The application of (smart) cameras for process control, mapping, and advanced imaging in agriculture has become an element of precision farming that facilitates the conservation of fertilizer, pesticides, and machine time. This technique additionally reduces the amount of energy required in terms of fuel. Although research activities have increased in this field, high camera prices reflect low adaptation to applications in all fields of agriculture. Smart, low-cost cameras adapted for agricultural applications can overcome this drawback. The normalized difference vegetation index (NDVI) for each image pixel is an applicable algorithm to discriminate plant information from the soil background enabled by a large difference in the reflectance between the near infrared (NIR) and the red channel optical frequency band. Two aligned charge coupled device (CCD) chips for the red and NIR channel are typically used, but they are expensive because of the precise optical alignment required. Therefore, much attention has been given to the development of alternative camera designs. In this study, the advantage of a smart one-chip camera design with NDVI image performance is demonstrated in terms of low cost and simplified design. The required assembly and pixel modifications are described, and new algorithms for establishing an enhanced NDVI image quality for data processing are discussed.

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Tracking individuals is a prominent application in such domains like surveillance or smart environments. This paper provides a development of a multiple camera setup with jointed view that observes moving persons in a site. It focuses on a geometrybased approach to establish correspondence among different views. The expensive computational parts of the tracker are hardware accelerated via a novel system-on-chip (SoC) design. In conjunction with this vision application, a hardware object request broker (ORB) middleware is presented as the underlying communication system. The hardware ORB provides a hardware/software architecture to achieve real-time intercommunication among multiple smart cameras. Via a probing mechanism, a performance analysis is performed to measure network latencies, that is, time traversing the TCP/IP stack, in both software and hardware ORB approaches on the same smart camera platform. The empirical results show that using the proposed hardware ORB as client and server in separate smart camera nodes will considerably reduce the network latency up to 100 times compared to the software ORB.

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