Phosphorus Level Affects Brown Blotch Disease, Development and Yield of Cowpea
Application of Phosphorus in adequate concentrations could directly or indirectly reduce disease incidence and severity of cowpea brown blotch and improve the forage and grain yield. A field experiment was conducted to investigate the effects of level of phosphorus application on Brown Blotch disease of cowpea (Vigna unguiculata (L.) Walp), caused by Colletotrichum capsici during 2003, 2004 and 2005 planting seasons. Application of phosphorus was at 30, 60, 90 and 120 kg ha of P O. As P level increased numbers of petioles, 1 25 pods, nodules, seed/pod, leaf area and yield significantly increased. Disease incidence and severity of Brown Blotch were significantly reduced at 90 and 120 kg ha of P O was not affected irrespective of the method of 1 25 application. Application method did not affect yield. Reduction in brown blotch disease at higher levels of P was recorded.
Rice Production and Nutrient Management in India
The demand for rice in India is projected at 128 million tonnes (M t) for the year 2012 and will require a production level of 3,000 kg/ ha...significantly greater than the present average yield of 1,930 kg/ ha. This low level of productivity can be increased substantially by growing high yielding varieties/hybrids and by increasing both the area under balanced fertilizer use and application rates.
Detection and Counting of On-Tree Citrus Fruit for Crop Yield Estimation
In this paper, we present a technique to estimate citrus fruit yield from the tree images. Manually counting the fruit for yield estimation for marketing and other managerial tasks is time consuming and requires human resources, which do not always come cheap. Different approaches have been used for the said purpose, yet separation of fruit from its background poses challenges, and renders the exercise inaccurate. In this paper, we use k-means segmentation for recognition of fruit, which segments the image accurately thus enabling more accurate yield estimation. We created a dataset containing 83 tree images with 4001 citrus fruits from three different fields. We are able to detect the on-tree fruits with an accuracy of 91.3%. In addition, we find a strong correlation between the manual and the automated fruit count by getting coefficients of determination R2 up to 0.99.