Genetic Algorithm to find Most Optimum Growing Technique for Multiple-Cropping using Big-data
In the present scenario, it is extremely important for any farmer to increase his farm productivity and using multi-farming techniques; it is one of the most suitable ways to achieve that (Paudel in J Nepal Agricu Res Council 2:37–45, 2016 [1]). Many new farming techniques are being introduced to which a general farmer has no access to and hence his growth rate is monotonous. Even after having the cutting-edge technology and farming techniques, a general farmer has no access to any of it, i.e. the outreach of information technology in farming is still very low. As the number of parameters to optimize farm productivity increases, so thus the permutations of number of techniques and hence expertise is needed to analyse the best farming technique for the given scenario. Given so many existing techniques which vary over even a slight change in parameters, only the experts in farming are adaptable to them and hence it is extremely important to automate the technique generation process so as to put the capability of generating the best farming output to a non-farming expert. From the farmer’s point of view, smart farming should provide him with the best crop output in the most sustainable manner. Moreover, multiple cropping over single piece of land has become a necessity to meet the financial requirements and the demand-supply chain in the market. Due to multiple crops being planted on the same piece of land, it makes the technique generation process more ambiguous and time-consuming. To tackle this problem, a big data analysis of farming parameters might prove to be helpful (Wolfert et al. in Agricu Syst 153:69–80, 2014 [2]). Big data analysis helps in exploiting large datasets computationally to observe hidden patterns, trends and outcome of each technique. The combination of using smart farming with the big data for multiple cropping can provide the most well-analysed results and the complex patterns which are not perceivable to humans in providing the most optimum use of farming resources under the given constraints. Using an analysis algorithm over the big-dataset might be able to provide faster and precise techniques over the complex set of quantifiable parameters and widely changing constraints.
Paper
Full text
Genetic Algorithm to find Most Optimum Growing Technique for Multiple-Cropping using Big-data
Semantic Scholar · Agricultural and Food Sciences · 2019
Abstract
In the present scenario, it is extremely important for any farmer to increase his farm productivity and using multi-farming techniques; it is one of the most suitable ways to achieve that (Paudel in J Nepal Agricu Res Council 2:37–45, 2016 [1]). Many new farming techniques are being introduced to which a general farmer has no access to and hence his growth rate is monotonous. Even after having the cutting-edge technology and farming techniques, a general farmer has no access to any of it, i.e. the outreach of information technology in farming is still very low. As the number of parameters to optimize farm productivity increases, so thus the permutations of number of techniques and hence expertise is needed to analyse the best farming technique for the given scenario. Given so many existing techniques which vary over even a slight change in parameters, only the experts in farming are adaptable to them and hence it is extremely important to automate the technique generation process so as to put the capability of generating the best farming output to a non-farming expert. From the farmer’s point of view, smart farming should provide him with the best crop output in the most sustainable manner. Moreover, multiple cropping over single piece of land has become a necessity to meet the financial requirements and the demand-supply chain in the market. Due to multiple crops being planted on the same piece of land, it makes the technique generation process more ambiguous and time-consuming. To tackle this problem, a big data analysis of farming parameters might prove to be helpful (Wolfert et al. in Agricu Syst 153:69–80, 2014 [2]). Big data analysis helps in exploiting large datasets computationally to observe hidden patterns, trends and outcome of each technique. The combination of using smart farming with the big data for multiple cropping can provide the most well-analysed results and the complex patterns which are not perceivable to humans in providing the most optimum use of farming resources under the given constraints. Using an analysis algorithm over the big-dataset might be able to provide faster and precise techniques over the complex set of quantifiable parameters and widely changing constraints.