Despite the perception people may have regarding the agricultural process, the reality is that today's agriculture industry is data-centered, precise, and smarter than ever. The rapid emergence of the Internet-of-Things (IoT) based technologies redesigned almost every industry including “smart agriculture” which moved the industry from statistical to quantitative approaches. Such revolutionary changes are shaking the existing agriculture methods and creating new opportunities along a range of challenges. This article highlights the potential of wireless sensors and IoT in agriculture, as well as the challenges expected to be faced when integrating this technology with the traditional farming practices. IoT devices and communication techniques associated with wireless sensors encountered in agriculture applications are analyzed in detail. What sensors are available for specific agriculture application, like soil preparation, crop status, irrigation, insect and pest detection are listed. How this technology helping the growers throughout the crop stages, from sowing until harvesting, packing and transportation is explained. Furthermore, the use of unmanned aerial vehicles for crop surveillance and other favorable applications such as optimizing crop yield is considered in this article. State-of-the-art IoT-based architectures and platforms used in agriculture are also highlighted wherever suitable. Finally, based on this thorough review, we identify current and future trends of IoT in agriculture and highlight potential research challenges.

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Internet-of-Things (IoT)-Based Smart Agriculture: Toward Making the Fields Talk

An Introduction to LDPC Codes

Precision agriculture using IoT data analytics and machine learning

The limited capacity to recognize faces under occlusions is a long-standing problem that presents a unique challenge for face recognition systems and even for humans. The problem regarding occlusion is less covered by research when compared to other challenges such as pose variation, different expressions, etc. Nevertheless, occluded face recognition is imperative to exploit the full potential of face recognition for real-world applications. In this paper, we restrict the scope to occluded face recognition. First, we explore what the occlusion problem is and what inherent difficulties can arise. As a part of this review, we introduce face detection under occlusion, a preliminary step in face recognition. Second, we present how existing face recognition methods cope with the occlusion problem and classify them into three categories, which are 1) occlusion robust feature extraction approaches, 2) occlusion aware face recognition approaches, and 3) occlusion recovery based face recognition approaches. Furthermore, we analyze the motivations, innovations, pros and cons, and the performance of representative approaches for comparison. Finally, future challenges and method trends of occluded face recognition are thoroughly discussed.

A survey of face recognition techniques under occlusion

Face recognition is an efficient technique and one of the most preferred biometric modalities for the identification and verification of individuals as compared to voice, fingerprint, iris, retina eye scan, gait, ear and hand geometry. This has over the years necessitated researchers in both the academia and industry to come up with several face recognition techniques making it one of the most studied research area in computer vision. A major reason why it remains a fast-growing research lies in its application in unconstrained environments, where most existing techniques do not perform optimally. Such conditions include pose, illumination, ageing, occlusion, expression, plastic surgery and low resolution. In this paper, a critical review on the different issues of face recognition systems are presented, and different approaches to solving these issues are analyzed by presenting existing techniques that have been proposed in the literature. Furthermore, the major and challenging face datasets that consist of the different facial constraints which depict real-life scenarios are also discussed stating the shortcomings associated with them. Also, recognition performance on the different datasets by researchers are also reported. The paper is concluded, and directions for future works are highlighted.

A review on face recognition systems: recent approaches and challenges

Autonomous agriculture robot is one of the promising solutions for precision agriculture. This paper presents the proposed sensor and vision based agricultural robot for sowing seeds. This prototype can navigate it on any agricultural land and perform seed sowing operation simultaneously. The onboard sensors along with vision system and vision based approaches achieves the navigation and localization tasks. Self-awareness of the robot's position is determined by the global and local maps generated from Global Positioning System (GPS) and on-board vision system paired with a personal computer. This paper also presents the proposed sensor based precision seed metering and sowing mechanism. The proposed robot is a micro planter whose primary task would be to sow seeds at prefixed seeding intervals in the field. The dimensions of the proposed robot are 26.5 × 18.5 × 19.65 mm as L × B × H respectively. A suspension system has been used to maintain the stability of the vehicle and prevent it from toppling in motion.

Sensor and vision based autonomous AGRIBOT for sowing seeds

Cognitive radios (CRs) mainly aim to reuse the spectrum holes in order to efficiently utilize the available scarce radio spectrum. However, current CRs techniques have a throughput limitation problem which ultimately limits telecommunication applications horizons nowadays. Moreover, achieving high throughput will overcome the bottleneck of CRs application limitations to the reporting and browsing applications only. To tackle this emerging throughput limitation issue in the CRs, this paper proposes the online greedy throughput maximization (OGTM) algorithm which overcomes the throughput limitations. OGTM allows the sensing cycle frame to have a variable length according to the assumed decision validity interval. Then, OGTM varies the decision validity interval of secondary users (SUs) based on the primary users (PUs) historical behavior. As a proof of concept, we developed a simulator in order to evaluate the performance of the proposed OGTM technique. The simulation results show that SUs benefit from the limited PU historical behavior learning, which resultantly increases the throughput up to 95% and at the same time decreases the miss detection probability by 50%.

Maximizing Cognitive Radio Networks Throughput Using Limited Historical Behavior of Primary Users

Presently, emergency vehicles use lights and sirens to alert road users of their approach and presence. Unfortunately, pedestrians and vehicle drivers may react too late or in a wrong way and increase emergency vehicle response times and collisions caused by these emergency vehicles. This paper presents an information dissemination system to clearly report the arrival and destination directions of the emergency vehicle to road users to reduce collisions and response times of emergency vehicles. The proposed information dissemination system is based on a new MAC protocol named Frequency and Distance based Priority MAC (FDP-MAC) to send the emergency messages to the traffic management center and other vehicles with lesser delay. The proposed system is simulated in NS-2. The proposed MAC prioritizes and delivers packets according to their service type and distance between the vehicle and intersection. The performance of the proposed MAC is analyzed and compared with the standard IEEE 802.15.4 and IEEE 802.11p MAC. The results show that the FDP-MAC delivers the emergency messages faster than IEEE 802.15.4 and IEEE 802.11p. Moreover, this work will help the effective dissemination of information, management of emergency vehicles and help reduce their response time as well as number of potential collisions.

A fast information dissemination system for emergency services over vehicular ad hoc networks

Face recognition systems as a biometrie system for human identification and verification has indeed shown a lot of progress over the years, illustrated through the various applications it has been used for. These applications include Human Computer Interaction systems, law, terrorist attacks, health and multimedia indexing. Furthermore, the installation of face recognition systems in public and private places helps with security improvement. However, occlusion as one of the major issues of the face recognition system tends to reduce its performance in real world applications. In this paper, the effect of occlusion scenario on face recognition technique was investigated. Also, the part of the occluded face region affecting the face recognition system is discussed.

Nellore Kapileswar

Papers

Sensor and vision based autonomous AGRIBOT for sowing seeds

Maximizing Cognitive Radio Networks Throughput Using Limited Historical Behavior of Primary Users

A fast information dissemination system for emergency services over vehicular ad hoc networks

Evaluating the effect of occlusion in face recognition systems

Hybrid intrusion detection system for wireless IoT networks using deep learning algorithm