Spectral characteristics of rice plants infested by brown planthoppers.
ABSTRACT Spectral characteristics of rice plants at various levels of infestation by the brown planthopper, Nilaparvata lugens (Stål), (Homoptera:Delphacidae), in the early grain-filling stage were measured and analyzed using a spectroradiometer. Plant damage was classified into six scales, i.e., 0 (CK), 1, 3, 5, 7 and 9, based on the scale of infestation displayed on the surfaces of plant parts. Results showed that mean curves of reflectance spectra (350 - 1800 nm) from different scales of insect infestation were clearly differentiated, especially in the region of 737 - 925 nm, where reflectance was in the order of severity. There were significant differences in reflectance among infestations at wavelengths of 755 and 890 nm particularly. Spectral parameters such as the normalized difference vegetation index (NDVI) and cumulative reflectance may also be used to discriminate levels of infestation. Twelve wavelengths from apparent peaks and valleys of individual spectra were selected as characteristic wavelengths making up the spectral signature of each infestation.
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ABSTRACT: Diseases in plants cause major production and economic losses in agricultural industry worldwide. Monitoring of health and detection of diseases in plants and trees is critical for sustainable agriculture. To the best of our knowledge, there is no sensor commercially available for real-time assessment of health conditions in trees. Currently, scouting is most widely used mechanism for monitoring stress in trees, which is an expensive, labor-intensive, and time-consuming process. Molecular techniques such as polymerase chain reaction are used for the identification of plant diseases that require detailed sampling and processing procedure. Early information on crop health and disease detection can facilitate the control of diseases through proper management strategies such as vector control through pesticide applications, fungicide applications, and disease-specific chemical applications; and can improve productivity.The present review recognizes the need for developing a rapid, cost-effective, and reliable health-monitoring sensor that would facilitate advancements in agriculture. It describes the currently used technologies that can be used for developing a ground-based sensor system to assist in monitoring health and diseases in plants under field conditions. These technologies include spectroscopic and imaging-based, and volatile profiling-based plant disease detection methods. The paper compares the benefits and limitations of these potential methods.Computers and Electronics in Agriculture. 01/2010;
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ABSTRACT: 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.01/2010;
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ABSTRACT: Analysis of aboveground biomass and carbon stocks (as equivalent CO2) was performed in the Castilla y León region, Spain. Data from the second and third Spanish Forest Inventories (1996 and 2006) were used. Total aboveground biomass was calculated using allometric biomass equations and biomass expansion factors (BEF), the first method giving higher values. Forests of Castilla y León stored 77,051,308 Mg of biomass, with a mean of 8.18 Mg ha−1, in 1996 and 135,531,737 Mg of biomass, with a mean of 14.4 Mg ha−1, in 2006. The total equivalent CO2 in this region’s forests increased 9,608,824 Mg year−1 between 1996 and 2006. In relation to the Kyoto Protocol, the Castilla y León forests have sequestered 3 million tons of CO2 per year, which represents 6.4% of the total regional emission of CO2. A Geographic Information System (GIS) based method was also used to assess the geographic distribution of residual forest biomass for bio-energy in the region. The forest statistics data on area of each species were used. The fraction of vegetation cover, land slope and protected areas were also considered. The residual forest biomass in Castilla y León was 1,464,991 Mg year−1, or 1.90% of the total aboveground biomass in 1996. The residual forest biomass was concentrated in specific zones of the Castilla y León region, suitable for the location of industries that utilize biomass as energy source. The energy potential of the residual forest biomass in the Castilla y León region is 7350 million MJ per year.Biomass & Bioenergy - BIOMASS BIOENERG. 01/2011; 35(1):243-252.