Detection of effects of three different plant pests for chrysanthemum flowers by using image processing and spectral imaging

Abstract

Ornamental floriculture is one of the main economic activities in different regions of Colombia. Different flowers have a great success, but its industry faces some challenges on phytosanitary controls caused mainly by the dependence on human monitors and the expertise in the detection of diseases throughout the crop fields. This paper focuses on the detection of the affection patterns caused by tomato spotted wilt virus, puncture leaf miner, and leaf miner larvae on chrysanthemum flowers (Dendrathema grandiflorum). A spectral imaging system was with 11 spectral channels was implemented which were generated by the same number of light emitting diodes groups. By using these images, there were carried out different image processing techniques and the combination of different linear relations among the spectral images were tested to enhance, isolate and quantify the affected area on the leaves. The results show that our system has more selective spectral width than common artificial vision systems and the effects of the diseases can be effectively detected.

High Zoom Ratio Foveated Snapshot Hyperspectral Imaging for Fruit Pest Monitoring

“…Multi-and hyperspectral imaging, which refers to the recording of two-dimensionally resolved spectral information, has been rapidly applied in the agricultural food sector [8][9][10][11]. Diferent spectral imaging techniques have been developed in the past few years to meet the individual requirements of each specifc application, including the "snapshot" spectral imaging used in real-time monitoring.…”