Many sensors have emerged for different applications nevertheless only rare of the sensor are in use for agriculture field to identify soil type and nutrients specifications this provides a vast space in research. Numerous agricultural research centers are developed and are still on work as an equipped lab for monitoring these data for farmer’s necessity. Getting soil from farmers processing in the lab and resulting in the required data is a common feature but realistic field monitoring sensors are a challenging task. This framework is to develop an easy man - handle sensor for identifying parameters such as: type of the soil, water scarcity, amount of nutrient present in the soil, type of seed for plantation, fertilizer required for the growth of crop, type of diseases that may infect, crop harvesting and cost estimation after cultivation. Classification of these substantial parameters are made using machine learning techniques and to correlate each parameter with its corresponding attributes to provide continuous field monitoring effective precision agriculture is the proposal work. This work focuses on all the parameter fixed together to a sensor listing out the production and cost estimation of any field.

Machine learning, Soil nutrients, Deep learning, Fertilizers

[1] Hopfield. J. J. 1982. Neural Networks and Physical Systems with Emergent Collective Computational Abilities, In: Proceedings of the National Academy of Science of the United States of America, Vol. 79:2554–2558.
[2] Badia Melis. R et al., 2016. "Artificial neural networks and thermal image for temperature prediction in apples,"Food and Bioprocess Technology, vol.9 no.7, pp. 1089-1099.
[3] Sellers PJ, Randall DA, Collatz GJ, Berry JA, Field CB, Dazlich DA, Zhang C, Collelo GD, Nounoua L 1996. A revised land surface parameterization (SiB2) for atmospheric GCMS, Part 1: Modelformulation. Journal of Climate 9: 676–705.
[4] ASCE Task Committee on applications of artificial neural networks in hydrology (2000). Artificial Neural Networks in Hydrology I: Preliminary Concepts. Journal of Hydrol. Eng. 5: 115–123.
[5] Krause A, Singh A, Guestrin C 2008. Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms,and empirical studies. Journal of Machine Learning Research 9: 235–284.
[6] Gondal, M.D. and Y.N. Khan, Early Pest Detection from Crop using Image Processing and Computational Intelligence.
[7] Pahuja, R., H. Verma, and M. Uddin, A wireless sensor network for greenhouse climate control.IEEE Pervasive Computing, 2013. 12(2): p. 49-58.
[8] Lee, W.S. and R. Ehsani, Sensing systems for precision agriculture in Florida. Computers and Electronics in Agriculture, 2015. 112: p. 2-9.
[9] Osman J, Inglada J , Dejoux JF. Assessment of a Markov logic model of crop rotations for early crop mapping, Elsevier. Computers and Electronics in Agriculture. 2015; 113:234–43.
[10] S. Russell and P. Norvig, 2003. ArtificialIntelligence: A Modern Approach,Prentice Hall, New York.
[11] Bah, A., S.K. Balasundram and M.H.A. Husni, “Sensor Technologies for Precision Soil NutrientManagement and Monitoring”, American Journal of Agricultural and Biological Sciences 7 (1): 43-49, ISSN1557-4989, 2012.
[12] Nanyang Zhu1,2, Xu Liu1,2, Ziqian Liu1,2, Kai Hu1,2, Yingkuan Wang3, Jinglu Tan4, Min Huang1,Qibing Zhu1, Xunsheng Ji1, Yongnian Jiang5, Ya Guo1,2,4*, “ Deep learning for smart agriculture:Concepts, tools, applications, and opportunities”, Int J Agric & Biol Eng, Vol. 11 No.4, July, 2018
[13] Kaushik Bhagawati, Amit Sen, Kshitiz Kumar Shukla, Rupankar Bhagawati, “Application and Scopeof Data Mining in Agriculture “ International Journal of Advanced Engineering Research and Science(IJAERS) [Vol-3, Issue-7, July- 2016]
[14] S.Veenadhari, 2Dr. Bharat Misra, 3Dr. CD Singh, “ Data mining Techniques for PredictingCrop Productivity – A review article”, International Journal of Computer Sci ence and Technology,Vol. 2, Iss ue 1, March 2011
[15] Deepa V. Ramane, Supriya S. Patil, A. D. Shaligram, “ Detection of NPK nutrients of soil usingfiber Optic Sensor”, International Journal of Research in Advent Technology (E-ISSN: 2321-9637)Special Issue National Conference “ACGT 2015”, 13-14 February 2015