Machine learning and handcrafted image processing methods for classifying common weeds in corn field

Pathak, Harsh; Igathinathane, C.; Howatt, Kirk; Zhang, Zhao

doi:10.1016/j.atech.2023.100249

Cited by 9 publications

(5 citation statements)

References 39 publications

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“…The input image must be a clear image of the pollinator. This is the most critical factor to consider since if a blurry and unclear image is used as an input image, the algorithm will have difficulty extracting pollinator features as reported by [18]. Some limitations also existed from this initial study since this current work only relying on the dataset that have been collected manually by the researchers.…”

Section: Discussionmentioning

confidence: 99%

“…Three examples of the advanced non-parametric machine learning models are k-NN, SVM, and random forest [17]. Authors in [18] recommended that the handcrafted simple image processing algorithm should be tried first, due to its simplicity, for the image classification before resorting to advanced and complex versatile machine learning modelling approaches.…”

Section: Introductionmentioning

confidence: 99%

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Non-Parametric Machine Learning for Pollinator Image Classification: A Comparative Study

Nurul Amelina Nasharuddin,

Nurul Shuhada Zamri

2023

ARASET

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Pollinators play a crucial role in maintaining the health of our planet's ecosystems by aiding in plant reproduction. However, identifying and differentiating between different types of pollinators can be a difficult task, especially when they have similar appearances. This difficulty in identification can cause significant problems for conservation efforts, as effective conservation requires knowledge of the specific pollinator species present in an ecosystem. Thus, the aim of this study is to identify the most effective methods, features, and classifiers for developing a reliable pollinator classifier. Specifically, this initial study uses two primary features to differentiate between the pollinator types: shape and colour. To develop the pollinator classifiers, a dataset of 186 images of black ants, ladybirds, and yellow jacket wasps was collected. The dataset was then divided into training and testing sets, and four different non-parametric classifiers were used to train the extracted features. The classifiers used were the k-Nearest Neighbour, Decision Tree, Random Forest, and Support Vector Machine classifiers. The results showed that the Random Forest classifier was the most accurate, with a maximum accuracy of 92.11% when the dataset was partitioned into 80% training and 20% testing sets. By developing a reliable pollinator classifier, researchers and conservationists can better understand the roles of different pollinator species in maintaining ecosystem health. This understanding can lead to better conservation strategies to protect these important creatures, ultimately helping to preserve our planet's biodiversity.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Non-Parametric Machine Learning for Pollinator Image Classification: A Comparative Study

Nurul Amelina Nasharuddin,

Nurul Shuhada Zamri

2023

ARASET

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“…Several examples of applications of machine learning in precision agriculture [51] are reported, i.e., soil properties detection [52][53][54], crop yield predictions [55][56][57][58][59], disease [60][61][62][63] and weed detection [64][65][66], site-specific irrigation [67][68][69], and livestock production and management [70][71][72]. One of the most in-depth topics is the analysis of plant health with hyperspectral data [73].…”

Section: Advantages Disadvantagesmentioning

confidence: 99%

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

Centorame,

Gasperini,

Ilari

et al. 2024

Agronomy

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Machine learning is a widespread technology that plays a crucial role in digitalisation and aims to explore rules and patterns in large datasets to autonomously solve non-linear problems, taking advantage of multiple source data. Due to its versatility, machine learning can be applied to agriculture. Better crop management, plant health assessment, and early disease detection are some of the main challenges facing the agricultural sector. Plant phenotyping can play a key role in addressing these challenges, especially when combined with machine learning techniques. Therefore, this study reviews available scientific literature on the applications of machine learning algorithms in plant phenotyping with a specific focus on sunflowers. The most common algorithms in the agricultural field are described to emphasise possible uses. Subsequently, the overview highlights machine learning application on phenotyping in three primaries areas: crop management (i.e., yield prediction, biomass estimation, and growth stage monitoring), plant health (i.e., nutritional status and water stress), and disease detection. Finally, we focus on the adoption of machine learning techniques in sunflower phenotyping. The role of machine learning in plant phenotyping has been thoroughly investigated. Artificial neural networks and stacked models seems to be the best way to analyse data.

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“…Image processing has been widely employed by researchers in various fields, including Environmental Science [14], medical [15], agricultural [16,17] and transportation [18] domains. Previous studies related to eggs have also utilized image processing techniques [19,20].…”

Section: Introductionmentioning

confidence: 99%

Development of Classification Method for Determining Chicken Egg Quality Using GLCM-CNN Method

Setiaji,

Adi,

Surarso

2024

ISI

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Eggs are widely consumed due to their high content of vitamin B12, choline and iron. External factors can be detected by observing the eggshell or taken into consideration. The nutrition in eggs is influenced by egg quality, which can be directly observed from the shell. If the shell is cracked, it can be inferred that the egg has poor quality because Salmonella bacteria are dangerous pathogens that can enter the egg. The current issue lies in the complexity and inefficiency of individually classifying eggs by workers, as it is a complicated, time-consuming, frustrating, and inefficient task. Therefore, it is important to separate them automatically. The selection of cracked and intact eggs in this research is an innovative approach to classification using a highly accurate machine learning method. The application of the GLCM-CNN method is an innovative strategy employed for selecting and classifying cracked eggs, as outlined in this research. VGG 19, one of the computational methods, is utilized as a comparative method alongside RESNET 50 and VGG 16. The GLCM-CNN algorithm in this research employed 1,000 images for each class, with a validation set of 20% for each class, resulting in an accuracy of 98%. The inefficient classification process and complexity of automated egg quality classification can be significantly addressed through the findings presented in this research.

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Machine learning and handcrafted image processing methods for classifying common weeds in corn field

Cited by 9 publications

References 39 publications

Non-Parametric Machine Learning for Pollinator Image Classification: A Comparative Study

Non-Parametric Machine Learning for Pollinator Image Classification: A Comparative Study

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

Development of Classification Method for Determining Chicken Egg Quality Using GLCM-CNN Method

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