Possibilities of cucumber powdery mildew detection by visible and near-infrared spectroscopy

Received: July 16th, 2021 ; Accepted: November 6th, 2021 ; Published: February 1st, 2022 ; Correspondence: [email protected] are one of the most demanded and widely grown greenhouse vegetables. Important factors that influence quality and quantity of yield are diseases. Powdery mildew (caused by Podosphaera xanthii and/or Golovinomyces cichoracearum), is one of the most harmful cucumber diseases. Early detection of mildew via non-destructive methods can optimize schemes of fungicide application. The study aimed to find regularities in the reflected light spectra, indices described in the literature, and severity of mildew. Plants were grown in the polycarbonate greenhouse under artificial lighting in a 16 h photoperiod with PAR at the tips of plants 200 ± 30 µmol m-2 s-1. Leaf reflection spectra were obtained using spectroradiometer RS-3500 (Ltd. Spectral Evolution). Spectral range 350–2,500 nm, bandwidth 1 nm. The severity of cucumber mildew was evaluated using 10 point scale (0- no symptoms, … 9 - the plant is dead). The vegetation indices found in the literature have been calculated. The obtained results show that the calculated indices have different sensitivities. The strongest correlation between the degree of cucumbers infection with powdery mildew and the light reflectance spectrum was found in the green range of visible light around 550 nm. Disease-Water Stress Index-2 (DSWI-2), Structure Intensive Pigment Index (SIPI), and Normalized Difference Vegetation Index (NDVI) are the most suitable indices for determining powdery mildew in cucumbers. New indices for detection of powdery mildew have been created. None of the studied indices allows determining the powdery mildew at the early stages of disease development when powdery mildew severity is below 10%

Modelling fuzzy combination of remote sensing vegetation index for durum wheat crop analysis

The application of new technologies (e.g. Internet of Things, mechatronics, remote sensing) to the primary sector will reduce the production costs, limit the waste of primary materials, and reduce the release of polluting compounds into the environment. Precision agriculture (PA) has been growing in the last years thanks to industry efforts and development of applications for diagnostic purposes. Many applications in PA use vegetation indices to measure phenology parameters in terms of Leaf Area Index (LAI). In this context, the correlation of some vegetation indices were analyzed with respect to the durum wheat canopy, evaluating two different phenological stages (elongation and maturity). The results show that for the first stage of growth, the Enhanced Vegetation Index (EVI) was the best-correlated vegetation index with LAI, while the Land Surface Water Index (LSWI) was more reliable for the following stage of growth. Considering trials findings, a fuzzy expert system was developed to combine EVI and LSWI, obtaining a new combined index (Case-specific Fuzzy Vegetation Index) that better represents the LAI in comparison with the single indices. Thus, this approach could give place to a better representative vegetation index of a different biological condition of the plant. It may also serve as a reliable method for wheat yield forecasting and stress monitoring