Intelligent Engineering Informatics pp 633-646 | Cite as
Application of Soft Computing in Crop Management
Abstract
Indian agriculture is overwhelmed by numerous complications; some of them are usual, and some others are artificial like small and fragmented land-holdings, seeds, manures, crop selection, crop planning, fertilizers and biocides, irrigation, lack of mechanization, soil erosion, agricultural marketing, inadequate storage facilities, and so on. With the progression of different and specific outfits for the viability test of crop management are essential for providing reliable data observing to the performance of crop management. Valuable practical data can be collected by utilizing fuzzy logic-based scheme, in contrast with the intrinsic objectivity for collecting the data in gradual progression without any flaw. By dint of subject expertise and with the knowledge of scientific derivation, the approach should inspire to every corners of the country and management of cropping schemes. This paper analyzes the application of soft computing techniques in crop management in the field of farming and organic engineering is manifested. Upcoming progress and implementation using soft computing in the arena of farming and organic work to be think about.
Keywords
Crop selection Crop planning Fuzzy Logic Soft computing Crop managementReferences
- 1.Rani, A.S.: The Impact of Data Analytics in Crop Management based on Weather Conditions (2017)Google Scholar
- 2.Kumari, P.L., Reddy, G.K., Krisna, T.G.: Optimum allocation of agriculture land to the vegetable crops under uncertain profits using fuzzy multiobjective linear programming. IOSR J. Agric. Vet. Sci. 7(12), 19–28CrossRefGoogle Scholar
- 3.Ingole, K., et al.: Crop prediction and detection using fuzzy logic in Matlab. Int. J. Adv. Eng. Technol. 6(5), p. 2006 (2013)Google Scholar
- 4.Huang, Y., et al.: Development of soft computing and applications in agricultural and biological engineering. Comput. Electron. Agric. 71(2), 107–127 (2010)CrossRefGoogle Scholar
- 5.Regulwar, D.G., Gurav, J.B.: Fuzzy approach based management model for irrigation planning. J. Water Resour. Prot. 2(06), p. 545 (2010)CrossRefGoogle Scholar
- 6.Kumar, P., Singh, R.K., Shankar, R.: Efficiency measurement of fertilizer-manufacturing organizations using Fuzzy data envelopment analysis. J. Manag. Anal. (2017)Google Scholar
- 7.Sundaravalli, N., Geetha, A.: A Study & Survey on Rainfall Prediction and Production of Crops Using Data Mining Techniques (2016)Google Scholar
- 8.Jawad, F., et al.: Analysis of Optimum Crop Cultivation using Fuzzy System. In: 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS). IEEE (2016)Google Scholar
- 9.Murmu, S., Biswas, S.: Application of fuzzy logic and neural network in crop classification: A review. Aquati. Procedia 4, 1203–1210 (2015)CrossRefGoogle Scholar
- 10.Dahikar, S.S., Rode, S.V.: Agricultural crop yield prediction using artificial neural network approach. Int. J. Innov. Res. Electr. Electron. Instrum. Control Eng. 2(1), 683–686 (2014)Google Scholar
- 11.Singh, H., Sharma, N.: A Review of Fuzzy Based Expert System in Agriculture. Int. J. Eng. Sci. Res. Technol.Google Scholar
- 12.Mansourifar, M., et al.: Optimization crops pattern in variable field ownership. World Appl. Sci. J. 21(4), 492–497 (2013)Google Scholar
- 13.Waongo, M., et al.: A crop model and fuzzy rule based approach for optimizing maize planting dates in Burkina Faso, West Africa. J.Appl. Meteorol. Climatol. 53(3), 598–613 (2014)CrossRefGoogle Scholar
- 14.Houshyar, E., et al.: Sustainable and efficient energy consumption of corn production in Southwest Iran: combination of multi-fuzzy and DEA modeling. Energy 44(1), 672–681 (2012)CrossRefGoogle Scholar
- 15.Naderloo, L., et al.: Application of ANFIS to predict crop yield based on different energy inputs. Measurement 45(6), 1406–1413 (2012)CrossRefGoogle Scholar