Monitoring Winter Wheat Growth Using Plant Nutrition Active Sensor
Abstract: A real-time integrated crop monitoring and application system is needed to be developed for the wheat growth environment. This research developed a ground base-sensing embedded on tractor which monitors the growth of winter wheat by using a couple of plant nutrition active sensors (Crop Spec) and RTK-GPS. In this active sensor, two different infrared wavelengths were used for determination of vegetation index which named S1-Value in this research. In order to consider the reliability of results a test field was cultivated winter wheat in main campus of Hokkaido University and four level of fertilizer were applied to make the difference in crop condition. The 20 points as a ground reference from field were randomly chosen and growth information (grass height, SPAD value, number of stem, nitrogen content and reflectance data) was acquired. By investigating correlation with S1 value and growth information, growth presumption using this plant nutrition sensor and its accuracy were verified. The results showed that there was high relationship between S1 Value and other ground reference data such as grass height, SPAD value, nitrogen Content and reflectance data which acquired by using a Spectroradiometer. So because of easily to use, on-the-go and speed of scanning of field by the Crop Spec, that is recommended for monitoring and estimation of crop growth condition. Keywords: remote sensing, Crop Canopy, Spectroradiometer, SPAD value, winter wheat
The global agricultural workforce continues to decrease, with individual workers being responsible for greater area of land. Application of precision farming (PF) is one the solutions to solve this problem. New concepts and new agricultural technology are needed (Noguchi et al., 1999). The need for a crop sensor for monitoring input resource application, especially nitrogen in field crops has been realized since the earliest days of computer controlled precision agriculture in the 1980s, but most of the commercial precision agriculture was based on maps. The fertilizer applications have been determined with the help of soil tests for nutrients, targeted crop yields, and other spatial information. One of the key constraints to map-based N application is the requirement of highly skilled personnel. The relatively slow adoption pattern of precision agriculture suggests that motivating farmers to spend time for analyzing data requires profits at farmer's level. Use of real-time sensing to develop variable rate application reduces the management time constraint to site-specific crop management (Ishwar Singh and et al, 2006). There are two basic methods of implementing site-specific management (SSM) for the variable-rate application (VRA) of crop production inputs: map-based and sensor-based. The map-based SSM method is based on the use of maps to represent crop yields, soil properties, pest infestations, and VRA plans. The sensor-based SSM method provides the capability to vary the application rate of crop production inputs with no mapping involved. The sensor-based method utilizes sensors to measure the desired properties, soil properties or crop characteristics, on the go. Measurements made by such a system are then processed and used immediately to control a variable-rate applicator. At this point, the major challenge is to develop sensors that will work accurately in field conditions at realistic working speeds. Sensor-based application systems must be capable of accomplishing the sensing, data processing, and application rate adjustment steps in one machine pass (V. Alchanatis and et al, 2005). The concept of precision agriculture has highlighted the need for simple and reliable tools to predict the within-field differences in fertilizer requirements (Dawson, 1997; McBratney and Pringle, 1997). Today, crop status sensors are used...
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