Real Time Weed Detection using a Boosted Cascade of Simple Features

Tannouche, Adil; Sbai, K.; Rahmoune, Miloud; Agounoun, Rachid; Rahmani, Abdelali

doi:10.11591/ijece.v6i6.pp2755-2765

Cited by 6 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several studies have yielded noteworthy research results in the realm of intra-row weeding. Tannouche et al (2016) [109] successfully implemented real-time weed detection through computer vision and designed robotic control strategies, leading to enhanced weed removal precision and reduced herbicide usage. In summary, various innovative technologies and techniques are being employed to advance weed detection and control in precision agriculture.…”

Section: Navigation and Localization Techniquesmentioning

confidence: 99%

Advancements in precision control in intra-row weeding: A comprehensive review

Lahre,

Satpathy

2024

Int. J. Res. Agron.

View full text Add to dashboard Cite

Sensor-based weed detection is a significant breakthrough in precise weed management that reduces the need for manual work and herbicides. GPS systems have shown promising results, with a single pass covering 30-49% of the intra-row area. Additionally, using an RGB sensor and a laser sensor together can identify the center of a plant, regardless of lighting conditions. Robots have demonstrated the ability to distinguish 99.7% of crop plants in dense outdoor areas with high weed density. These advancements offer opportunities for more effective and environmentally-friendly weed management in different farming systems.

show abstract

Section: Navigation and Localization Techniquesmentioning

confidence: 99%

Advancements in precision control in intra-row weeding: A comprehensive review

Lahre,

Satpathy

2024

Int. J. Res. Agron.

View full text Add to dashboard Cite

show abstract

“…Figure 1. Real-time spraying system concept Tilted images: These images are derived from video sequences from several cultures for real-time processing [1]. For this current study, each image is first cropped and masked to keep only the region of interest (ROI) of about 3x2 m 2 in front of the moving tractor.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…In precision agriculture and during the last decade, several technologies have been developed to detect weeds and achieve localized and selective spraying. In our previous work, we have combined the Haar-like features with the AdaBoost algorithm to achieve the real time weeds detection in the inter-row of different crops [1]. Also, we have developed a new adjacency descriptor for the selection of weeds (monocot or dicot) to achieve the real time selective spraying [2].…”

Section: Introductionmentioning

confidence: 99%

Weeds detection efficiency through different convolutional neural networks technology

Tannouche

Gaga

Boutalline

et al. 2022

IJECE

Self Cite

View full text Add to dashboard Cite

The preservation of the environment has become a priority and a subject that is receiving more and more attention. This is particularly important in the field of precision agriculture, where pesticide and herbicide use has become more controlled. In this study, we propose to evaluate the ability of the deep learning (DL) and convolutional neural network (CNNs) technology to detect weeds in several types of crops using a perspective and proximity images to enable localized and ultra-localized herbicide spraying in the region of Beni Mellal in Morocco. We studied the detection of weeds through six recent CNN known for their speed and precision, namely, VGGNet (16 and 19), GoogLeNet (Inception V3 and V4) and MobileNet (V1 and V2). The first experiment was performed with the CNNs architectures from scratch and the second experiment with their pre-trained versions. The results showed that Inception V4 achieved the highest precision with a rate of 99.41% and 99.51% on the mixed image sets and for its version from scratch and its pre-trained version respectively, and that MobileNet V2 was the fastest and lightest with its size of 14 MB.

show abstract

“…The works that have been realized in our research team [10,11] propose a method based on computer vision and deep learning [12] to make the classification between four types of poultry, with a very high accuracy by retraining the Mobile Net V2 model [13], is a highly efficient deep learning model designed for mobile and embedded vision applications. It uses depthwise separable convolutions and inverted residuals to achieve high accuracy while maintaining low computational complexity and small model size.…”

Section: Introductionmentioning

confidence: 99%

Poultry Meat Classification Using MobileNetV2 Pretrained Model

Salma¹,

Habib²,

Tannouche³

et al. 2023

RIA

View full text Add to dashboard Cite

In Morocco the meat business risks being targeted by fraud and adulteration, leading customers to probe the authenticity of the meat. The traditional styles for verifying meat types are expensive and consuming time. In this work, we propose a method based on computer vision and deep learning, which allows the bracket and isolation between turkey and chicken and Fayoumi and chicken farmer meat. We created a model grounded on the pre-trained Mobile Net V2 model and trained it with a Dataset containing the collected images of the four poultries. The evaluation of this model has given satisfactory results and has demonstrated that the model is suitable to predict the meat class with a delicacy of over 98%. The algorithm can be generalized to separate between authentic and fake meat.

show abstract

Real Time Weed Detection using a Boosted Cascade of Simple Features

Cited by 6 publications

References 15 publications

Advancements in precision control in intra-row weeding: A comprehensive review

Advancements in precision control in intra-row weeding: A comprehensive review

Weeds detection efficiency through different convolutional neural networks technology

Poultry Meat Classification Using MobileNetV2 Pretrained Model

Contact Info

Product

Resources

About