نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه مهندسی بیوسیستم ، دانشکده کشاورزی، دانشگاه شهید چمران اهواز، اهواز، ایران.
2 گروه مهندسی کامپیوتر، دانشکده برق و کامپیوتر، دانشگاه تبریز، ایران
3 گروه مهندسی بیوسیستم، دانشکده کشاورزی دانشگاه تبریز ، تبریز، ایران.
4 گروه مهندسی بیوسیستم، دانشکده کشاورزی دانشگاه شهید چمران اهواز، اهواز، ایران.
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
In this research, one of the main goals of Khuzestan sugarcane Agro-industry companies, i.e., increasing the yield of sugarcane fields by using data mining methods, has been investigated. This research is of analytical type and includes the irrigation, drainage, soil and plant data of 1201 farms which were collected from Amirkabir Agriculture Company in 2013 to 2016 crop years. In this research, four algorithms of long Short-Term Memory (LSTM) recurrent neural network, Multilayer Neural Network Perceptron (MLP), decision tree and Support Vector Machine (SVM) were used, and two-dimension reduction methods, principal component analysis (PCA) and algorithm Independent Component Analysis (ICA) was applied using Python software. In the PCA method, the final variables including crop variety, soil texture, spraying area ratio, soil electrical conductivity, drainage and nitrogen fertilizer were identified. While in the ICA method, the final variables included product variety, soil electrical conductivity, water electrical conductivity, plant age, the number of times of irrigation and soil texture. The results showed that the LSTM recurrent neural network algorithm performed better in the PCA dimension reduction method. The values of R² equal to 97%, RMSE equal to 51.79, and RRMSE equal to 0.89 were obtained for this algorithm in the PCA method, compared to the ICA method, which had values of R² equal to 91%, RMSE equal to 62.75, and RRMSE equal to 0.798., which provided better results. This shows that PCA had a better ability to reduce the dimensionality for this model.
کلیدواژه‌ها [English]
EXTENDED ABSTRACT
The yield optimization of sugarcane fields is a primary goal for agro-industry companies in Khuzestan, Iran. In recent years, advancements in data mining and machine learning have opened new avenues for enhancing agricultural productivity. This study investigates the application of deep learning techniques, particularly the Long Short-Term Memory (LSTM) recurrent neural network, to estimate sugarcane crop yields. By leveraging the capabilities of LSTM networks, the research aims to identify the most influential factors affecting farm performance and classify farms into three performance categories. This classification can provide valuable insights for improving management practices and achieving er yields. In this context, the application of deep learning algorithms, specifically LSTM recurrent neural network, presents a promising avenue for yield estimation. LSTM networks are particularly suited for time-series data, which is prevalent in agriculture due to the seasonal nature of farming activities and the longitudinal recording of environmental and operational parameters. By leveraging LSTM's ability to capture temporal dependencies and patterns, this study aims to develop a robust model for predicting sugarcane yields.
In this article, using one of the deep learning algorithms called deep recurrent neural network LSTM, the yield estimation of sugarcane crop has been done. In this research, by using LSTM deep recurrent neural network, the most influential features in the performance of farms are selected and based on them, the performance of farms is placed in one of three categories. In this regard, available data sets such as irrigation and drainage, soil and plant data were used to determine the effect of different combinations of these factors on production performance. This research is of analytical type and its database includes records of 1201 farms. The data required for this paper were obtained from Amir Kabir Sugarcane Agriculture and Industry Company during the crop years of 2014 to 2018. The analysis was done with the help of Python software. In this research, four algorithms of LSTM recurrent neural network, Multilayer Neural Network Perceptron (MLP), decision tree and Support Vector Machine (SVM) were used, and two-dimension reduction methods of Principal Component Analysis (PCA) and independent component analysis algorithm. (ICA) was also applied.
The results showed that executive and management indicators have an effect on changing the performance level of sugarcane fields. Also, crop variety and soil electrical conductivity have appeared as the most important independent variables in modeling in both algorithms; Therefore, the obtained results can help in planning and preparing optimal conditions to reach the set goals of the production rate. The results showed that the Long Short-Term Memory (LSTM) recurrent neural network algorithm performed better in the Principal Component Analysis (PCA) dimension reduction method. The values of R² equal to 0.97%, RMSE equal to 51.79, and RRMSE equal to 0.89 were obtained for this algorithm in the PCA method, which compared to the ICA method, which had values of R² equal to 0.91%, RMSE equal to 62.75, and RRMSE equal to 0.798. The results gave a better This shows that PCA had a better ability to reduce the dimensionality for this model.
This research demonstrates the potential of using LSTM deep recurrent neural networks for yield estimation in sugarcane farming. The accuracy achieved by the model underscores its applicability in real-world agricultural settings. Identifying crop variety and soil electrical conductivity as major influencers provides actionable insights for farm management. The study's results can assist agro-industry companies in Khuzestan in planning and implementing optimal conditions to achieve their production goals. Future research could expand on these findings by incorporating additional variables and exploring other advanced machine learning techniques to further enhance yield prediction accuracy.
For research articles with several authors, a short paragraph specifying their individual contributions must be provided. The following statements should be used “Conceptualization, Kimia Shirini and Adel Taheri Hajivand; methodology, Kimia Shirini; software, Kimia Shirini.; validation, Hassan Zaki Dizaji, Adel Taheri Hajivand; formal analysis, Hassan Zaki Dizaji; investigation, Hassan Zaki Dizaji, Kimia Shirini; resources, Hassan Zaki Dizaji. data curation, Kimia Shirini; writing—original draft preparation, Hassan Zaki Dizaji, Kimia Shirini; writing—review and editing, Adel Taheri Hajivand, Hassan Zaki Dizaji and Nasim Monjezi; visualization, Hassan Zaki Dizaji; supervision, Hassan Zaki Dizaji; project administration, Hassan Zaki Dizaji; funding acquisition, Hassan Zaki Dizaji. All authors have read and agreed to the published version of the manuscript.” Please turn to the CRediT taxonomy for the term explanation. Authorship must be limited to those who have contributed substantially to the work re-ported.
The data supporting the results reported in this study are available from the authors upon request. For inquiries, please contact the corresponding author.
The authors would like to appreciate the Vice Chancellor for Research and Technology of Shahid Chamran University of Ahvaz, Iran, for financial support under the special research grant number SCU.AA1402.505, and also to the R&D of Amir Kabir sugarcane Agro-Industry Company for preparing the data.
The authors would like to thank all participants of the present study.
The authors avoided data fabrication, falsification, plagiarism, and misconduct.
The author declares no conflict of interest.
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