Monitoring Mushroom Growth with Machine Learning

Moysiadis, Vasileios; Kokkonis, George; Bibi, Stamatia; Moscholios, Ioannis D.; Maropoulos, Nikolaos; Sarigiannidis, Panagiotis

doi:10.3390/agriculture13010223

Cited by 14 publications

(8 citation statements)

References 27 publications

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“…IoT environmental sensor [39] shiitake temperature, humidity, CO 2 [40] oyster temperature, humidity, light [13] oyster temperature, humidity, light [36] gourmet * temperature, humidity, CO 2, light [41] oyster temperature, humidity Camera and AI computer vision [42] enoki RGB image [43] white button RGB image [44] gourmet RGB image [45] oyster RGB image [8] gourmet RGB image Integrated IoT environmental sensor-camera [46] white button temperature, humidity, RGB image [47] white button temperature, humidity, CO 2 , RGB image [28] gourmet temperature, humidity, soil moisture, soil temperature, light, RGB image [48] gourmet temperature, humidity, RGB image [49] oyster temperature, humidity, light, soil moisture, RGB image * gourmet mushrooms include all edible mushrooms such as oyster, shiitake, enoki, or white button mushrooms.…”

Section: Study Mushroom Species Monitoring Parametersmentioning

confidence: 99%

An Integrated IoT Sensor-Camera System toward Leveraging Edge Computing for Smart Greenhouse Mushroom Cultivation

Nguyen,

Shin,

Jung

et al. 2024

Agriculture

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Industrial greenhouse mushroom cultivation is currently promising, due to the nutritious and commercial mushroom benefits and its convenience in adapting smart agriculture technologies. Traditional Device-Cloud protocol in smart agriculture wastes network resources when big data from Internet of Things (IoT) devices are directly transmitted to the cloud server without processing, delaying network connection and increasing costs. Edge computing has emerged to bridge these gaps by shifting partial data storage and computation capability from the cloud server to edge devices. However, selecting which tasks can be applied in edge computing depends on user-specific demands, suggesting the necessity to design a suitable Smart Agriculture Information System (SAIS) architecture for single-crop requirements. This study aims to design and implement a cost-saving multilayered SAIS architecture customized for smart greenhouse mushroom cultivation toward leveraging edge computing. A three-layer SAIS adopting the Device-Edge-Cloud protocol, which enables the integration of key environmental parameter data collected from the IoT sensor and RGB images collected from the camera, was tested in this research. Implementation of this designed SAIS architecture with typical examples of mushroom cultivation indicated that low-cost data pre-processing procedures including small-data storage, temporal resampling-based data reduction, and lightweight artificial intelligence (AI)-based data quality control (for anomalous environmental conditions detection) together with real-time AI model deployment (for mushroom detection) are compatible with edge computing. Integrating the Edge Layer as the center of the traditional protocol can significantly save network resources and operational costs by reducing unnecessary data sent from the device to the cloud, while keeping sufficient information.

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Section: Study Mushroom Species Monitoring Parametersmentioning

confidence: 99%

An Integrated IoT Sensor-Camera System toward Leveraging Edge Computing for Smart Greenhouse Mushroom Cultivation

Nguyen,

Shin,

Jung

et al. 2024

Agriculture

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“…Integrating these advanced techniques into horticultural research has yielded impressive performance and opened avenues for innovative applications. For instance, Moysiadis et al [94] highlighted the potential of using the YOLOv5 pre-trained model for mushroom growth monitoring. The complexities of mushroom growth patterns raise the significance of this research.…”

Section: A Crop Growth Monitoringmentioning

confidence: 99%

“…Most studies rely on small proprietary datasets collected by the researchers themselves, often just a few hundred images, which restricts generalization of techniques ( [92], [97], [106]). There is a need to establish extensive public databases encapsulating the diversity of greenhouse environments, with variability in factors like lighting, humidity, crop types, growth stages, and imaging angles represented ( [91], [94], [105]). Centralized repositories like PlantVillage offer a valuable start but have limited coverage and annotation complexity.…”

Section: ) Lack Of Large-scale Standardized Datasetsmentioning

confidence: 99%

“…Networks designed for generic datasets do not transfer effectively to greenhouses, as the specialized appearance and growth patterns of crops in controlled settings are not represented ( [98], [107], [108]). The frequent occlusion from dense foliage, glass surface reflections, condensation, extreme illumination fluctuations, and evolution of visual features across plant growth requires specialized model architectures and training strategies ( [94], [99], [113]). For instance, Li et al [96] found that directly applying YOLOv3 for cucumber canopy recognition resulted in insufficient accuracy, requiring an enhanced model with additional coordinate attention modules.…”

Section: ) Lack Of Large-scale Standardized Datasetsmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture

Akbar,

Kamarulzaman,

Muzahid

et al. 2024

IEEE Access

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With the escalating global challenges of food security and resource sustainability, innovative solutions like deep learning and computer vision are transforming agricultural practices by enabling datadriven decision-making. This paper provides a focused review of recent advancements in deep learningenabled computer vision techniques tailored specifically for greenhouse environments. First, deep learning and computer vision fundamentals are briefly introduced. Over 100 studies from 2020 to date are then comprehensively reviewed in which these technologies were applied within greenhouses for growth monitoring, disease detection, yield estimation, and other tasks. The techniques, datasets, models, and overall performance results reported in the literature are analyzed. Tables and figures showcase real-world implementations and results synthesized from current research. Key challenges are also outlined related to aspects like model adaptability, lack of sufficient labeled greenhouse data, computational constraints, the need for multi-modal sensor fusion, and other areas needing further investigation. Future trends and prospects are discussed to provide guidance for researchers exploring computer vision in the niche greenhouse domain. By condensing prior work and elucidating the state-of-the-art, this timely review aims to promote continued progress in smart greenhouse agriculture. The focused analysis, specifically on greenhouse environments, fills a gap compared to previous agricultural surveys. Overall, this paper highlights the immense potential of computer vision and deep learning in driving the emergence of data-driven, smart greenhouse farming worldwide.

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“…Image classification and object detection are two tasks that are highly used in Smart Farming for various purposes, such as detecting or classifying diseases [25][26][27], weeds [28,29], fruits [30,31], pests [32,33] or monitoring crops [34] in the fields or greenhouses. Various machine learning and deep learning algorithms based on Convolutional Neural Networks (CNN) have been developed in recent years.…”

Section: Introductionmentioning

confidence: 99%

Cherry Tree Crown Extraction Using Machine Learning Based on Images from UAVs

Moysiadis,

Siniosoglou,

Kokkonis

et al. 2024

Agriculture

Self Cite

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Remote sensing stands out as one of the most widely used operations in the field. In this research area, UAVs offer full coverage of large cultivation areas in a few minutes and provide orthomosaic images with valuable information based on multispectral cameras. Especially for orchards, it is helpful to isolate each tree and then calculate the preferred vegetation indices separately. Thus, tree detection and crown extraction is another important research area in the domain of Smart Farming. In this paper, we propose an innovative tree detection method based on machine learning, designed to isolate each individual tree in an orchard. First, we evaluate the effectiveness of Detectron2 and YOLOv8 object detection algorithms in identifying individual trees and generating corresponding masks. Both algorithms yield satisfactory results in cherry tree detection, with the best F1-Score up to 94.85%. In the second stage, we apply a method based on OTSU thresholding to improve the provided masks and precisely cover the crowns of the detected trees. The proposed method achieves 85.30% on IoU while Detectron2 gives 79.83% and YOLOv8 has 75.36%. Our work uses cherry trees, but it is easy to apply to any other tree species. We believe that our approach will be a key factor in enabling health monitoring for each individual tree.

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Monitoring Mushroom Growth with Machine Learning

Cited by 14 publications

References 27 publications

An Integrated IoT Sensor-Camera System toward Leveraging Edge Computing for Smart Greenhouse Mushroom Cultivation

An Integrated IoT Sensor-Camera System toward Leveraging Edge Computing for Smart Greenhouse Mushroom Cultivation

A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture

Cherry Tree Crown Extraction Using Machine Learning Based on Images from UAVs

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