12. Rao, A.N., Singh, R.G., Mahajan, G., Wani, S.P., Weed research issues, challenges, and opportunities in India, Crop Protection, 134, Februrary 2018.
13. DWR, 2015. Vision 2050, Directorate of Weed Research. Indian Council of Agricultural Research, Jabalpur 482 004, Madhya Pradesh, 2015.
14. Singh, R., Das, T.K., Kaur, R., et al. Weed Management in Dryland Agriculture in India for Enhanced Resource Use Efficiency and Livelihood Security. Proc. Natl. Acad. Sci., India, Sect. B Biol. Sci., 88, 1309–1322, 2018, https://doi.org/10.1007/s40011-016-0795-y.
15. Singh, B., Dhaka, A.K., Pannu, R.K., Kumar, S., Integrated weed management-a strategy for sustainable wheat production—A review. Agric. Rev., 34, 243–255, 2013.
16. Rao, A.N., Wani, S.P., Ramesha, M., Ladha, J.K., Weeds and weed management of rice in Karnataka State, India. Weed Technol., 29, 1–17, 2015a.
17. Sunitha, N. and Kalyani, D.L., Weed management in maize (Zea mays L.)—A review. Agric. Rev., 33, 70–77, 2012.
18. Vijayakumar, M., Jayanthi, C., Kalpana, R., Ravisankar, D., Integrated weed management in sorghum [Sorghum bicolor (L.) Moench]—A review. Agric. Rev., 35, 79–91, 2014.
19. Annadurai, K., Puppala, N., Angadi, S., Chinnusamy, C., Integrated weed management in the groundnut-based intercropping system—A review. Agric. Rev., 31, 11–20, 2010.
20. Nithya, C., Chinnusamy, C., Ravisankar, D., Weed management in herbicide-tolerant transgenic cotton (Gossypium hisrsutum L.)—a review. Int. J. Agric. Sci. Res.(IJASR), 3, 277–284, 2013.
21. Rao, A.N. and Nagamani, A., Integrated weed management in India— Revisited. Indian J. Weed. Sci., 42, 1–10, 2010.
22. Agarwal, R.G., Water management key to sustainable agriculture growth in India New Delhi, Financial Express, Updated: Mar 14, 2019, 3:46 AM, https://www.financialexpress.com/opinion/water-management-key-to-sustainable-agriculture-growth-in-India/1515331/.
23. Timesnow, Global water crisis: Groundwater is being pumped 70% faster than expected in North India, research claims, New Delhi, 24 February 2019, https://www.timesnownews.com/mirror-now/in-focus/article/global-water-crisis-groundwater-is-being-pumped-70-faster-than-expected-in-north-india-research-claims/371528#:~:text=Scientists%20mentioned%20that%20the%20groundwater,than%20what%20was%20estimated%20earlier.&-text=Drying%20up%20of%20groundwater%20by,underground%20 water%20is%20much%20higher.
24. Baruah, A., Artificial Intelligence in Indian Agriculture – An Industry and Startup Overview, Emerald, The AI Research and Advisory Company, 2019.
25. Mary, A., Evangeline, S., Minnang, M.R., A beginners guide for machine learning models with python environment, lambert publication, Republic of Moldova, Chisinau-2068, 2019.
26. Digital Agriculture: Farmers in India are using AI to increase crop yields, Microsoft News Center India, 7 November, 2017, https://news.microsoft.com/en-in/features/ai-agriculture-icrisat-upl-india/.
27. Anonyms, Machine Learning, IBM Cloud Education, updated 15 July 2020.
28. Adisa, O., Botai, J., Adeola, A. et al., Application of artificial neural network for predicting maize production in South Africa. Sustainability, 11, 4, 1145–1227, 2019.
29. Chary, S., Mustaffha, S., Ismail, W.I.W., Determining the yield of the crop using artificial neural network method. Int. J. Eng. Adv. Technol., 9, 1, 2959–2965, 2019.
30. Gliever, C. and Slaughter, D.C., Crop verses weed recognition with artificial neural networks, ASAE paper., 01-3104, 2001, 1–12, 2001.
31. Maier, H.R., Dandy, G.C., Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications, Environmental Modelling & Software, 15, 1, 101–124, 2000.
32. Song, H. and He, Y., Crop nutrition diagnosis expert system based on artificial neural networks. third International Conference on Information Technology and Applications (ICITA’05), Sydney, NSW, 1 (2005), pp. 357–362, 2005.
33. Singh, A., Ganapathysubramanian, B., Singh, A.K., Sarkar, S., Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci., 21, 2, 110–124, 2016.
34. Pan, S.J. and Yang, Q., A survey on transfer learning. IEEE Trans. Knowl. Data Eng., 22, 10, 1345–1359, 2010.
35. Danziger, C., The Environmental Impacts of AI and IoT In Agriculture, aitrends, January 9, 2020, https://www.aitrends.com/ai-in-agriculture/the-environmental-impacts-of-ai-and-iot-in-agriculture/.
36. Abhishek, S., AI for farmers, Indian Express, Updated: November 26, 2020.
37. Jain, P., Artificial Intelligence in Agriculture: Using Modern Day AI to Solve Traditional Farming Problems, aAnalytics Vidhya, November 4, 2020, https://www.analyticsvidhya.com/blog/2020/11/artificial-intelligence-in-agriculture-using-modern-day-ai-to-solve-traditional-farming-problems/.
38. Big data and Agriculture: A Complete Guide, talend, 2020, https://www.talend.com/resources/big-data-agriculture/.
39. Dua, A.M., Artificial Intelligence in Indian Agriculture, Bhajan Global Impact Foundation, updated Feb 2020, http://bhajanfoundation.org/knowledge/artificial-intelligence-in-indian-agriculture/.
1 * Corresponding author: [email protected]
2
Comparative Evaluation of Neural Networks in Crop Yield Prediction of Paddy and Sugarcane Crop
K. Krupavathi1*, M. Raghu Babu2 and A. Mani3
1Department of Irrigation and Drainage Engineering, Dr. NTR College of Agricultural Engineering, Bapatla, ANGRAU, India
2Department of Irrigation and Drainage Engineering, College of Agricultural Engineering, Madakasira, ANGRAU, India
3Department of Soil and Water Engineering, Dr. NTR College of Agricultural Engineering, Bapatla, ANGRAU, India
Abstract
Climate change causing extreme temperature events, erratic pattern of rainfall, droughts and floods poses serious limitations on agriculture, in turn requires regular crop monitoring and management of resources to get maximum yields. Food chain of the crops can be transformed by technological innovations, like mechanization, artificial intelligence and robotics, UAVs, sensors, Internet of Things (IoT), remote sensing, machine learning and deep learning in agriculture. The present study focused on ability of machine learning algorithm in integration with remote sensing in crop yield prediction of paddy and sugarcane crops at regional level. Crop-sensitive parameters extracted from high resolution LANDSAT 8 OLI imageries are