Determination of the appropriate illumination wavelength for accurate and early detection of poplar tree leaf spot disease by using image processing technique


  • Shahryar Sedighi M.SC Student, Department of Biosystems Engineering, Faculty of Agricultural Engineering, Sari Agricultural Sciences and Natural Resources University (SANRU), Iran
  • Davood Kalantari Dep. of Mechanics of Biosystems Engineering
  • Saeid Shiukhy Department of water engineering, Sari Agricultural Sciences and Natural Resources University (SANRU), Iran
  • Jozef Rédl c Department of Machine Design, Faculty of Engineering, SAU in Nitra, Slovak Republic


Image Processing, Machine Vision, Poplar Tree, Wavelength


Leaf spot disease is one of the most common fungal diseases that cause immense and sometimes irreparable damage to poplar trees. Therefore, in order to prevent the development of this fungal disease and to reduce its losses, identification and elimination of its pathogen (Septoria fungi) is very important. In this regard, conventional methods for detection of fungal contamination are time-consuming, costly and difficult. Therefore in this study, in order to distinguish healthy leaves from infected ones, as well as determining the rate of infection progress, a modified image processing algorithm by using the Laplacian threshold was used. Based on the results obtained in this study, the effect of illumination wavelength on the percentage of disease detections was statistically significant (P ≤ 0.05). According to the results, the red spectrum with 680nm wavelength showed the highest contaminated surface on the leaf (190500μm2). In contrast, the yellow spectrum with a wavelength of 585nm determined the lowest amount of contaminated surface (65781μm2). The blue and green spectra showed roughly the same performance in early detection of fungal contamination. Overall obtained results showed that the red spectrum with wavelength of 680 nm is more reliable for early poplar leaves’ surface contamination detection in compare to blue (470 nm), green (550 nm) and yellow (585 nm) wavelengths with 13, 14 and 55.8% improvement, respectively. The method presented in this study can be used to identify the quality and the health of biological products and disease progression, significantly easier and faster in compare to the conventional methods.






III-Equipment Engineering for Plant Production