Embedded software and hardware implementation system for a human machine interface based on ISOAgLib

Modern agricultural machinery demands adoption of embedded electronic and remote sensing technology for precision agriculture.One of the electronic devices commonly used is the virtual terminal(VT) for tractors.A VT’s functions and terminology are described in the ISO 11783 standard.This paper presents a control system design and implementation for a VT and some other electronic control units(ECUs) for agricultural vehicles based on that standard.Hardware and software development for the VT is implemented using the ISOAgLib open library,in the advanced embedded system.The main part of the system is an embedded board based on a Samsung S3C6410 ARM11 core microprocessor with a controller area network(CAN) module.Its working environment is Windows Embedded CE 6.0(WinCE6.0).The ISOAgLib library provides abundant open sources consistent implementation of ISO 11783.It is written in C++ programming language using object-oriented technology.In this paper,we describe an ISO 11783-based tractor control system with a CAN and its implementation in the embedded system.This paper also explains the operation of a CAN-bus device driver in WinCE6.0 and some modifications of ISOAgLib for our target system.The target system consists of the VT,an ECU for the global positioning system(GPS),and an ECU for lighting for an agricultural tractor.The ECU for GPS and the ECU of a light controller are implemented using STM32F107F ARM Cortex M3-based development boards.     

A hierarchical approach to Compact Airborne Spectrographic Imager (CASI) high-resolution image classification of Little Miami River Watershed for environmental modelling

Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery was acquired over the Little Miami River Watershed in Ohio, USA, which was one of the largest hyperspectral image acquisitions. A hierarchical approach was employed using two different classification algorithms: ‘image object segmentation’ for level 1 and ‘spectral angle mapper’ (SAM) for level 2. This classification scheme was developed to overcome the spectral inseparability of urban and rural features and to deal with radiometric distortions due to cross-track illumination. The land‐use/land‐cover (LULC) classes were urban/built, corn, soya bean, wheat, forest, dry herbaceous, grass, lentic, lotic, urban barren, rural barren and unclassified. The final phase of processing was completed after an extensive quality assurance and quality control (QA/QC) phase with 902 points. The overall accuracy was 83.9%. The data set was made available for public research and application; certainly, this product represents an improvement over more commonly utilized, coarser spatial resolution data sets such as National Land Cover Data (NLCD).