Automatic detection of rust disease of Lentil by machine learning system using microscopic images

<p>Accurate and early detection of plant diseases will facilitate mitigate the worldwide losses experienced by the agriculture area. MATLAB image processing provides quick and non-destructive means of rust disease detection. In this paper, microscopic image data of rust disease of <em>Lentil</em> was combined with image processing with depth information and developed a machine learning system to detect rust disease at early stage infected with fungus <em>Uromyces fabae</em> (Pers) de Bary. A novel feature set was extracted from the image data using local binary pattern (LBP) and HBBP (Brightness Bi-Histogram Equalization) for image enhancement. It was observed that by combining these, the accuracy of detection of the diseased plants at microscopic level was significantly improved. In addition, we showed that our novel feature set was capable of identifying rust disease at haustorium stage without spreading of disease. </p>

Trichothecium roseum Link ex Fr. on Uromyces fabae (Pers.) de Bary

Mycelium of <i>Trichothecium roseum</i> Link ex Fr. was observed in Poland on the surface of separate soruses of <i>Uromyces fabae</i> (Pers.) de Bary on the leaves of <i>Vicia faba</i> L. and <i>V. faba</i> L. var. <i>minor</i>. The sourses of <i>Uromyces fabae</i> were covered by the, fruiting mycelium of <i>Trichothecium roseum</i>, which, probably, inhibited the development of its uredospores. Therefore, biological control of <i>U. fabae</i> with the use of <i>Trichothecium roseum</i> seems feasible.

Fusarium graminearum Schw. pasożyt wtórny na Uromyces fabae (Pers.) de Bary - Fusarium graminearum Schw. secondary parasite of Uromyces fabae (Pers.) de Bary

The work presents the description of <i>Fusarium praminearum</i> Schw. seconday parasite of <i>Uromyces fabae</i> (Pers.) de Bary occurring on the leves of <i>Vicia faba</i> L.