Martinez, F, Romaine, JB, Johnson, P, Cardona, A and Millan, P (2025) Novel Fusion Technique for High-Performance Automated Crop edge Detection in Smart Agriculture. IEEE Access, 13.

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Abstract

Optimising vegetable production systems is crucial for maintaining and enhancing agricultural productivity, particularly for crops like lettuce. Separating the crop from the background poses a significant challenge when using automated tools. To address this, a novel technique has been developed to automatically detect the vegetative area of lettuces, optimising time and eliminating subjectivity during crop inspections. The proposed deep learning model integrates the YOLOv10 object detector, the K-means classifier, and a segmentation method known as superpixel. This combination enables lettuce area identification using bounding box labels instead of contour labels during training, improving efficiency compared to other methods like YOLOv8 and Detectron2. Additionally, the combination of the YKMS method with YOLOv8 (YKMSV8) is evaluated, where YKMS serves as a label assistant. These methods are also used as benchmarks to compare the proposed approach. For the training of each methods, a custom database has been created using a low-cost, low-power custom IoT node deployed on a real farm to provide the most accurate data. Throughout the comparison, a custom metric is used to evaluate performance both in training and inference, balancing computational cost and area error, making it applicable in agriculture. Performance metric is associated with computational cost factor and accuracy factor whose value are respectively 65% and 35%, ensuring applicability for autonomous agricultural devices. Computational cost is prioritised to maintain battery life during extended campaigns. The custom metric results during inference were YOLOv8: 81.9%, Detectron2: 84%, YKMS: 70.26%, and YKMSV8: 87.3%. Mean error values were YOLOv8: 3.3%, Detectron2: 3.9%, YKMS: 5.2%, and YKMSV8: 5.2%, while mean time values were YOLOv8: 1.01 s, Detectron2: 4.12 s, YKMS: 4.07 s, and YKMSV8: 0.46 s.

Item Type:	Article
Uncontrolled Keywords:	08 Information and Computing Sciences; 09 Engineering; 10 Technology
Subjects:	S Agriculture > S Agriculture (General) T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Engineering
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
SWORD Depositor:	A Symplectic
Date Deposited:	31 Jan 2025 12:30
Last Modified:	14 Feb 2025 14:15
DOI or ID number:	10.1109/access.2025.3536701
URI:	https://researchonline.ljmu.ac.uk/id/eprint/25519

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