Correction to: Weed Detection Approach Using Feature Extraction and KNN Classification

Khurana, Gurpreet; Bawa, Navneet

doi:10.1007/978-981-15-5463-6_93

Cited by 1 publication

(3 citation statements)

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“…The application of KNN in this context involves the use of sensor-based systems to distinguish between crops and weeds [22]. One approach involves the use of textural feature analysis and morphological scanning applied to specific crops, such as sugar beet plants [23]. Following this, the KNN classifier was used to classify and distinguish the weed plant from the field crop [23].…”

Section: Introductionmentioning

confidence: 99%

“…One approach involves the use of textural feature analysis and morphological scanning applied to specific crops, such as sugar beet plants [23]. Following this, the KNN classifier was used to classify and distinguish the weed plant from the field crop [23]. The results of the weed detection were analyzed in terms of accuracy and execution time, demonstrating the effectiveness of the KNN approach [23].…”

Section: Introductionmentioning

confidence: 99%

“…Following this, the KNN classifier was used to classify and distinguish the weed plant from the field crop [23]. The results of the weed detection were analyzed in terms of accuracy and execution time, demonstrating the effectiveness of the KNN approach [23].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development of Mechatronics-based Self-propelled Intra-Row Weeder

Lahre,

Satpathy,

Sukdeve

et al. 2024

J. Exp. Agric. Int.

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Background: This research presents a significant stride in precision agriculture, focusing on the development and field evaluation of a self-propelled intra-row weeder engineered using mechatronics and machine learning. The study was motivated by the need for labor-efficient and environmentally friendly weed control methods, as conventional techniques pose various challenges. Methodology: The intra-row weeder, equipped with a crop detection and avoidance system, was developed using a sensor, servo motor, encoder, weeding tool, and a microprocessor (Arduino Uno). A crop detection and avoidance algorithm, based on the K-nearest neighbor machine learning tool, was developed and trained using a customized feature method. This facilitated the system’s ability to accurately distinguish between plants and crops, a distinction that was programmed based on object height. This approach proved effective under the various conditions. Results: Field performance evaluation of the weeder was conducted at different forward speeds and plant-to-plant spacing. The results revealed strong correlations between operating parameters and responses such as plant damage, weeding efficiency, performance index, and field efficiency, with R² values ranging from 67.87% to 83.61%. Optimal performance was achieved at a forward speed of 2.5 km∙h-1 and plant spacing of 60 cm, yielding a field capacity of 0.041 ha.h-1 and field efficiency of 86.25%. This study, therefore, provides a less labor-intensive solution for weed management in precision agriculture, paving the way for future innovations in the sector.

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Section: Introductionmentioning

confidence: 99%