Mushroom Classification Research Articles

Mushroom Classification of Toxicity Using Deep Learning

Mushroom Classification of Toxicity Using Deep Learning

Comparative Analysis of CNN Algorithms for Mushroom Classification with Proposed Lightweight CNN Model

The classification of mushroom species presents significant ecologic and health-related challenges; advancement in classification techniques is required to gain reliable identifications. This study aims to explain a methodology that was devised and evaluated in the development of a novel, lightweight Convolutional Neural Network (CNN) designed specifically for the task of mushroom classification. The paper provides a custom CNN model that is computationally cost-effective and capable of high-precision classification, fit for real-time usage. Hence, the proposed model was evaluated on this dataset of curated mushroom images with traditional classifiers and state-of-the-art CNN architectures, such as EfficientNet-B7, ResNet50, InceptionV3, and MobileNetV2. The custom model is depth-wise separations engineered in such a way that while they reduce the computational load, they don't compromise the effectiveness of the model. The custom model achieved a test score of 0.68, which is moderate compared to more established models such as EfficientNet-B7 or ResNet50. This approach helps the model function effectively even on platforms having low computational resources. A comprehensive evaluation reveals that a custom CNN has reasonable accuracy in the identification of different mushroom species vis-à-vis existing models, but also significantly lightens the classification process.

Analysis and Development of Eight Deep Learning Architectures for the Classification of Mushrooms

Analysis and Development of Eight Deep Learning Architectures for the Classification of Mushrooms

Deep Learning Based Approach for Classification of Mushrooms

Deep learning algorithms have produced amazing results in recent years when used to identify items in digital photographs. A deep learning technique is suggested in this work to classify mushrooms in their natural habitat. The study's objective is to identify the most effective method for categorizing mushroom images produced by well-known CNN models. This study will be helpful for the field of pharmacology, mushroom hunters who gather mushrooms in the wild, and it will help to lower the number of people who are at risk of becoming ill from poisonous mushrooms. Images are taken from data labelled by INaturalist specialist. The photographs show mushrooms in their natural environment and feature a variety of backgrounds. The "Mobilenetv2_GAP_flatten_fc" model, which was the study's top performer, had a training data set accuracy of 99.99%. It was 97.20% accurate in the categorization that was done using the validation data. Using the test data set, the classification accuracy was 97.89%. This paper presents the results of a performance comparison between the best-performing model and a multitude of state-of-the-art models that have undergone prior training. Mobilenetv2_GAP_flatten_fc model greatly outperformed the trained models, according to the precision, recall, F1 Score. This illustrates how the basic training process of the suggested model can be applied to enhance feature extraction and learning.

A Special Robot for Precise Grading and Metering of Mushrooms Based on Yolov5

In order to accomplish accurate mushroom classification and measurement, it is necessary to optimize the existing classification algorithm and measurement devices, as well as to design a specific robot to improve classification accuracy and measurement efficiency. In order to achieve the above objectives, a research-level verification of mushroom grading using Yolov5 + OpenCV and a mushroom measuring system using a resistance strain gauge sensor was carried out. In the aspect of mushroom grading, a method based on the OpenCV visual library was used to identify the minimum quadrilateral outside the mushroom contour, allowing the size of the mushroom to be measured. The experiment’s results show that the method can assess target objects that are occluded with each other under different illumination conditions with 96% accuracy. In the measurement of mushrooms, the strain of the resistance is converted into an analog signal, and the weight of different grades of mushrooms is converted according to the linear relationship after processing by the detection circuit module. Through this method, the error range is successfully controlled within ±0.02 kg, which meets the requirements of accurate measurement of mushrooms. The results of field experiments show that the proposed accurate grading and measurement method of Lentinula edodes is effective and feasible, and provides technical support for the intelligent grading and measurement of Lentinula edodes in production units.

Implementation of Deep Learning for Classification of Mushroom Using CNN Algorithm

Mushrooms are a type of low-level plant that lacks chlorophyll. One of the advantages of fungi is that they are commonly utilized as food items in the community. This paper discussed the implementation of CNN for the classification of mushrooms. The project aims to develop a robust system that can automate the labor-intensive task of mushroom classification. The CNN model will be trained on a large dataset of annotated mushroom images, learning to extract meaningful features and patterns for accurate categorization. To evaluate the performance of the developed system, a comprehensive set of metrics, including accuracy, precision, recall, and F1 score, will be used. The dataset will be split into training, validation, and testing sets to assess the model's generalization ability to unseen data. Based on the experimental result, the average accuracy rate in the Agaricus Portobello test was % -99.89 %, % -99.89 % in the Amanita Phalloides test, % -99.59 % in the Cantharellus Cibarius test, % -98.89 % in the Gyromitra Esculenta test, % -99.96 % in the Hygrocybe Conica, and % -99.93 % in the Omphalotus Orealius.

Mushroom Classification

Mushroom Classification

Machine Learning-based Classification of Edible and Poisonous Mushrooms: A Performance Comparison

Abstract: In the disciplines of computer vision and machine learning, classifying mushrooms is a key problem. The objective of this study is to create a machine learning model that can correctly categorise different species of mushrooms as either edible or harmful based on their visual characteristics. The Field Guide to North American Mushrooms of the Audubon Society served as the study's data source, and it includes descriptions of hypothetical samples of 23 species of gilled mushrooms from the Agaricus and Lepiota families. Each sample is categorised as either definitely edible, definitely poisonous. To create our machine learning model for this study, we combined five different algorithms: SVM, logistic regression, decision tree, random forest, and KNN. Metrics such as accuracy, precision, recall, and F1 score were used to assess each algorithm's performance. Our findings show that the random forest algorithm outperformed the other algorithms with a 95% accuracy rate. However, by employing feature selection strategies and hyperparameter tuning, we further improved our model. We were able to raise the accuracy of our model to 97% by choosing the most pertinent features and fine-tuning its hyperparameters. The outcomes of this study should offer a practical method for precisely categorising mushrooms and lowering the danger of mushroom poisoning. This paper proposes an ensemble approach utilizing five different machine learning classifiers to detect mushroom classification. The study analyzes various features, such as cap-shape, gill-colour, stem texture and ring-type & stalk features, and the finding

Wild Mushroom Classification Based on Improved MobileViT Deep Learning

Wild mushrooms are not only tasty but also rich in nutritional value, but it is difficult for non-specialists to distinguish poisonous wild mushrooms accurately. Given the frequent occurrence of wild mushroom poisoning, we propose a new multidimensional feature fusion attention network (M-ViT) combining convolutional networks (ConvNets) and attention networks to compensate for the deficiency of pure ConvNets and pure attention networks. First, we introduced an attention mechanism Squeeze and Excitation (SE) module in the MobilenetV2 (MV2) structure of the network to enhance the representation of picture channels. Then, we designed a Multidimension Attention module (MDA) to guide the network to thoroughly learn and utilize local and global features through short connections. Moreover, using the Atrous Spatial Pyramid Pooling (ASPP) module to obtain longer distance relations, we fused the model features from different layers, and used the obtained joint features for wild mushroom classification. We validated the model on two datasets, mushroom and MO106, and the results showed that M-ViT performed the best on the two test datasets, with accurate dimensions of 96.21% and 91.83%, respectively. We compared the performance of our method with that of more advanced ConvNets and attention networks (Transformer), and our method achieved good results.

The Improvement of Artificial Neural Network Accuracy Using Principle Component Analysis Approach

An important problem in a classification system is how to get good accuracy results. A way to increase the accuracy of a classifier system is to improve the number of input data attributes. Improving the number of input data attributes can be done using the Principal Component Analysis (PCA) method. The aim of this research is to reduce the number of input data attributes to increase the accuracy in a mushroom classification system. The research method used in this study started from collecting datasets from Kaggle.com related to mushroom-classification, then the data visualization process was carried out using pie charts then a dimension reduction process was carried out to reduce the number of variables using the PCA method. The next step is the training and testing of the artificial neural network. The architecture of artificial neural network used is backward error propagation with the number of hidden layers as much as 2 layers with the number of cells as many as 3 and 2. The training data used is 80%, while the testing data is 20%. Based on the test results, obtained an accuracy of 100% with 150,000 iterations and using 11 input variables from 22 existing input variables. By adding Principal Component Analysis part of the development that can improve the accuracy and performance of Artificial Neural Networks

Machine Learning-Based Classification of Mushrooms Using a Smartphone Application

Worldwide, a large number of cases of harmful mushroom exposure and consumption result in hallucinations, sickness, and death. One contributing factor is that certain poisonous mushrooms closely resemble their edible counterparts, making it difficult for general public collectors to distinguish one from the other. We propose a method to classify mushroom types from field-collection images using a smartphone application based on a convolutional neural network. The application helps people without proper mycology background or training to distinguish poisonous mushrooms from edible ones with which they may be confused. We showed three case studies to classify two-, three-, and five-class models by optimizing their training steps by cross-validation. An android app was developed by transferring the server-based trained model and allowing users to obtain probability scores for the correct genus classification. Our experiments showed that this method could provide sensitivity and specificity of two-, three-, and five-class mushroom models ranging from 89% to 100% using an image from the field with diverse backgrounds and objects.

Automatic Mushroom Species Classification Model for Foodborne Disease Prevention Based on Vision Transformer

Mushrooms are the fleshy, spore-bearing structure of certain fungi, produced by a group of mycelia and buried in a substratum. Mushrooms are classified as edible, medicinal, and poisonous. However, many poisoning incidents occur yearly by consuming wild mushrooms. Thousands of poisoning incidents are reported each year globally, and 80% of these are from unidentified species of mushrooms. Mushroom poisoning is one of the most serious food safety issues worldwide. Motivated by this problem, this study uses an open-source mushroom dataset and employs several data augmentation approaches to decrease the probability of model overfitting. We propose a novel deep learning pipeline (ViT-Mushroom) for mushroom classification using the Vision Transformer large network (ViT-L/32). We compared the performance of our method against that of a convolutional neural network (CNN). We visualized the high-dimensional outputs of the ViT-L/32 model to achieve the interpretability of ViT-L/32 using the t-distributed stochastic neighbor embedding (t-SNE) method. The results show that ViT-L/32 is the best on the testing dataset, with an accuracy score of 95.97%. These results surpass previous approaches in reducing intraclass variability and generating well-separated feature embeddings. The proposed method is a promising deep learning model capable of automatically classifying mushroom species, helping wild mushroom consumers avoid eating toxic mushrooms, safeguarding food safety, and preventing public health incidents of food poisoning. The results will offer valuable resources for food scientists, nutritionists, and the public health sector regarding the safety and quality of mushrooms.

Validitas Video Animasi Powtoon Kelas X SMA Berdasarkan Hasil Inventarisasi Jamur Makroskopis

Powtoon is an software animation video that can be accessed online, contains simple and interesting animations, and provides music facilities and can record narration. This study aims to determine the feasibility of an animated video powtoon based on the results of an inventory of macroscopic fungi found in the Sekadau Mount Protection Forest, Sambas Regency. The research instrument used is a validation sheet, which includes aspects of format, content, language, simplicity, illustrations, media quality, and attractiveness. This type of research is research and development with the Plomp model. The results showed that the validation of the animated video powtoon was carried out by five validators with a CVI (Content Validity Index) value of 0.99 which was declared feasible. Based on the validation results, it can be concluded that the animated powtoon video in the class X high school mushroom classification submaterial is declared suitable for use as a learning medium.

Validitas Lembar Kerja Peserta Didik Berbasis Discovery Learning pada Submateri Klasifikasi Jamur di Kelas X SMA

Discovery learning-based Student Worksheets are printed teaching materials containing materials and instructions for implementing learning tasks that can help students make discoveries in the learning process. This study aims to determine the feasibility of student worksheets based on discovery learning in the class X high school mushroom classification submaterial based on the results of an inventory of macrocosmic fungi in the Gunung Majau Protected Forest, Sambas Regency. The research instrument used is a validation sheet, which includes aspects of content, language, presentation and graphics. The method used in this research is a descriptive method with a quantitative research form. The results of this study indicate that the validation of student worksheets based on discovery learning was carried out by five validators with a CVI (Content Validity Index) value of 0.99 which was declared feasible or valid. Based on the results of the validation, it can be concluded that the student worksheets based on discovery learning from the inventory of macrocosmic fungi in the Gunung Majau Protection Forest, Sambas Regency are feasible in terms of content with a CVI value of 0.99 (feasible or valid), so they can be used as teaching materials in the classification sub-material class X high school mushrooms.

Application of Decision Tree Algorithm for Edible Mushroom Classification

The purpose of this research is to classify the mushroom based on its characteristic to be in an edible class or poisonous one using the Decision Tree Algorithm. The result showed that odor is the most important attribute to classify the mushroom. Mushrooms which have almond and anise odors are edible, while the rest of it, such as pungent, foul, creosote, fishy, spicy, and musty are poisonous which means they can't be eaten. For mushrooms that have no odor, there are some attributes to be checked such as spore-print-color, gill-size, gill-spacing, and population. At first, overfitting happened. To overcome this, the researcher used Random Sampling Techniques until got better accuracy. The most accurate sample is 99,9% using sample 6 or 2000 data.

PERFORMANCE COMPARISON OF MUSHROOM TYPE CLASSIFICATION BASED ON MULTI-SCENARIO DATASET USING DECISION TREE C4.5 AND C5.0

Indonesia has a tropical climate that supports mushroom growth. Mushroom classification into poisonous and non-poisonous mushrooms. Identification of the type of mushroom is vital because mushrooms, especially poisonous mushrooms, risk causing potential hazards to humans, such as causing serious illness and even death. This study aimed to identify the fungus type using a computational approach, namely the Decision Tree C4.5 and C5.0 Algorithms. This research contributes to using multi-scenario datasets and comparing the performance of the C4.5 and C5.0 decision tree algorithms. The dataset used is a fungal classification dataset obtained from kaggle.com. The method stages in this research are literature study, data collection, and data preprocessing, which includes a data cleaning process and a partitioning process for multi-scenario datasets. Afterwards, the Decision Tree Algorithms C4.5 and C5.0 were implemented using the sci-kit-learn library. The last step is to do a performance comparison using the confusion matrix. The results showed that identifying poisonous mushrooms using the Decision Tree C5.0 Algorithm obtained an accuracy of 97.05% for scenario 1, 97.00% for scenario 2, and 97.11% for scenario 3. At the same time, the Decision Tre C4.5 algorithm yielded an accuracy. by 96.92% for scenario 1, 96.90% for scenario 2, and 97.05% for scenario 3. Based on the comparison of the performance of the classification results, we conclude that the Decision Tree C5.0 algorithm in scenario 3 has the highest accuracy for fungal identification poisonous.

A New Deep Learning Model for the Classification of Poisonous and Edible Mushrooms Based on Improved AlexNet Convolutional Neural Network

The difficulty involved in distinguishing between edible and poisonous mushrooms stems from their similar appearances. In this study, we attempted to classify five common species of poisonous and edible mushrooms found in Thailand, Inocybe rimosa, Amanita phalloides, Amanita citrina, Russula delica, and Phaeogyroporus portentosus, using the convolutional neural network (CNN) and region convolutional neural network (R-CNN). This study was motivated by the yearly death toll from eating poisonous mushrooms in Thailand. In this research, a method for the classification of edible and poisonous mushrooms was proposed and the testing time and accuracy of three pretrained models, AlexNet, ResNet-50, and GoogLeNet, were compared. The proposed model was found to reduce the duration required for training and testing while retaining a high level of accuracy. In the mushroom classification experiments using CNN and R-CNN, the proposed model demonstrated accuracy levels of 98.50% and 95.50%, respectively.

IoT enabled mushroom farm automation with Machine Learning to classify toxic mushrooms in Bangladesh

The recent statistics on Agriculture identically remarks the massive contributions of mushroom farming in the Global market. Thus, the popularity of mushroom farming and cultivation is increasing day after day. Weather monitoring and management is a significant feature for mushroom growth, especially the impact of temperature and humidity. Most of the farmers in remote areas worldwide use traditional ways to cultivate the mushrooms. The traditional way is very complicated, and often poisonous mushrooms appear due to the lack of sufficient weather and cultivation process monitoring. This paper reflects an architectural design of IoT & Machine Learning (ML)-based Smart Mushroom Farming. The proposed system introduces Remote Monitoring and Management (RMM), Farm Automation, and Mushroom classification. The Internet of Things (IoT), the microcontroller ESP32, and some agriculture-related sensors enable smart monitoring and farm automation. Machine Learning (ML) technology has been adopted to classify edible mushrooms to avoid poisonous mushrooms. To investigate the efficiency of the proposed system, several experiments have been enumerated and interpreted. The study is further furnished with the System Usability Scale (SUS) to track the regular user's satisfaction and gained the SUS score of 82%. The ML model is utilized by an ensemble model that is composed of six classifiers namely Decision Tree (DT), Logistic Regression (LR), K-nearest neighbor (KNN), Support Vector Machine (SVM), Naïve Bayes (NB), and Random Forest (RF). The highest accuracy gained with the ensemble model is 100% which outperforms each individual classifier. However, the system will be efficient for real-time farm automation and cultivation in the mushroom industry.

Prediction of Insufficient Accuracy for Mushroom Classification whether Poisonous or Eatable Food using Random Forest Training by comparing Logistic Regression to Improve Accuracy

Prediction of Insufficient Accuracy for Mushroom Classification whether Poisonous or Eatable Food using Random Forest Training by comparing Logistic Regression to Improve Accuracy

Prediction of Insufficient Accuracy for Mushroom Classification whether Poisonous or Eatable Mushrooms using Support Vector Machine by comparing Logistic Regression to Improve Accuracy

Prediction of Insufficient Accuracy for Mushroom Classification whether Poisonous or Eatable Mushrooms using Support Vector Machine by comparing Logistic Regression to Improve Accuracy