Design and Implementation of SENSEP ACK: An IoT Based Mushroom Cultivation Monitoring System

Shakir, Abdul Alim; Hakim, Faysal; Rasheduzzaman, Mirza; Chakraborty, Sagar; Ahmed, Tausif Uddin; Hossain, Sazzad

doi:10.1109/ecace.2019.8679183

Cited by 7 publications

(6 citation statements)

References 7 publications

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“…According to study [11], implementing IoT technology in mushroom farms presents challenges such as energy management, data security, sensor node placement, internet connectivity, and transmission range. The research in [12] designed a smart system called SENSEPACK to monitor the environment of a mushroom cultivation farm. This system measures temperature, humidity, light, and CO2 levels using appropriate sensors to control the environment of the nursing room.…”

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

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“…Based on Table 1, temperature and humidity are the two important parameters in mushroom cultivation. At the same time, CO 2 is recommended for shitake mushrooms identified by Mat et al (2019) and Ibrahim et al (2018), especially in an enclosed room in a building where air circulation is stagnant (Mat et al, 2019;Shakir et al, 2019). In their control setup, Shakir et al (2019) also added a light intensity parameter to light up the enclosed room after 6.00 pm.…”

Section: Related Workmentioning

confidence: 99%

“…At the same time, CO 2 is recommended for shitake mushrooms identified by Mat et al (2019) and Ibrahim et al (2018), especially in an enclosed room in a building where air circulation is stagnant (Mat et al, 2019;Shakir et al, 2019). In their control setup, Shakir et al (2019) also added a light intensity parameter to light up the enclosed room after 6.00 pm. They observed the increase of CO 2 level in the presence of artificial light, indicating the mushrooms produced more CO2 when the light was turned on.…”

Section: Related Workmentioning

confidence: 99%

Enhanced IoT-Based Climate Control for Oyster Mushroom Cultivation Using Fuzzy Logic Approach and NodeMCU Microcontroller

Ariffin¹,

Ramli²,

Zainol³

et al. 2021

JST

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Urban farming has the potential to utilise unused space in the community to alleviate food shortages and increase the community’s income through local food production. When Internet of Things (IoT) technology is integrated with urban farming, it can further improve its efficiencies and yield. The work in this paper improved our previous work of using an IoT-based climate control system to regulate the cultivation environment of oyster mushrooms automatically. Even though the climate control system could produce two batches of mushroom yields, there were several limitations, such as less efficient climate control due to threshold-based corrective action, water wastage, and system instability. This paper aims to address these stated limitations by implementing a fuzzy logic algorithm and redesigned the climate control system. Two crisp input variables from DHT22 sensors representing temperature and humidity were fed into the Node MCU microcontroller’s fuzzy logic coded in C language. The temperature and humidity conditions were divided into five fuzzy trapezoidal membership functions resulting in 25 fuzzy rules to control the duration of running the water pump and ventilation fan. An internal, lightweight web server were managed all HTTP client requests. The enhanced system also included a safety measurement to avoid overheating the microcontroller and causing water wastage. Upon analysis of the data captured in two months, the result showed a decrease of 40% in water utilisation and an increase of mushrooms yield up to 226%. The enhanced climate control system also facilitated maintaining and controlling the temperature and humidity conducive for optimal mushroom cultivation.

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“…Adjusting the ambient temperature, installing insulation, or utilizing natural ventilation are all methods for regulating temperature. Using technology to control and monitor temperature and humidity effectively maintains optimal conditions [38] for cultivating Indian oyster mushrooms. Sulistyanto et al [39] and Hishamuddin [40] utilized Arduino UNO and the IoT framework to develop temperature and humidity control system prototypes.…”

mentioning

confidence: 99%

“…Utilizing technology and preparing the substrate correctly can optimize the light management for Indian oyster mushroom cultivation. Shakir et al [38], Sulistyanto et al [39], and Mohammed et al [41] discussed using IoT technology to monitor and control the environment for oyster mushroom cultivation automatically, which can increase productivity and decrease labor expenses. Vieira and Andrade [32] and Girmay et al [42] deliberated the significance of substrate preparation for oyster mushroom cultivation, with Vieira and Andrade [32] discovering that substrate composted for seven days with conditioning exhibited higher yield and biological efficiency, and Girmay et al [42] discovering that cotton seed and paper waste were suitable substrates for oyster mushroom cultivation.…”

mentioning

confidence: 99%

IoT-Based Mushroom Cultivation System with Solar Renewable Energy Integration: Assessing the Sustainable Impact of the Yield and Quality

Rukhiran,

Sutanthavibul,

Boonsong

et al. 2023

Sustainability

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The conventional method of mushroom cultivation can be labor-intensive and produce limited yields. Due to the humidity and temperature in the summer season, mushroom production is significantly diminished. The growth of each mushroom species depends on the consistency of care, the skill of experienced farmers, and crucial cultivation parameters such as temperature, humidity, irrigation, and exposure to sunlight. This study aims to implement an IoT-enabled cultivation system to control and monitor the environmental parameters of Indian mushroom cultivation within the proposed innovative framework, as compared to conventional methods. The IoT-based cultivation system consists of hardware components, circuit connections, software, and algorithms. This study confirms that consistent control of environmental parameters, such as temperature and relative humidity, by a dynamic climate promotes mushroom growth that is superior to conventional cultivation. Our findings reveal a substantial increase in the yield and quality of mushrooms, demonstrating the tangible advantages of applying an innovative approach. Traditional cultivation yielded an average of 4.118 kg, whereas IoT-based cultivation systems produced an average of 5.306 kg. The t-test statistic comparing yields has highlighted the significance of the observed differences with a p-value of 0.0000. The research contributions are to design and demonstrate the IoT-enabled system innovation with solar renewable energy, illustrating the effect of mushroom production and quality on the economic market analysis of mushroom cultivation in the direction of environmentally sustainable and green agricultural practices. This study’s comprehensive perspective can provide farmers, agricultural professionals, and policymakers with valuable insights regarding the future of mushroom cultivation, particularly the reduction of carbon emissions and energy consumption.

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Design and Implementation of SENSEP ACK: An IoT Based Mushroom Cultivation Monitoring System

Cited by 7 publications

References 7 publications

Automating Mushroom Culture Classification: A Machine Learning Approach

Automating Mushroom Culture Classification: A Machine Learning Approach

Enhanced IoT-Based Climate Control for Oyster Mushroom Cultivation Using Fuzzy Logic Approach and NodeMCU Microcontroller

IoT-Based Mushroom Cultivation System with Solar Renewable Energy Integration: Assessing the Sustainable Impact of the Yield and Quality

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