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

Rahman, Hasibur; Faruq, Md. Omar; Hai, Talha Bin Abdul; Rahman, Wahidur; Hossain, Muhammad Minoar; Hasan, Mahbubul; Hasan, Shafiqul; Moinuddin, Mohammed; Islam, Md. Tarequl; Azad, Mir Mohammad

doi:10.1016/j.jafr.2021.100267

Cited by 45 publications

(31 citation statements)

References 2 publications

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“…Extensive irrigation makes use of 70% of the available freshwater resources. Internet of ings (IoT) and machine learning (ML)-based solution may help in efficient monitoring, controlling, and irrigation scheduling for agriculture fields [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

IoT-Driven Model for Weather and Soil Conditions Based on Precision Irrigation Using Machine Learning

Singh

Sobti

Malik

et al. 2022

Security and Communication Networks

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To feed a growing population, sustainable agriculture practices are needed particularly for irrigation. Irrigation makes use of about 85% of the world’s freshwater resources. Thus, for efficient utilization of water in irrigation, conventional irrigation practices need to either be modified or be replaced with advanced and intelligent systems deploying Internet of Things, wireless sensor networks, and machine learning. This article proposes intelligent system for precision irrigation for monitoring and scheduling using Internet of Things, long range, low-power (LoRa)-based wireless sensor network, and machine learning. The proposed system makes use of soil and weather conditions for predicting the crop’s water requirement. The use of machine learning algorithms provides the proposed system capability of the prediction of irrigation need. Dataset of soil and weather conditions captured using sensors is used with six different machine learning algorithms, and the best one giving highest efficiency in predicting the irrigation scheduling is selected. Linear discriminant analysis algorithm gives the best efficiency of 91.25% with prediction efficiency.

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Section: Introductionmentioning

confidence: 99%

IoT-Driven Model for Weather and Soil Conditions Based on Precision Irrigation Using Machine Learning

Singh

Sobti

Malik

et al. 2022

Security and Communication Networks

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“…In Reference 23, a new ensemble machine learning framework has been presented for monitoring the mushroom field management process. The described system includes automation in agriculture and a remote surveillance system.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The obtained features are partitioned via Equation ( 20) and the anomaly is detected using the process defined in Equation (21). The threshold of the anomaly is determined using a process narrated in Equations ( 22) and (23). At the end of the process, the system provides any one of the following results:…”

Section: Algorithm 1 Anomaly Detection Using Proposed Ai Modelmentioning

confidence: 99%

“…Another contribution of this research work is to compare the proposed model with other existing machine learning classifiers such as KNN, 23 NB, 23 DT, 23 LR, 24 RF, 24 and SVM. 28 From Figure 13, it is apparent that the proposed model outperforms well when compared with the existing machine learning classifiers.…”

Section: Performance Comparison Of Proposed Ai Model Over Existing Me...mentioning

confidence: 99%

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Artificialintelligence‐basedremote monitoring system for automated anomaly detection in wireless sensor networks

Govardhan¹,

Surendranath

Sundararajan³

2022

Concurrency and Computation

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In recent decades, wireless sensor networks (WSNs) are employed in the remote monitoring system which experiences a huge delay due to the excessive time consumption by the image processing techniques. The existing video surveillance system is unable to detect the anomaly accurately and falls short in classifying the routine activity and the anomaly activities. Therefore, an efficient anomaly detection model is necessitated for identifying the anomaly behavior of malicious nodes in the WSN environment. This research work introduces a novel artificial intelligence (AI)-based remote monitoring system for WSN. Primarily, the online video frames are retrieved from the wireless video sensors where the frames are generated by segmenting the entire stream of video as a constant-sized structural frame. The sequence/stream of video segments is fed into the proposed AI framework to detect anomaly activities. It has multiple levels of hidden layers to compare the features of the input captured image with the preloaded reference image. Furthermore, a 5-fold cross-validation approach has been inspected to validate the efficacy of the suggested AI model. The performance results evident that the proposed AI system acquires a better prediction accuracy of 98.2% and precision of 97.3% than the existing monitoring systems.

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“…ese studies can be divided into two main learning approaches. One approach is to manually extract mushroom features and classify the input features by using machine learning models such as support vector machines (SVMs) [21], logistic regression [22], and random forest [23]. Another approach involves extracting features automatically from mushroom images using deep learning models (e.g., CNN) [24].…”

Section: Introductionmentioning

confidence: 99%

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

Wang

2022

Journal of Food Quality

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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.

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

Cited by 45 publications

References 2 publications

IoT-Driven Model for Weather and Soil Conditions Based on Precision Irrigation Using Machine Learning

IoT-Driven Model for Weather and Soil Conditions Based on Precision Irrigation Using Machine Learning

Artificialintelligence‐basedremote monitoring system for automated anomaly detection in wireless sensor networks

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

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