Controlling nematodes in gardens

"Nematodes cause serious damage to gardens in Southeast Missouri. These pests can occur in other Missouri areas but are less common there. Nematodes are a greater problem where there are long, warm growing seasons and lighter, sandier soils."--First page.H.F. DiCarlo (Department of Horticulture), James A. Wrath (Plant Pathology, College of Agriculture)Revised 5/90/8

Cotton seed and seedling diseases and their control

"Seedling diseases cause serious losses to Missouri cotton each year. Failure to get a uniform stand of cotton is due largely to seedling diseases. Cold, wet soils are conducive to most seedling diseases. These conditions occur frequently in Missouri. Injuries caused by these diseases can result in stand losses that necessitate replanting. The U.S. Cotton Disease Council reports that seedling diseases cause average losses of 3 percent nationally. Farmers in southeast Missouri lose 5 percent annually to seedling disease."--First page.James A. Wrather and Einar W. Palm (Department of Plant Pathology College of Agriculture)Revised 2/88/4

Cotton seed and seedling diseases and their control

"Seedling diseases cause serious losses to Missouri cotton each year, Failure to get a uniform stand of cotton is due largely to seedling diseases. Cold, Wet soils are conducive to most seedling diseases. These conditions occur frequently in Missouri. Thus, injuries due to these diseases can result in stand losses that necessitate replanting, which is costly. In 1979, the U.S. Cotton Disease Council reported that seedling diseases are the most serious of all cotton disease problems, causing from 5 to 25 percent annually."--First page.James A. Wrather and Einar W. Palm (Department of Plant Pathology, College of Agriculture)New 1/82/8

Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems

Abstract Background Charcoal rot is a fungal disease that thrives in warm dry conditions and affects the yield of soybeans and other important agronomic crops worldwide. There is a need for robust, automatic and consistent early detection and quantification of disease symptoms which are important in breeding programs for the development of improved cultivars and in crop production for the implementation of disease control measures for yield protection. Current methods of plant disease phenotyping are predominantly visual and hence are slow and prone to human error and variation. There has been increasing interest in hyperspectral imaging applications for early detection of disease symptoms. However, the high dimensionality of hyperspectral data makes it very important to have an efficient analysis pipeline in place for the identification of disease so that effective crop management decisions can be made. The focus of this work is to determine the minimal number of most effective hyperspectral wavebands that can distinguish between healthy and diseased soybean stem specimens early on in the growing season for proper management of the disease. 111 hyperspectral data cubes representing healthy and infected stems were captured at 3, 6, 9, 12, and 15 days after inoculation. We utilized inoculated and control specimens from 4 different genotypes. Each hyperspectral image was captured at 240 different wavelengths in the range of 383–1032 nm. We formulated the identification of best waveband combination from 240 wavebands as an optimization problem. We used a combination of genetic algorithm as an optimizer and support vector machines as a classifier for the identification of maximally-effective waveband combination. Results A binary classification between healthy and infected soybean stem samples using the selected six waveband combination (475.56, 548.91, 652.14, 516.31, 720.05, 915.64 nm) obtained a classification accuracy of 97% for the infected class. Furthermore, we achieved a classification accuracy of 90.91% for test samples from 3 days after inoculation using the selected six waveband combination. Conclusions The results demonstrated that these carefully-chosen wavebands are more informative than RGB images alone and enable early identification of charcoal rot infection in soybean. The selected wavebands could be used in a multispectral camera for remote identification of charcoal rot infection in soybean