مروری بر پیش‌بینی عملکرد محصول با استفاده از الگوریتم‌های هوش مصنوعی

Abdullahi, M. U., Olalere, M., Aimufua, G. I., and Egga, B. H. (2024). Crop Recommendation Predictive Analysis Using Ensembling Techniques. Journal of Basics and Applied Sciences Research, 2 (1), 162-176. https://doi.org/10.33003/jobasr-2024-v2i1-43.

Ali, A., Hussain, T., tantashutikun, N., hussain, N., & Cocetta, G. (2023). Application of Smart Techniques, Internet of Things, and Data Mining for Resource Use Efficient and Sustainable Crop Production. Agriculture, 13 (2), 397. https://doi.org/10.3390/agriculture13020397.

Ali, A., Hussain, T., tantashutikun, N., hussain, N., & Cocetta, G. (2023). Application of Smart Techniques, Internet of Things, and Data Mining for Resource Use Efficient and Sustainable Crop Production. Agriculture, 13 (2), 397. https://doi.org/10.3390/agriculture13020397.

Bashiri, M., Marousi, A., Salari, A. and Qudousi, M. (2016). Climatic Classification and Determination of Areas Prone to Saffron Cultivation Using Data Mining Algorithms at the Level of Razavi Khorasan Province. Journal of Saffron Agriculture and Technology, 2 (3), 379-392. (In Persian). https://doi.org/10.22048/jsat.2017.60768.1189.

Bocca, F. F. and Rodrigues, L. H. A. (2016). The Effect of Tuning, Feature Engineering, and Feature Selection in Data Mining Applied to Rainfed Sugarcane Yield Modelling. Computers and Electronics in Agriculture, 128, 67-76. https://doi.org/10.1016/j.compag.2016.08.015.

Cai, Y., Guan, K., Lobell, D., Potgieter, A. B., Wang, S., Peng, J., ... and Peng, B. (2019). Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches. Agricultural and forest meteorology, 274, 144-159. https://doi.org/10.1016/j.agrformet.2019.03.010.

Charoen-Ung, P. and Mittrapiyanuruk, P. (2018). Sugarcane Yield Grade Prediction Using Random Forest and Gradient Boosting Tree Techniques. 2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE), 1-6. IEEE. https://doi.org/10.1109/JCSSE.2018.8457391.

Charoen-Ung, P. and Mittrapiyanuruk, P. (2019). Sugarcane Yield Grade Prediction Using Random Forest with Forward Feature Selection and Hyper-Parameter Tuning. In Recent Advances in Information and Communication Technology. Proceedings of the 14th International Conference on Computing and Information Technology (IC2IT 2018), 33-42. Springer International Publishing. https://doi.org/10.1007/978-3-319-93692-5_4.

de Oliveira, M. P. G., Bocca, F. F., and Rodrigues, L. H. A. (2017). From Spreadsheets to Sugar Content Modeling: A Data Mining Approach. Computers and Electronics in Agriculture, 132, 14-20. https://doi.org/10.1016/j.compag.2016.11.012.

Everingham, Y., Sexton, J., Skocaj, D. and Inman-Bamber, G. (2016). Accurate Prediction of Sugarcane Yield Using a Random Forest Algorithm. Agronomy for Sustainable Development, 36, 1-9. https://doi.org/10.1007/s13593-016-0364-z.

Ferraro, D. O., Rivero, D. E., and Ghersa, C. M. (2009). An Analysis of the Factors that Influence Sugarcane Yield in Northern Argentina Using Classification and Regression Trees. Field Crops Research, 112(2-3), 149-157. https://doi.org/10.1016/j.fcr.2009.02.014.

Fleiss, J. L., Levin, B., and Paik, M. C. (2013). Statistical Methods for Rates and Proportions. John Wiley & Sons.

 

Gharehveran, S. S., Shirini, K., Khavar, S. C., Mousavi, S. H., & Abdolahi, A. (2024). Deep learning-based demand response for short-term operation of renewable-based microgrids. The Journal of Supercomputing, 1-34.  https://doi.org/10.1007/s11227-024-06407-z

Gheibi, Y., Shirini, K., Razavi, S. N., Farhoudi, M., & Samad-Soltani, T. (2023). CNN-Res: deep learning framework for segmentation of acute ischemic stroke lesions on multimodal MRI images. BMC Medical Informatics and Decision Making, 23(1), 192. https://doi.org/10.1186/s12911-023-02289-y.

González Sánchez, A., Frausto Solís, J., and Ojeda Bustamante, W. (2014). Predictive Ability of Machine Learning Methods for Crop Yield Prediction. Journal of Agricultural Engineering Research, 97(3), 423-443. https://doi.org/10.5424/sjar/2014122-4439.

Gopal, P. M. and Bhargavi, R. (2019). A Novel Approach for Efficient Crop Yield Prediction. Computers and Electronics in Agriculture, 165, 104968. https://doi.org/10.1016/j.compag.2019.104968.

Hajivand, A. T., Shirini, K., Salehi, M., & Meshkini, A. (2024). A Review of Artificial Intelligence Optimization Algorithms in Scheduling Problems with Constrained Resources in Mechanization. 9th Advanced Engineering Days (AED), 9, 425-428.

Hajivand, A. T.,Shirini, K., & Ghareveran, S. S. (2024). Smart Agriculture: Leveraging Information Technology and Human Expertise for Sustainable Farming. 9th Advanced Engineering Days (AED), 9, 419-421

Hammer, R. G., Sentelhas, P. C., and Mariano, J. C. (2020). Sugarcane Yield Prediction through Data Mining and Crop Simulation Models. Sugar Tech, 22 (2), 216-225. https://doi.org/10.1007/s12355-019-00776-z.

Kumar, S., Kumar, V., and Sharma, R. K. (2015). Sugarcane Yield Forecasting Using Artificial Neural Network Models. International Journal of Artificial Intelligence and Applications, 6 (5), 51-68. https://doi.org/10.5121/ijaia.2015.6504.

Medar, R. A., Rajpurohit, V. S., and Ambekar, A. M. (2019). Sugarcane Crop Yield Forecasting Model Using Supervised Machine Learning. Journal of Intelligent-Systems-and-Applications, 11 (8), 11. https://doi.org/10.5815/ijisa.2019.08.02.

Nematpour Malek Abad, H., and Zaki Dizaji, H. (2023). Analytical Investigation of Factors Affecting the Sustainability of Livestock Industry Development: A Case Study of Lorestan Province. Iranian Journal of Biosystems Engineering, 54 (3), 43-60. (In Persian). https://doi.org/10.22059/ijbse.2024.368363.665531.

Qiu, Y., Zhou, J., Khandelwal, M., Yang, H., Yang, P., & Li, C. (2022). Performance evaluation of hybrid WOA-XGBoost models to predict ground vibration. Engineering with Computers, 38 (Suppl 5), 4145-4162. https://doi.org/10.1007/s00366-021-01393-9.

Ravi, & Baranidharan, B. (2020). Crop yield Prediction using XG Boost algorithm. Int. J. Recent Technol. Eng, 8(5), 3516-3520. https://doi.org/10.35940/ijrte.D9547.018520.

Rajamani, S. K. and Iyer, R. S. (2023). Machine Learning-Based Mobile Applications Using Python and Scikit-Learn. In Designing and Developing Innovative Mobile Applications, 282-306. IGI Global. https://doi.org/10.4018/978-1-6684-8582-8.ch016.

Rajeswari, S. K. R., K., Suthendran, K., and Rajakumar, K. (2017). A Smart Agricultural Model by integrating IoT, Mobile and Cloud-Based Big Data Analytics. In 2017 International Conference on Intelligent Computing and Control (I2C2). IEEE. pp. 1-5. https://doi.org/10.1109/I2C2.2017.8321902.

Ramesh, D., and Vardhan, B. V. (2015). Analysis of Crop Yield Prediction Using Data Mining Techniques. International Journal of Research in Engineering and Technology, 4 (1), 47-473. https://doi.org/10.15623/ijret.2015.0401071

Saito, T. and Rehmsmeier, M. (2015). The Precision-Recall Plot is More Informative than the ROC Plot when Evaluating Binary Classifiers on Imbalanced Datasets. PloS one, 10 (3), e0118432. https://doi.org/10.1371/journal.pone.0118432.

Sattari, M. T., Bagheri, R., Shirini, K., and Allahverdipour, P. (2024). Modeling daily and monthly rainfall in Tabriz using ensemble learning models and decision tree regression. Scientific Journal of Golestan University, 5(18). (In Persian) https://doi.org/10.30488/CCR.2024.433394.1192.

 Sattari, M. T., Shirini, K., and Javidan, S. (2024). Evaluating the efficiency of dimensionality reduction methods in improving the accuracy of water quality index modeling using machine learning algorithms. Water and Soil Management and Modelling, 4(2), 89-104. https://doi.org/10.22098/mmws.2023.12434.1241.

Sellam, V., and Poovammal, E. (2016). Prediction of Crop Yield Using Regression Analysis. Indian Journal of Science and Technology, 9 (38), 1-5. https://doi.org/10.17485/ijst/2016/v9i38/91714.

Sharma, M., Kumar, C. J., and Bhattacharyya, D. K. (2024). Machine/deep learning techniques for disease and nutrient deficiency disorder diagnosis in rice crops: A systematic review. Biosystems Engineering, 244, 77-92. https://doi.org/10.1016/j.biosystemseng.2024.05.014.

Shingade, S. D., and Mudhalwadkar, R. P. (2024). Analysis of Crop Prediction Models Using Data Analytics and ML Techniques: A Review. Multimedia Tools and Applications, 83 (13), 37813-37838. https://doi.org/10.1007/s11042-023-17038-6.

Shirini, K., Aghdasi, H. S., & Saeedvand, S. (2024). A comprehensive survey on multiple-runway aircraft landing optimization problem. International Journal of Aeronautical and Space Sciences, 1-29. https://doi.org/10.1007/s42405-024-00747-z.

Shirini, K., Hajivand, A. T., & Ghareveran, S. S. (2024). A novel deep learning-based method for potato leaf disease classification. 9th Advanced Engineering Days, 9, 462-464.

Shirini, K., Taherihajivand, A., and samadi Gharehveran, S. (2023). A review of algorithms for solving the project scheduling problem with resource-constrained considering agricultural problems. Agricultural Mechanization, 8(1), 1-14. (In Persian). https://doi.org/10.22034/jam.2023.55751.1227.

Sirsat, M. S., Mendes-Moreira, J., Ferreira, C., and Cunha, M. (2019). Machine Learning predictive model of grapevine yield based on agroclimatic patterns. Engineering in Agriculture. Environment and Food. 12(4), 443-450. https://doi.org/10.1016/j.eaef.2019.07.003.

 Sutha, K., Indumathi, N., and SHANKARI, S. (2023). Recommending and Predicting Crop Yield using Smart Machine Learning Algorithm (SMLA). Current Agriculture Research Journal, 11(2). http://dx.doi.org/10.12944/CARJ.11.2.30.

Taheri, H. A., Shirini, K., and Samadi, G. S. (2024). Weed detection in cereal fields using convolutional neural network based on deep learning. Agricultural Engineering, 47(1): 129-142. (In Persian). https://doi.org/10.22055/agen.2024.45327.1688.

Taheri hajivand, A., Shirini, K., and Samadi Gharehveran, S. (2024). Weed detection in fields using convolutional neural network based on deep learning. Agricultural Engineering, 47(1), 129-142. (In Persian). https://doi.org/10.22055/agen.2024.45327.1688.

Thuankaewsing, S., Khamjan, S., Piewthongngam, K., and Pathumnakul, S. (2015). Harvest scheduling algorithm to equalize supplier benefits: A case study from the Thai sugar cane industry. Computers and Electronics in Agriculture, 110, 42-55. https://doi.org/10.1016/j.compag.2014.10.005.

Vani, P. S. and Rathi, S. (2023). Improved Data Clustering Methods and Integrated A-FP Algorithm for Crop Yield Prediction. Distributed and Parallel Databases, 41 (1), 117-131. https://doi.org/10.1007/s10619-021-07350-1.

Varjavand, P. and Khorramian, M. (2024). Real Water Saving Using Crop Substitution in Cropping Pattern: A Case Study of Khuzestan Province, Iran. Journal of Irrigation and Drainage Engineering, 150 (6), 05024004. https://doi.org/10.1061/JIDEDH.IRENG-10318.

Veenadhari, S., Misra, B., and Singh, C. D. (2014). Machine learning approach for forecasting crop yield based on climatic parameters. In 2014 International Conference on Computer Communication and Informatics IEEE (pp. 1-5). https://doi.org/10.1109/ICCCI.2014.6921718.

Yang, Y., Khorshidi, H., and Aickelin, U. (2024). A Review on Over-Sampling Techniques in Classification of Multi-Class Imbalanced Datasets: Insights for Medical Problems. Frontiers in Digital Health, 6, 1430245. https://doi.org/10.3389/fdgth.2024.1430245.

Zahiri, M., Shirini, K., & Samadi Gharehveran, S. (2024). Network traffic analysis with machine learning for faster detection of distributed denial of service attack. Journal of Advanced Defense Science & Technology. 14 (4), 67-76. https://dorl.net/dor/20.1001.1.26762935.1402.14.3.1.5.

Zaki Dizaji, H., Bahrami, H., Monjezi, N., and Sheikhdavoodi, M. J. (2019). Modeling the Variables that Influence Sugarcane Yield using CART and QUEST Decision Tree Algorithms. Journal of Agricultural Machinery, 9 (2), 469-484. https://doi.org/10.22067/jam.v9i2.469.