Seed-setting in potatoes as affected by spraying with a-naphthaleneacetamide and by light
SummarySpraying potato plants with dilute concentrations of a-naphthal-eneacetamide was not effective in increasing flower or seed ball production in the greenhouse at Beltsville Maryland, or Greeley, Colorado. Higher concentrations reduced flower production at Presque Isle, Maine.Sebago and Irish Cobbler produced more seed balls at Greeley than at Beltsville. Less cloudy weather at Greeley during the winter months is probably the chief factor responsible for this result.S 245–25, when self-pollinated, produced as many seed balls at Beltsville as at Greeley. It appears that this variety flowers and sets seed more readily under fairly unfavorable light conditions than either Sebago or Irish Cobbler.
Heterogeneous photocatalytic degradation of pesticides using decatungstate intercalated macroporous layered double hydroxides
Decatungstate W10O324− was efficiently intercalated between the layers of three-dimensionally ordered macroporous Mg2Al-layered double hydroxide. The structural and textural properties of as-prepared intercalated compound were characterized using different solid-state characterization techniques such as X-ray powder diffraction, FTIR and Raman spectroscopies and electronic microscopy. The photocatalytic properties of immobilized W10O324− within Mg2Al structure were investigated using 2-(1-naphthyl) acetamide (NAD) as a model of pesticide. The influence of different parameters such as amount of catalyst, pH and oxygen concentration were investigated. An optimal NAD degradation was obtained for a photocatalyst concentration of 60 mg l−1. Under our experimental conditions, this heterogeneous photocatalyst induces photodegradation of 60 % of NAD after 17 h of irradiation at 365 nm and at pH 6.6. Interestingly, pesticide photodegradation leads to the mineralization of substrates to H2O and CO2 and the photocatalyst can be recycled and reused without any loss of activity over four cycles.
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.