Mushroom Classification using Random Forest and REP Tree Classifiers

Paudel, Nawaraj; Bhatta, Jagdish

doi:10.3126/njmathsci.v3i1.44130

Cited by 3 publications

(1 citation statement)

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“…InceptionV3 achieved the highest accuracy of 88.40% among the implemented algorithms. The research in [8] conduct a comparison between the performance of Random Forest and Reduced Error Pruning (REP) tree classification algorithms in classifying edible and poisonous mushrooms. The study in [9] employed Gaussian naïve Bayes along with Linear Discriminant Analysis (LDA) to separate edible and non-edible mushrooms.…”

Section: Related Workmentioning

confidence: 99%

Automating Mushroom Culture Classification: A Machine Learning Approach

Ujir,

Hipiny,

Bolhassan

et al. 2024

IJACSA

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Traditionally, the classification of mushroom cultures has conventionally relied on manual inspection by human experts. However, this methodology is susceptible to human bias and errors, primarily due to its dependency on individual judgments. To overcome these limitations, we introduce an innovative approach that harnesses machine learning methodologies to automate the classification of mushroom cultures. Our methodology employs two distinct strategies: the first involves utilizing the histogram profile of the HSV color space, while the second employs a convolutional neural network (CNN)-based technique. We evaluated a dataset of 1400 images from two strains of Pleurotus ostreatus mycelium samples over a period of 14 days. During the cultivation phase, we base our operations on the histogram profiles of the masked areas. The application of the HSV histogram profile led to an average precision of 74.6% for phase 2, with phase 3 yielding a higher precision of 95.2%.For CNN-based method, the discriminative image features are extracted from captured images of rhizomorph mycelium growth. These features are then used to train a machine learning model that can accurately estimate the growth rate of a rhizomorph mycelium culture and predict contamination status. Using MNet and MConNet approach, our results achieved an average accuracy of 92.15% for growth prediction and 97.81% for contamination prediction. Our results suggest that computer-based approaches could revolutionize the mushroom cultivation industry by making it more efficient and productive. Our approach is less prone to human error than manual inspection, and it can be used to produce mushrooms more efficiently and with higher quality.

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Section: Related Workmentioning

confidence: 99%

Automating Mushroom Culture Classification: A Machine Learning Approach

Ujir,

Hipiny,

Bolhassan

et al. 2024

IJACSA

View full text Add to dashboard Cite

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Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review

et al. 2023

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Due to the increasing global population and the growing demand for food worldwide as well as changes in weather conditions and the availability of water, artificial intelligence (AI) such as expert systems, natural language processing, speech recognition, and machine vision have changed not only the quantity but also the quality of work in the agricultural sector. Researchers and scientists are now moving toward the utilization of new IoT technologies in smart farming to help farmers use AI technology in the development of improved seeds, crop protection, and fertilizers. This will improve farmers' profitability and the overall economy of the country. AI is emerging in three major categories in agriculture, namely soil and crop monitoring, predictive analytics, and agricultural robotics. In this regard, farmers are increasingly adopting the use of sensors and soil sampling to gather data to be used by farm management systems for further investigations and analyses. This article contributes to the field by surveying AI applications in the agricultural sector. It starts with background information on AI, including a discussion of all AI methods utilized in the agricultural industry, such as machine learning, the IoT, expert systems, image processing, and computer vision. A comprehensive literature review is then provided, addressing how researchers have utilized AI applications effectively in data collection using sensors, smart robots, and monitoring systems for crops and irrigation leakage. It is also shown that while utilizing AI applications, quality, productivity, and sustainability are maintained. Finally, we explore the benefits and challenges of AI applications together with a comparison and discussion of several AI methodologies applied in smart farming, such as machine learning, expert systems, and image processing.

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