Real Time Deep Learning Algorithm for Counting Weed’s Growth Stages

Almalky, Abeer M.; Ahmed, Khaled R.

doi:10.1109/isads56919.2023.10092053

2023 IEEE 15th International Symposium on Autonomous Decentralized System (ISADS) 2023

DOI: 10.1109/isads56919.2023.10092053

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Real Time Deep Learning Algorithm for Counting Weed’s Growth Stages

Abeer M. Almalky

Khaled R. Ahmed

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Cited by 2 publications

References 26 publications

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Automatic Lettuce Weed Detection and Classification Based on Optimized Convolutional Neural Networks for Robotic Weed Control

Zhao,

Wang,

et al. 2024

Agronomy

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Weed management plays a crucial role in the growth and yield of lettuce, with timely and effective weed control significantly enhancing production. However, the increasing labor costs and the detrimental environmental impact of chemical herbicides have posed serious challenges to the development of lettuce farming. Mechanical weeding has emerged as an effective solution to address these issues. In precision agriculture, the prerequisite for autonomous weeding is the accurate identification, classification, and localization of lettuce and weeds. This study used an intelligent mechanical intra-row lettuce-weeding system based on a vision system, integrating the newly proposed LettWd-YOLOv8l model for lettuce–weed recognition and lettuce localization. The proposed LettWd-YOLOv8l model was compared with other YOLOv8 series and YOLOv10 series models in terms of performance, and the experimental results demonstrated its superior performance in precision, recall, F1-score, mAP50, and mAP95, achieving 99.732%, 99.907%, 99.500%, 99.500%, and 98.995%, respectively. Additionally, the mechanical component of the autonomous intra-row lettuce-weeding system, consisting of an oscillating pneumatic mechanism, effectively performs intra-row weeding. The system successfully completed lettuce localization tasks with an accuracy of 89.273% at a speed of 3.28 km/h and achieved a weeding rate of 83.729% for intra-row weed removal. This integration of LettWd-YOLOv8l and a robust mechanical system ensures efficient and precise weed control in lettuce cultivation.

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Automatic Lettuce Weed Detection and Classification Based on Optimized Convolutional Neural Networks for Robotic Weed Control

Zhao,

Wang,

et al. 2024

Agronomy

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Deep Learning Model for Classifying and Evaluating Soybean Leaf Disease Damage

Goshika,

Meksem,

Ahmed

et al. 2023

IJMS

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Soybean (Glycine max (L.) Merr.) is a major source of oil and protein for human food and animal feed; however, soybean crops face diverse factors causing damage, including pathogen infections, environmental shifts, poor fertilization, and incorrect pesticide use, leading to reduced yields. Identifying the level of leaf damage aids yield projections, pesticide, and fertilizer decisions. Deep learning models (DLMs) and neural networks mastering tasks from abundant data have been used for binary healthy/unhealthy leaf classification. However, no DLM predicts and categorizes soybean leaf damage severity (five levels) for tailored pesticide use and yield forecasts. This paper introduces a novel DLM for accurate damage prediction and classification, trained on 2930 near-field soybean leaf images. The model quantifies damage severity, distinguishing healthy/unhealthy leaves and offering a comprehensive solution. Performance metrics include accuracy, precision, recall, and F1-score. This research presents a robust DLM for soybean damage assessment, supporting informed agricultural decisions based on specific damage levels and enhancing crop management and productivity.

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Real Time Deep Learning Algorithm for Counting Weed’s Growth Stages

Cited by 2 publications

References 26 publications

Automatic Lettuce Weed Detection and Classification Based on Optimized Convolutional Neural Networks for Robotic Weed Control

Automatic Lettuce Weed Detection and Classification Based on Optimized Convolutional Neural Networks for Robotic Weed Control

Deep Learning Model for Classifying and Evaluating Soybean Leaf Disease Damage

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