Segmentation-based quantification of Tuta absoluta’s damage on tomato plants

Abstract

The invasion of the tomato leaf miner (Tuta absoluta) poses a significant threat to tomato productivity, leading to substantial yield losses for farmers. Currently, there is a lack of reliable methods for quantifying the effects of Tuta absoluta at an early stage before it causes significant damage. This research proposes a deep Convolutional Neural Network (CNN) model for the segmentation-based quantification of Tuta absoluta on tomato plants. The proposed quantification method employed a Mask RCNN model that achieved a mAP of 85.67 % and precisely detected and segmented the shapes of Tuta absoluta-infected areas on tomato leaves. The ability to accurately detect, segment and count Tuta mines in a tomato leaf image can have a significant impact on the agricultural industry by enabling farmers to quickly assess the extent of damage to their crops and take appropriate measures to prevent further losses.