Modeling of peach production energy using machine learning in Nazarabad township, Alborz province

Document Type : Research Paper


Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran


Today, providing food security for the world's growing population by preserving the earth's resources & minimal environmental effects has become one of the basic & important challenges in sustainable agriculture, & the optimal use of resources is one of the main requirements of sustainable agriculture. In this study, the pattern of energy consumption during peach production, analysis & modeling of energy & performance of peach production in Nazarabad city was investigated. Data were collected through interviews & filling specialized questionnaires. The results showed that the total energy consumption & production were equal to 72716.83 & 5234.89 megajoules per hectare, respectively. Electricity was the most consumed input with a share of 59% of the total input energy. The indices of energy efficiency, energy efficiency, energy intensity and net energy were obtained as 0.07, (kg/MJ) 0.03, (MJ/Kg) 26.39 and (MJ) -67481 respectively. Modeling was done with three methods: enhanced gradient regression, decision tree regression, and random forest regression, and RRMSE was -0.003, -0.0090, and -0.0091, and R2 was 0.98, 0.95, and 90, respectively was calculated. The results showed that the enhanced gradient method is able to accurately predict the values of the energy efficiency indices of peach production. The results showed that the energy efficiency and production can be predicted with high accuracy through the inputs of irrigation water, electricity, chemical and animal fertilizers, labor force, chemical poisons, diesel fuel and machines and machine learning method. Sensitivity analysis was performed with SHAP and the results showed that the most influential input in energy prediction was nitrogen fertilizer.


Main Subjects

Modeling of peach production energy using machine learning in Nazarabad township, Alborz province





it is predicted that by 2050, the world's population will reach 9.2 million people, and Iran will be one of the 20 most populous countries in the world this year. Also, according to the report of the Food and Agriculture Organization of the United Nations (FAO), in order to provide food for the people of the world in 2050, the production of products must increase by 70% during this time. Agriculture is also the most important food producer, not only energy consumer, but also the most important energy supplier. Securing the food security of the world's growing population with conservation of earth's resourcesand environmental is one of the basic and important challenges of sustainable agriculture Therefore, It has gone towards the use of artificial intelligence and machine technologies to produce healthier and more products with the least use of resources

Research Method:

In this research, modeling the energy and performance of peach production in Nazarabad city was done using machine learning And the pattern of energy consumption in peach production was investigated. Data were collected through interviews and specialized questionnaires, and simple random sampling was used to determine the sample size. Cochran's formula was used to determine the sample size, and three algorithms of Gradient boostingregression (GBR), decision tree (DTR) and random forest (RFR) were used for prediction and modeling.


The results showed that the total consumed and produced energy is equal to 72716.83 and 5234.89 megajoules respectively on the hectares and renewable energyis 8.6%, Non-renewable energy is 91.39%, indirect energy is 86.21% and direct energy is 13 percent. of the total energy consumption, Electricity was the most consumed input with a share of 59% of the total input energy. The indices of energy efficiency, energy efficiency, energy intensity and net energy were obtained as 0.07, (kg/MJ) 0.03, (MJ/kg) 26.39 and (MJ) 67481 respectively. Modeling was done with three methods: Gradient boostingRegression (GBR), Decision Tree Algorithm (DTR) and Random Forest Algorithm (RFR) and RRMSE was -0.003, -0.0090 and -0.0091 and R2 was 0.98, 0.95 and 0.90 respectively. The calculation of the results showed that the GBR method can predict energy consumption of peaches with higher accuracy than the energy indices and for RRMSE performance 0.04, 0.039 and 0.033 respectively and R 0.67, 0.47 respectively and 0.74 was calculated. The results showed that the RFR method is able to predict Peach production more accurately.


The results showed that the amounts of energy consumption and production of peaches can be more accurately predicted with the inputs of irrigation water, electricity, chemical and animal fertilizers, labor, chemical poisons, diesel fuel and machinery and machine learning methods. Sensitivity analysis was done with SHAP analysis and the results showed that nitrogen fertilizer and machinery are two important features of input parameters for energy prediction and the most effective fertilizers in peach production are phosphate, nitrogen and potassium respectively and insecticides have the least effect on yield.





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