Segmentation-based Quantification of Tuta absoluta’s Damage on Tomato Plants
Published in Smart Agricultural Technology - Elsevier, 2024
Recommended citation: Loyani, L., (2024). Segmentation-based Quantification of Tuta absoluta’s Damage on Tomato Plants. Smart Agricultural Technology, 7, 100415 https://doi.org/10.1016/J.ATECH.2024.100415
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.
Recommended citation: Loyani, L., (2024). Segmentation-based Quantification of Tuta absoluta’s Damage on Tomato Plants. Smart Agricultural Technology, 7, 100415. https://doi.org/10.1016/J.ATECH.2024.100415