Whether color, shape and texture of leaves are the key features for image processing based plant recognition? An analysis!

Thanikkal, Jibi G.; Dubey, Ashwani Kumar; Thomas, M. T.

doi:10.1109/rdcape.2017.8358305

Cited by 15 publications

(3 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Leaf tips and leaf margin were key factors considered in this study [24,25]. The authors also explored the use of context-based shape descriptors, including the leaf margin value and the spatial relationship between the leaf and its margin.…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Methodology for Plant Species Recognition Using Morphology of Leaves

Barhate,

Pathak,

Kumar Dubey

2023

AEJ

View full text Add to dashboard Cite

Plants play a crucial role in supporting all forms of life on Earth, not just humans but every living organism. Understanding the diverse range of plant species that surround us is essential due to their significance in various aspects of human life, including agriculture, the environment, medicine, cosmetics, and more. Advancements in machine learning and computer vision algorithms have opened possibilities for identifying different types of plant species, both within and across classes. Plant species detection typically involves several steps, such as image acquisition, feature extraction, categorization, and pre-processing. In this study, three datasets—namely Flavia, Swedish, and the intelligent computing laboratory (ICL) dataset—were chosen for experimentation purposes. For feature extraction, three different models were employed: k-nearest neighbour (KNN), naive Bayes (NB), and the visual geometry group (VGG)-16 model. These models were used to extract distinctive features such as shape, texture, venation, and margin from the plant images. A multiclass classification task was conducted to categorize the plant species. Among the models tested, the VGG-16 model consistently demonstrated superior performance in terms of accuracy. Specifically, when using the VGG-16 model, the obtained accuracies were 96.68% for the Flavia dataset, 97.65% for the Swedish dataset, and 96.11% for the ICL dataset.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Methodology for Plant Species Recognition Using Morphology of Leaves

Barhate,

Pathak,

Kumar Dubey

2023

AEJ

View full text Add to dashboard Cite

show abstract

“…One of the most significant and most studied attributes of a crop is the texture of its leaves [6] as several studies have proven its capability to determine the life status (i.e., diseases) of different plant species [7]- [10]. Texture, among other features: shape and color, perform better by 5.2% and 6%, respectively, with an identification rate of 92% for image processing recognition [11]. Thus this was utilized for building the dataset as extracted through image processing.…”

Section: Introductionmentioning

confidence: 99%

Lettuce life stage classification from texture attributes using machine learning estimators and feature selection processes

Lauguico

Concepcion

Alejandrino

et al. 2020

Int. J. Adv. Intell. Informatics

View full text Add to dashboard Cite

Classification of lettuce life or growth stages is an effective tool for measuring the performance of an aquaponics system. It determines the balance in water nutrients, adequate temperature and lighting, other environmental factors, and the system’s productivity to sustain cultivars. This paper proposes a classification of lettuce life stages planted in an aquaponics system. The classification was done using the texture features of the leaves derived from machine vision algorithms. The attributes underwent three different feature selection processes, namely: Univariate Selection (US), Recursive Feature Elimination (RFE), and Feature Importance (FI) to determine the four most significant features from the original eight attributes. The features selected were used for training four estimators from Decision Trees Classifier (DTC), Gaussian Naïve Bayes (GNB), Stochastic Gradient Descent (SGD), and Linear Discriminant Analysis (LDA). The models trained using DTC and SGD were then optimized as they have hyperparameters for tuning. A comparative analysis among Machine Learning (ML) algorithms was conducted to identify the best-performing model with the given application. The best features were derived from US and FI as they have the same top four features using the DTC estimator optimized with the hyperparameters tuned to max depth having 5, criterion equated to ‘Gini', and splitter was set to 'Best'. The accuracy obtained from cross-validation evaluation resulted in 87.92%. Considering consistency with hold-out validation, LDA outperforms optimized DTC even with lower accuracy of 86.67%. This accuracy of LDA outperformed DTC due to its sufficient fit for generalizing the testing data on classifying lettuce growth stage.

show abstract

“…Combination features showed improvement in leaf recognition. In Reference [24], more leaf features used in a recognition system resulted in better accuracy for a leaf recognition system using a computer vision.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Grading Method for Gambier Leaves Based on Combination of Area, Perimeter, and Image Intensity Using Backpropagation Artificial Neural Network

et al. 2019

View full text Add to dashboard Cite

Gambier leaves are widely used in cosmetics, beverages, and medicine. Tarantang village in West Sumatera, Indonesia, is famous for its gambier commodity. Farmers usually classify gambier leaves by area and color. They inherit this ability through generations. This research creates a tool to imitate the skill of the farmers to classify gambier leaves. The tool is a box covered from outside light. Two LEDs are attached inside the box to get maintain light intensity. A camera is used to capture the leaf image and a raspberry Pi processes the leaf features. A mini monitor is provided to operate the system. Six hundred and twenty-five gambier leaves were classified into five grades. Leaves categorized into grades 1, 2, and 3 are forbidden to be picked. Grade 4 leaves are allowed to be picked and those in grade 5 are the recommended ones for picking. Leaf features are area, perimeter, and intensity of leaf image. Three artificial neural networks are developed based on each feature. One thousand leaf images were used for training and 500 leaf images were used for testing. The accuracies of the features are about 93%, 96% and 97% for area, perimeter and intensity, respectively. A combination of rules are introduced into the system based on the feature accuracy. Those rules can give 100% accuracy compared to the farmer’s recommendation. A real time application to classify the leaves could provide classification with the same decision result as the classifying performed by the farmers.

show abstract

Whether color, shape and texture of leaves are the key features for image processing based plant recognition? An analysis!

Cited by 15 publications

References 42 publications

A Deep Learning Methodology for Plant Species Recognition Using Morphology of Leaves

A Deep Learning Methodology for Plant Species Recognition Using Morphology of Leaves

Lettuce life stage classification from texture attributes using machine learning estimators and feature selection processes

Implementation of Grading Method for Gambier Leaves Based on Combination of Area, Perimeter, and Image Intensity Using Backpropagation Artificial Neural Network

Contact Info

Product

Resources

About