A Multispectral Dataset for the Detection of Tuta Absoluta and Leveillula Taurica in Tomato Plants

Abstract

Tomato (Solanum lycopersicum) is one of the most important vegetables for human nutrition and its cultivation employs amounts of resources worldwide. However, tomato cultivation is plagued by several diseases and pests that increase production cost and introduce additional environmental and health risks due to pesticide use. Timely disease and pest detection is of high importance for tomato crop output and the environment, since plant protection input can be optimized. Here, we present a dataset of multispectral images (RGB and NIR) of tomato plants, at various stages of infection with Tuta absoluta and Leveillula taurica, which to our knowledge is unique. The dataset comprised of 263 images collected from a real greenhouse. Additionally, we applied a baseline Faster-RCNN object detector for the localization and classification lesions. Our experiments include (i) a version for the RGB channels and (ii) a custom backbone architecture version for feature fusion using the same Faster-RCNN head. Lastly, based on the detector’s output, we compute an >0.9 F1-score on binary classification, while mAP 18.5% and mAP 20.2% on detection, highlight the added value of NIR spectral bands for detecting these diseases.

Keywords

• Plant disease dataset
• Tuta absoluta
• Leveillula taurica
• Leaf miner
• Powdery mildew
• Faster-RCNN