality of cloud services is unknown to the device, and hence the device designs a menu of contracts to enable a reliable and incentive-compatible service. Based on the received contracts, the cloud service provider (SP) serves the device by determining its optimal cyber defense strategy. A contract-based FlipCloud game is used to assess the security risk and the cloud quality of service (QoS) under advanced persistent threats. The contract design approach creates a pricing mechanism for on-demand security as a service for cloud-enabled IoCT. By focusing on high and low QoS types of cloud SPs, we find that the contract design can be divided into two regimes (regimes I and II) with respect to the provided cloud QoS. Specifically, the physical devices whose optimal contracts are in regime I always request the best possible cloud security service. In contrast, the device only asks for a cloud security level that can stabilize the system when the optimal contracts lie in regime II. We illustrate the obtained results via case studies of a cloud-enabled smart home.

Security as a Service for Cloud-Enabled Internet of Controlled Things Under Advanced Persistent Threats: A Contract Design Approach

Every year farmers experience huge losses due to pest infestation in crops & this inturn impacts his livelihood. In this paper we discuss a novel approach to solve this problem by constantly monitoring crops using video processing, cloud computing and robotics. The paper concentrates in methodologies to detect pests in one of the most popular fruits in the world - the tomato. An insight into how the idea of the Internet of Things can also be conceptualized in this project has been elaborated.

A novel cloud computing based smart farming system for early detection of borer insects in tomatoes

There are countless plant species available globally. To manage massive content, development of a fast and effective categorization methods has turned into a territory of dynamic research. As trees and plants are very important to ecology, accurate Identification and classification becomes necessary. Classification procedure is carried out through number of sub procedures. An identification or Classification issue is managed by mapping a input data with one of the unique classes. In this procedure, at first, database of a leaf images is created, that comprises of images of test leaf with their equivalent plant information. Essential features are extracted using image processing techniques. The features have to be stable in order to make the identification system robust. Subsequently the plant/leaf is recognized using machine learning techniques. In this paper a survey is presented on the various types of leaf identification process.

Survey on leaf recognization and classification

In this paper, we propose a robust and an accurate face recognition model which uses the feature extraction capabilities of fractional discrete cosine transform (FDCT). We apply FDCT on the face image database, and use transform coefficients as features. The proposed model is tested on publicly available standard AT&T face database to demonstrate the recognition accuracy. Extensive experimental comparison is provided with the DCT based face recognition algorithm to establish the superiority of the proposed model in terms of recognition accuracy.

Face recognition based on fractional discrete cosine transform

This research work focuses on to the development of neural network based detection and characterization of electrocardiogram (ECG) and electroencephalogram (EEG) signal. ECG and EEG signals have prime importance for patients under critical care. These signals have to be continuously monitored and processed as they are inter dependent. In this research Dyadic wavelet transform (DyWT) is used to process ECG data and Daubechies wavelet transform (DWT) is used to process EEG data. Emerging back propagation NN algorithm and Hopfield algorithm is used to detect and characterize both ECG and EEG signals. The different ECG and EEG data's have been collected and simultaneously processed and recognized.

Wavelet transform based neural network model to detect and characterise ECG and EEG signals simultaneously

Archaeological departments throughout the world have undertaken massive digitization projects to digitize their historical document corpus. In order to provide worldwide visibility to these historical documents residing in the digital libraries, a character recognition system is an inevitable tool. Automatic character recognition is a challenging problem as it needs a cautious blend of enhancement, segmentation, feature extraction, and classification techniques. This work presents a novel holistic character recognition system for the digitized Estampages of Historical Handwritten Kannada Stone Inscriptions (EHHKSI) belonging to 11th century. First, the EHHKSI images are enhanced using Retinex and Morphological operations to remove the degradations. Second, the images are segmented into characters by connected component labeling. Third, LBP features are extracted from these characters. Finally, decision tree is used to learn these features and classify the characters into appropriate classes. The LBP features improved the performance of the system significantly.

G. Thippeswamy

Papers

A novel cloud computing based smart farming system for early detection of borer insects in tomatoes

Survey on leaf recognization and classification

Face recognition based on fractional discrete cosine transform

Wavelet transform based neural network model to detect and characterise ECG and EEG signals simultaneously

Recognition of Historical Handwritten Kannada Characters Using Local Binary Pattern Features