Artificial Intelligence Aided Agricultural Sensors for Plant Frostbite Protection
Frostbite and frost is one of the problems that endanger the health of crops and can ruin plants and fruits. Soil temperature is the most significant factor that influences the freezing depth. Therefore, monitoring and predicting this characteristic is crucial for frostbite protection. This study ai...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2022.2031814 |
| _version_ | 1797641102319157248 |
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| author | Shiva Hassanjani Roshan Javad Kazemitabar Ghorban Kheradmandian |
| author_facet | Shiva Hassanjani Roshan Javad Kazemitabar Ghorban Kheradmandian |
| author_sort | Shiva Hassanjani Roshan |
| collection | DOAJ |
| description | Frostbite and frost is one of the problems that endanger the health of crops and can ruin plants and fruits. Soil temperature is the most significant factor that influences the freezing depth. Therefore, monitoring and predicting this characteristic is crucial for frostbite protection. This study aims to predict soil temperature on cold days to prevent frostbite injury in crops. For this matter, we used the registered and logged hourly data by the HOBO U30 data logging device and predicted the soil temperature from air temperature, soil water content, and relative humidity. We used 80% of the data set for the training data and assigned the other 20% to the test data. RMSE and MSE were two of the evaluation criteria of the neural network in this study. Also, we calculated P-value and T-value for statistical hypothesis testing. In another approach for weighting the neural network, we used evolutionary algorithms such as Genetic Algorithm and Particle Swarm Optimization instead of the gradient-based methods. According to the results, Multi-layer perceptron neural network with the respective values 0.082 and 0.0068 for RMSE and MSE in training data and 0.085 and 0.0073 for RMSE and MSE in testing data proved to have a better performance in the soil temperature prediction compared to the ANN-GA and ANN-PSO models. Farmers, botanical researchers, and policymakers in food security can use these results. |
| first_indexed | 2024-03-11T13:40:42Z |
| format | Article |
| id | doaj.art-c201de4618e2485ab4343e5a82a91cd0 |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-11T13:40:42Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-c201de4618e2485ab4343e5a82a91cd02023-11-02T13:36:38ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452022-12-0136110.1080/08839514.2022.20318142031814Artificial Intelligence Aided Agricultural Sensors for Plant Frostbite ProtectionShiva Hassanjani Roshan0Javad Kazemitabar1Ghorban Kheradmandian2Babol Noshirvani University of TechnologyBabol Noshirvani University of TechnologyTosanFrostbite and frost is one of the problems that endanger the health of crops and can ruin plants and fruits. Soil temperature is the most significant factor that influences the freezing depth. Therefore, monitoring and predicting this characteristic is crucial for frostbite protection. This study aims to predict soil temperature on cold days to prevent frostbite injury in crops. For this matter, we used the registered and logged hourly data by the HOBO U30 data logging device and predicted the soil temperature from air temperature, soil water content, and relative humidity. We used 80% of the data set for the training data and assigned the other 20% to the test data. RMSE and MSE were two of the evaluation criteria of the neural network in this study. Also, we calculated P-value and T-value for statistical hypothesis testing. In another approach for weighting the neural network, we used evolutionary algorithms such as Genetic Algorithm and Particle Swarm Optimization instead of the gradient-based methods. According to the results, Multi-layer perceptron neural network with the respective values 0.082 and 0.0068 for RMSE and MSE in training data and 0.085 and 0.0073 for RMSE and MSE in testing data proved to have a better performance in the soil temperature prediction compared to the ANN-GA and ANN-PSO models. Farmers, botanical researchers, and policymakers in food security can use these results.http://dx.doi.org/10.1080/08839514.2022.2031814 |
| spellingShingle | Shiva Hassanjani Roshan Javad Kazemitabar Ghorban Kheradmandian Artificial Intelligence Aided Agricultural Sensors for Plant Frostbite Protection Applied Artificial Intelligence |
| title | Artificial Intelligence Aided Agricultural Sensors for Plant Frostbite Protection |
| title_full | Artificial Intelligence Aided Agricultural Sensors for Plant Frostbite Protection |
| title_fullStr | Artificial Intelligence Aided Agricultural Sensors for Plant Frostbite Protection |
| title_full_unstemmed | Artificial Intelligence Aided Agricultural Sensors for Plant Frostbite Protection |
| title_short | Artificial Intelligence Aided Agricultural Sensors for Plant Frostbite Protection |
| title_sort | artificial intelligence aided agricultural sensors for plant frostbite protection |
| url | http://dx.doi.org/10.1080/08839514.2022.2031814 |
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