Development of a weed detection system using machine learning and neural network algorithms

Urmashev, Baydaulet A.; Buribayev, Zholdas; Amirgaliyeva, Zhazira; Ataniyazova, Aisulu; Zhassuzak, M.; Turegali, A.

doi:10.15587/1729-4061.2021.246706

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…On the other side, an integrated approach was developed by [21] which was based on random forest, kNN, decision tree, and YOLOv5 neural network for the detection of different types of weeds. The proposed approach was assessed against a public dataset and achieved an 84% weighted average recognition rate under static circumstances.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Edge Detection Algorithm for Weed Image Analysis

Alhwaiti,

Siddiqi,

Ahmad

2023

CSSE

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Weeds are one of the utmost damaging agricultural annoyers that have a major influence on crops. Weeds have the responsibility to get higher production costs due to the waste of crops and also have a major influence on the worldwide agricultural economy. The significance of such concern got motivation in the research community to explore the usage of technology for the detection of weeds at early stages that support farmers in agricultural fields. Some weed methods have been proposed for these fields; however, these algorithms still have challenges as they were implemented against controlled environments. Therefore, in this paper, a weed image analysis approach has been proposed for the system of weed classification. In this system, for preprocessing, a Homomorphic filter is exploited to diminish the environmental factors. While, for feature extraction, an adaptive feature extraction method is proposed that exploited edge detection. The proposed technique estimates the directions of the edges while accounting for non-maximum suppression. This method has several benefits, including its ease of use and ability to extend to other types of features. Typically, low-level details in the form of features are extracted to identify weeds, and additional techniques for detecting cultured weeds are utilized if necessary. In the processing of weed images, certain edges may be verified as a footstep function, and our technique may outperform other operators such as gradient operators. The relevant details are extracted to generate a feature vector that is further given to a classifier for weed identification. Finally, the features have been used in logistic regression for weed classification. The model was assessed against logistic regression that accurately identified different kinds of weed images in naturalistic domains. The proposed approach attained weighted average recognition of 98.5% against the weed images dataset. Hence, it is assumed that the proposed approach might help in the weed classification system to accurately identify narrow and broad weeds taken captured in real environments.

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Section: Related Workmentioning

confidence: 99%

An Adaptive Edge Detection Algorithm for Weed Image Analysis

Alhwaiti,

Siddiqi,

Ahmad

2023

CSSE

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“…Significant advances have also been made through the publication of several deep-learning and hand-crafted models [11] As a subfield of both ML and AI, deep learning (DL) is making significant progress toward automating precision agriculture [12,13]. DL has made important contributions to many fields of agriculture [14,15], including: disease detection [16], crop plant detection and counting [17], crop row detection [18], crop stress detection [19], fruit detection and freshness grading [20], fruit harvesting [18,21], and site-specific weed control [21].…”

Section: Introductionmentioning

confidence: 99%

“…Hand-crafted methodologies require substantial testing to extract essential features and may also be insufficiently robust in complicated environments and biological variability [20]. CNNs have improved object detection [22].…”

Section: Introductionmentioning

confidence: 99%

A Metaheuristic Harris Hawks Optimization Algorithm for Weed Detection Using Drone Images

Ismail

Ali

2023

Applied Sciences

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There are several major threats to crop production. As herbicide use has become overly reliant on weed control, herbicide-resistant weeds have evolved and pose an increasing threat to the environment, food safety, and human health. Convolutional neural networks (CNNs) have demonstrated exceptional results in the analysis of images for the identification of weeds from crop images that are captured by drones. Manually designing such neural architectures is, however, an error-prone and time-consuming process. Natural-inspired optimization algorithms have been widely used to design and optimize neural networks, since they can perform a blackbox optimization process without explicitly formulating mathematical formulations or providing gradient information to develop appropriate representations and search paradigms for solutions. Harris Hawk Optimization algorithms (HHO) have been developed in recent years to identify optimal or near-optimal solutions to difficult problems automatically, thus overcoming the limitations of human judgment. A new automated architecture based on DenseNet-121 and DenseNet-201 models is presented in this study, which is called “DenseHHO”. A novel CNN architecture design is devised to classify weed images captured by sprayer drones using the Harris Hawk Optimization algorithm (HHO) by selecting the most appropriate parameters. Based on the results of this study, the proposed method is capable of detecting weeds in unstructured field environments with an average accuracy of 98.44% using DenseNet-121 and 97.91% using DenseNet-201, the highest accuracy among optimization-based weed-detection strategies.

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“…to 0.92 for each class [3]. There are different climatic zones in Kazakhstan, so different weed species are found in different regions.…”

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confidence: 99%

Identification of weeds in fields based on computer vision technology

Kaldarova,

Аkanova,

Nazyrova

et al. 2023

EEJET

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The problem of multiple zones in computer vision, including pattern recognition in the agricultural sector, occupies a special place in the field of artificial intelligence in the modern aspect. The object of the study is the recognition of weeds based on deep learning and computer vision. The subject of the study is the effective use of neural network models in training, involving classification and processing using datasets of plants and weeds. The relevance of the study lies in the demand of the modern world in the use of new information technologies in industrial agriculture, which contributes to improving the efficiency of agro-industrial complexes. The interest of private agricultural enterprises and the state is caused by an increase in the yield of agricultural products. To recognize weeds, machine learning methods, in particular neural networks, were used. The process of weed recognition is described using the Mark model, as a result of processing 1,562 pictures, segmented images are obtained. Due to the annual increase in weeds on the territory of Kazakhstan and in the course of solving these problems, a new plant recognition code was developed and written in the scanner software module. The scanner, in turn, provides automatic detection of weeds. Based on the results of a trained neural network based on the MaskRCNN neural network model written in the scanner software module meeting new time standards, the automated plant scanning and recognition system was improved. The weed was recognized in an average of 0.2 seconds with an accuracy of 89 %, while the additional human factor was completely removed. The use of new technology helps to control weeds and contributes to solving the problem of controlling them

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Development of a weed detection system using machine learning and neural network algorithms

Cited by 5 publications

References 23 publications

An Adaptive Edge Detection Algorithm for Weed Image Analysis

An Adaptive Edge Detection Algorithm for Weed Image Analysis

A Metaheuristic Harris Hawks Optimization Algorithm for Weed Detection Using Drone Images

Identification of weeds in fields based on computer vision technology

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