| Autor: |
Hasibur Rahman, Md. Omar Faruq, Talha Bin Abdul Hai, Wahidur Rahman, Muhammad Minoar Hossain, Mahbubul Hasan, Shafiqul Islam, Md. Moinuddin, Md. Tarequl Islam, Mir Mohammad Azad |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Journal of Agriculture and Food Research, Vol 7, Iss , Pp 100267- (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2666-1543 |
| DOI: |
10.1016/j.jafr.2021.100267 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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