The nature of statistical learning theory
The nature of statistical learning theory
From wireless sensors to field mapping: Anatomy of an application for precision agriculture
Computers and Electronics in Agriculture
SAINT-W '07 Proceedings of the 2007 International Symposium on Applications and the Internet Workshops
SAINT-W '07 Proceedings of the 2007 International Symposium on Applications and the Internet Workshops
A computer-vision based precision seed drill guidance assistance
Computers and Electronics in Agriculture
Original paper: A vision based row detection system for sugar beet
Computers and Electronics in Agriculture
Crop/weed discrimination in perspective agronomic images
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Real-time measurement of soil attributes using on-the-go near infrared reflectance spectroscopy
Computers and Electronics in Agriculture
Field comparison of two prototype soil strength profile sensors
Computers and Electronics in Agriculture
A real-time wireless smart sensor array for scheduling irrigation
Computers and Electronics in Agriculture
Measuring crop biomass density by laser triangulation
Computers and Electronics in Agriculture
Autopilot for a combine harvester
Computers and Electronics in Agriculture
Detecting infestation of take-all disease in wheat using Landsat Thematic Mapper imagery
International Journal of Remote Sensing
IEEE Distributed Systems Online
Wavelet transform to discriminate between crop and weed in perspective agronomic images
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Wireless Sensor Networks for precision horticulture in Southern Spain
Computers and Electronics in Agriculture
Weed detection in multi-spectral images of cotton fields
Computers and Electronics in Agriculture
Large-scale investigation of weed seed identification by machine vision
Computers and Electronics in Agriculture
Software development for real-time ultrasonic mapping of tree canopy size
Computers and Electronics in Agriculture
Plant species identification using Elliptic Fourier leaf shape analysis
Computers and Electronics in Agriculture
A wearable module for recording worker position in orchards
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Assessment of forage mass from grassland swards by height measurement using an ultrasonic sensor
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Review: Future internet and the agri-food sector: State-of-the-art in literature and research
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Variable rate sprayer. Part 2 - Vineyard prototype: Design, implementation, and validation
Computers and Electronics in Agriculture
Prototyping the visualization of geographic and sensor data for agriculture
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
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With the advances in electronic and information technologies, various sensing systems have been developed for specialty crop production around the world. Accurate information concerning the spatial variability within fields is very important for precision farming of specialty crops. However, this variability is affected by a variety of factors, including crop yield, soil properties and nutrients, crop nutrients, crop canopy volume and biomass, water content, and pest conditions (disease, weeds, and insects). These factors can be measured using diverse types of sensors and instruments such as field-based electronic sensors, spectroradiometers, machine vision, airborne multispectral and hyperspectral remote sensing, satellite imagery, thermal imaging, RFID, and machine olfaction system, among others. Sensing techniques for crop biomass detection, weed detection, soil properties and nutrients are most advanced and can provide the data required for site specific management. On the other hand, sensing techniques for diseases detection and characterization, as well as crop water status, are based on more complex interaction between plant and sensor, making them more difficult to implement in the field scale and more complex to interpret. This paper presents a review of these sensing technologies and discusses how they are used for precision agriculture and crop management, especially for specialty crops. Some of the challenges and considerations on the use of these sensors and technologies for specialty crop production are also discussed.