بررسی، بهینه‌سازی مصرف انرژی و مدل‌سازی عملکرد دو رقم شلتوک با الگوریتم ژنتیک-کلونی زنبور عسل مصنوعی

استفاده کارآمد از انرژی در تولید شلتوک برنج، با کاهش انتشار گازهای گلخانه‌ای، از تخریب بوم‌نظام‌های کشاورزی جلوگیری نموده و سبب توسعه کشاورزی پایدار می‌شود. هدف از این مطالعه، بررسی، مقایسه، بهینه‌سازی مصرف انرژی و مدل‌سازی عملکرد محصول در تولید دو رقم شلتوک با الگوریتم ژنتیک-کلونی زنبور عسل مصنوعی بود. داده‌ها از طریق مصاحبه با 120 کشاورز و صاحب مزرعه جمع‌آوری شد. نتایج نشان داد که در رقم مرغوب هاشمی و رقم پُرمحصول جمشیدی میانگین کل انرژی مصرفی به‌ترتیب 55.973 و 54.796 گیگاژول بر هکتار بود و میانگین کل انرژی تولیدی به‌ترتیب 30.74 و 62.52 گیگاژول بر هکتار به‌دست آمد که نشان از افزایش 2.03 برابری انرژی تولیدی در رقم جمشیدی نسبت به رقم هاشمی داشت. ارزیابی شاخص‌های r2 ،rmse ،mape ،ef و مقایسه آماری میانگین، واریانس و توزیع آماری در مدل تلفیقی الگوریتم ژنتیک-کلونی زنبور عسل مصنوعی بیانگر نتایج مطلوب الگوریتم زنبور عسل مصنوعی به‌عنوان تابع برازندگی الگوریتم ژنتیک بود. همچنین، نتایج بهینه‌سازی مصرف انرژی توسط الگوریتم ترکیبی ژنتیک-کلونی زنبور عسل مصنوعی نشان داد که بیشتر منابع مصرفی از حالت بهینه فاصله دارند که با مدیریت صحیح امکان صرفه‌جویی مصرف انرژی در رقم‌های هاشمی و جمشیدی به‌ترتیب 53.96 و 39.41 درصد وجود دارد. ارقام پُرمحصول با هدف تامین امنیّت غذایی، آبی و انرژی اصلاح شده‌اند. نتایج پژوهش حاضر، می‌تواند در شناسایی پتانسیل صرفه‌جویی انرژی در صنعت برنج کشور کمک به‌سزایی داشته باشد.

investigation, optimization of energy consumption and yield modeling of two paddy cultivars with genetic-artificial bee colony algorithm

introductionefficient use of energy in paddy production can lower greenhouse gas emissions, safeguard agricultural ecosystems, and promote the growth of sustainable agriculture. meanwhile, intelligent agriculture has come to the aid of farmers and policy-makers by harnessing cutting-edge technologies, which will lead to sustainable welfare and the comfort of human society in the present and the future. therefore, this study aimed to analyze energy consumption and production, as well as model and optimize the yield of two paddy cultivars using artificial bee colony (abc) and genetic algorithms (ga).materials and methodsextensive research data was collected by thoroughly examining documentary and library resources, as well as conducting face-to-face questionnaires with 120 paddy farmers and farm owners in rezvanshahr city, located in the province of guilan, iran, during the 2019-2020 production year. the farms consisted of 80 high-grading and 40 high-yielding paddies. the independent variables were machinery, diesel and gasoline fuels, electricity, seed, compost and straw, biocides, fertilizers, and labor. the dependent variable was paddy yield per hectare [of the farm area]. in the first step, energy consumption and production were calculated by multiplying the variables by their corresponding coefficients. in the second step, all the variables that maximize paddy yield were entered into matlab software. an artificial bee colony (abc) algorithm with a novel and straightforward elitism structure was utilized to enhance the fitness function of the genetic algorithm (ga). the sphere, repmat, and unifrnd functions were employed to determine the objective function, define the position of the bee colony, and quantify the position of the bee colony, respectively. in each generation, 900 new solutions were created, and the algorithm iterated 200 times. for the genetic algorithm, the population was defined as a double vector with a size of 100.results and discussionthe findings revealed that the hashemi (high-grading) paddy cultivar had an average energy consumption and production of 55.973 and 30.742 gj·ha-1, respectively. the jamshidi (high-yielding) paddy cultivar had an average energy consumption of 54.796 gj·ha-1 and double the energy production of the hashemi at 62.522 gj·ha-1. in both cultivars, agricultural machinery consumed the highest amount of energy, while straw consumed the lowest amount. the average energy consumption of tractors in the hashemi and jamshidi cultivars was 25.111 and 25.865 gj·ha-1, respectively, accounting for 44.862% and 47.202% of the total average consumed energy. this undoubtedly demonstrates the significant effect of this input and reflects the operators’ skill and experiential knowledge. the evaluation indexes, including r², rmse, mape, and ef, as well as statistical comparisons such as mean, std, and distribution, consistently demonstrated that the abc algorithm provided the essential conditions for the fitness function. the results of the bee-genetic algorithm optimization revealed that the majority of the consumed resources could be effectively managed on the farm to closely match optimal conditions. through this optimization, energy consumption in the hashemi and jamshidi cultivars was reduced by 53.96% and 39.41%, respectively.conclusiongiven its impressive performance and potential for minimizing energy consumption, the abc-ga algorithm offers an opportunity for policymakers in energy resource management and rice industry managers to develop innovative strategies for significantly reducing energy usage in rice production. this approach could lead to more sustainable and efficient practices in the agricultural sector.