Applications of Artificial Intelligence in Agriculture: A Review

N. C. Eli-Chukwu

Abstract


The application of Artificial Intelligence (AI) has been evident in the agricultural sector recently. The sector faces numerous challenges in order to maximize its yield including improper soil treatment, disease and pest infestation, big data requirements, low output, and knowledge gap between farmers and technology. The main concept of AI in agriculture is its flexibility, high performance, accuracy, and cost-effectiveness. This paper presents a review of the applications of AI in soil management, crop management, weed management and disease management. A special focus is laid on the strength and limitations of the application and the way in utilizing expert systems for higher productivity.


Keywords


artificial intelligence; agriculture; soil management; crop management; disease management; weed management; yield

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References


M. A. Kekane, “Indian agriculture-status, importance and role in Indian economy”, International Journal of Agriculture and Food Science Technology, Vol. 4, No. 4, pp. 343-346, 2013

B. F. Johnston, P. Kilby, Agriculture and Structural Transformation: Economic Strategies in Late-Developing Countries, Oxford University Press, 1975

S. Kuznets, “Modern economic growth: Findings and reflections”, American Economic Association, Vol. 63, No. 3, pp. 247–258, 1973

M. Syrquin, “Patterns on Structural Change”, in: Handbook of Development Economics, Vol. 1, Elsevier, 1988

R. Dekle, G. Vandenbroucke, “A quantitative analysis of China’s structural transformation”, Journal of Economic Dynamics and Control, Vol. 36, No. 1, pp. 119-135, 2012

M. Fan, J. Shen, L. Yuan, R. Jiang, X. Chen, W. J. Davies, F. Zhang, “Improving crop productivity and resource use efficiency to ensure food security and environmental quality in China”, Journal of Experimental Botany, Vol. 63, No. 1, pp. 13-24, 2012

O. Oyakhilomen, R. G. Zibah, “Agricultural production and economic growth in Nigeria: Implication for rural poverty alleviation”, Quarterly Journal of International Agriculture, Vol. 53, No. 3, pp. 207-223, 2014

T. O. Awokuse, “Does Agriculture Really Matter for Economic Growth in Developing Countries?”, The American Agricultural Economics Association Annual Meeting, Milwaukee, Newark, USA, July 28, 2009

O. Badiene, Sustaining and Accelerating Africa’s Agricultural Growth Recovery in the Context of Changing Global Food Prices, IFPRI Policy Brief 9, 2008

S. Block, C. Timmer, Agriculture and Economic Growth: Conceptual Issues and the Kenyan Experience, Harvard Institute for International Development, 1994

C. R. D. Kimpe, J. L. Morel, “Urban soil management: A growing concern”, Soil Science, Vol. 165, No. 1, pp. 31-40, 2000

M. Pagliai, N. Vignozzi, S. Pellegrini, “Soil structure and the effect of management practices”, Soil and Tillage Research, Vol. 79, No. 2, pp. 131-143, 2004

G. S. Abawi, T. L. Widmer, “Impact of soil health management practices on soilborne pathogens, nematodes and root diseases of vegetable crops”, Applied Soil Ecology, Vol. 15, No. 1, pp. 37-47, 2000

J. K. Syers, Managing Soil for Long-Term Productivity, The Royal Society, 1997

M. Li, R. Yost, “Management-oriented modelling: Optimizing nitrogen management with artificial intelligence”, Agricultural Systems, Vol. 65, No. 1, pp. 1-27, 2000

E. M. Lopez, M. Garcia, M. Schuhmacher, J. L. Domingo, “A fuzzy expert system for soil characterization”, Environment International, Vol. 34, No. 7, pp. 950-958, 2008

H. Montas, C. A. Madramootoo, “A Decision Support System for Soil Conservation Planning”, Computers and Electronics in Agriculture, Vol. 7, No. 3, pp. 187-202, 1992

S. Tajik, S. Ayoubi, F. Nourbakhsh, “Prediction of soil enzymes activity by digital terrain analysis: Comparing artificial neural network and multiple linear regression models”, Environmental Engineering Science, Vol. 29, No. 8, pp. 798-806, 2012

E. R. Levine, D. S. Kimes, V. G. Sigillito, “Classifying soil structure using neural networks”, Ecological Modelling, Vol. 92, No. 1, pp. 101-108, 1996

M. Bilgili, “The use of artificial neural network for forecasting the monthly mean soil temperature in Adana, Turkey”, Turkish Journal of Agriculture and Forestry, Vol. 35, No. 1, pp. 83-93, 2011

Z. Zhao, T. L. Chow, H. W. Rees, Q. Yang, Z. Xing, F. R. Meng, “Predict soil texture distributions using an artificial neural network model”, Computers and Electronics in Agriculture, Vol. 65, No. 1, pp. 36-48, 2009

A. Elshorbagy, K. Parasuraman, “On the relevance of using artificial neural networks for estimating soil moisture content”, Journal of Hydrology, Vol. 362, No. 1-2, pp. 1-18, 2008

D. H. Chang, S. Islam, “Estimation of soil physical properties using remote sensing and artificial neural network”, Remote Sensing of Enviroment, Vol. 74, No. 3, pp. 534-544, 2000

T. Behrens, H. Forster, T. Scholten, U. Steinrucken, E. D. Spies, M. Goldschmitt, “Digital soil mapping using artificial neural networks”, Journal of Plant Nutrition and Soil Science, Vol. 168, No. 1, pp. 21-33, 2005

M. Kim, J. E. Gilley, “Artificial neural network estimation of soil erosion and nutrient concentrations in runoff from land application areas”, Computers and Electronics in Agriculture, Vol. 64, No. 2, pp. 268-275, 2008

M. S. Moran, Y. Inoue, E. M. Barnes, “Opportunities and limitations for image-based remote sensing in precision crop management”, Remote Sensing of Enviroment, Vol. 61, No. 3, pp. 319-346, 1997

P. Debaeke, A. Aboudrare, “Adaptation of crop management to water-limited environments”, European Journal of Agronomy, Vol. 21, No. 4, pp. 433-446, 2004

C. Aubry, F. Papy, A. Capillon, “Modelling decision-making processes for annual crop management”, Agricultural Systems, Vol. 56, No. 1, pp. 45-65, 1998

R. E. Plant, “An artificial intelligence based method for scheduling crop management actions”, Agricultural Systems, Vol. 31, No. 1, pp. 127-155, 1989

H. Lal, J. W. Jones, R. M. Peart, W. D. Shoup, “FARMSYS-A whole-farm machinery management decision support system”, Agricultural Systems, Vol. 38, No. 3, pp. 257-273, 1992

S. S. Snehal, S. V. Sandeep, “Agricultural crop yield prediction using artificial neural network approach”, International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, Vol. 2, No. 1, pp. 683-686, 2014

T. Pilarski, M. Happold, H. Pangels, M. Ollis, K. Fitzpatrick, A. Stentz, The Demeter System for Automated Harvesting, Springer, 2002

E. J. V. Henten, J. Hemming, B. A. J. V. Tuijl, J. G. Kornet, J. Meuleman, J. Bontsema, E. A. V. Os, An Autonomous Robot for Harvesting Cucumbers in Greenhouses, Springer, 2002

H. Song, Y. He, “Crop Nutrition Diagnosis Expert System Based on Artificial Neural Networks”, 3rd International Conference on Information Technology and Applications, Sydney, Australia, July 4–7, 2005

E. I. Papageorgiou, A. T. Markinos, T. A. Gemtos, “Fuzzy cognitive map based approach for predicting crop production as a basis for decision support system in precision agriculture application”, Applied Soft Computing, Vol. 11, No. 4, pp. 3643-3657, 2011

X. Dai, Z. Huo, H. Wang, “Simulation of response of crop yield to soil moisture and salinity with artificial neural network”, Field Crops Research, Vol. 121, No. 3, pp. 441-449, 2011

C. C. Yang, S. O. Prasher, J. A. Landry, H. S. Ramaswamy, “Development of herbicide application map using artificial neural network and fuzzy logic”, Agricultural Systems, Vol. 76, No. 2, pp. 561-574, 2003

B. Ji, Y. Sun, S. Yang, J. Wan, “Artificial neural networks for rice yield prediction in mountainous regions”, Journal of Agricultural Science, Vol. 145, No. 3, pp. 249-261, 2007

BEA, Value Added by Industry as a Percentage of Gross Domestic Product, available at: https://apps.bea.gov/iTable/iTable.cfm?ReqID=51

&step=1#reqid=51&step=51&isuri=1&5114=a&5102=5, 2018

Weed Science Society of America, Facts About Weeds, available at: http://wssa.net/wp-content/uploads/WSSA-Fact-SheetFinal.pdf

J. Fang, C. Zhang, S. Wang, “Application of Genetic Algorithm (GA) Trained Artificial Neural Network to Identify Tomatoes with Physiological Diseases”, International Conference on Computer and Computing Technologies in Agriculture, Wuyishan, China, August 18-20, 2007

K. Balleda, D. Satyanvesh, N. V. S. S. P. Sampath, K. T. N. Varma, P. K. Baruah, “Agpest: An Efficient Rule-Based Expert System to Prevent Pest Diseases of Rice & Wheat Crops”, 8th International Conference on Intelligent Systems and Control, Coimbatore, India, January 10–11, 2014

J. Jesus, T. Panagopoulos, A. Neves, “Fuzzy Logic and Geographic Information Systems for Pest Control in Olive Culture”, 4th IASME/WSEAS International Conference on Energy, Environment, Ecosystems & Sustainable Development, Algarve, Portugal, June 11–13, 2008

S. Kolhe, R. Kamal, H. S. Saini, G. K. Gupta, “A web-based intelligent disease-diagnosis system using a new fuzzy-logic based approach for drawing the interferences in crops”, Computers and Electronics in Agriculture, Vol. 76, No. 1, pp. 16-27, 2011

S. Kolhe, R. Kamal, H. S. Saini, G. K. Gupta, “An intelligent multimedia interface for fuzzy-logic based inference in crops”, Expert Systems with Applications, Vol. 38, No. 12, pp. 14592-14601, 2011

M. Y. Munirah, M. Rozlini, Y. M. Siti, “An Expert System Development: Its Application on Diagnosing Oyster Mushroom Diseases”, 13th International Conference on Control, Automation and Systems, Gwangju, South Korea, October 20-23, 2013

G. Liu, X. Yang, Y. Ge, Y. Miao, “An Artificial Neural Network–Based Expert System for Fruit Tree Disease and Insect Pest Diagnosis”, International Conference on Networking, Sensing and Control, Lauderdale, USA, April 23–25, 2006

F. Siraj, N. Arbaiy, “Integrated Pest Management System Using Fuzzy Expert System”, Knowledge Management International Conference & Exhibition, Kuala Lumpur, Malaysia, June 6–8, 2006

P. Virparia, “A Web Based Fuzzy Expert System for Insect Pest Management in Groundnut Crop ‘Prajna’”, Journal Of Pure & Applied Sciences, Vol. 15, pp. 36-41, 2007

X. Wang, M. Zhang, J. Zhu, S. Geng, “Spectral prediction of phytophthora infestans infection on tomatoes using artificial neural network”, International Journal of Remote Sensing, Vol. 29, No. 6, pp. 1693-1706, 2006

K. N. Harker, “Survey of yield losses due to weeds in central Alberta”, Canadian Journal of Plant Science, Vol. 81, No. 2, pp. 339–342, 2001

M. Khan, N. Haq, “Wheat crop yield loss assessment due to weeds”, National Agricultural Research Cen intensification tre, Vol. 18, No. 4, pp. 449–453, 2002

S. Fahad, S. Hussain, B. S. Chauhan, S. Saud, C. Wu, S. Hassan, M. Tanveer, A. Jan, J. Huang, “Weed growth and crop yield loss in wheat as influenced by row spacing and weed emergence times”, Crop Protection, Vol. 71, pp. 101–108, 2015

A. N. Rao, S. P. Wani, J. K. Ladha, Weed Management Research in India-An Analysis of the Past and Outlook for Future, ICAR, 2014

A. Datta, H. Ullah, N. Tursun, T. Pornprom, S. Z. Knezevic, B. S. Chauhan, “Managing weeds using crop competition in soybean [Glycine max(L.) Merr.]”, Crop Protection, Vol. 95, pp. 60–68, 2017

T. Mruthul, Chemical weed management in sesame (Sesamum indicum L.), MSc Thesis, College of Agriculture, Raichur, University of Agricultural Sciences, 2015

C. J. Swanton, R. Nkoa, R. E. Blackshaw, “Experimental methods for crop-weed competition studies”, Weed Science Society of America, Vol. 63, No. 1, pp. 2–11, 2015

P. Jha, V. Kumar, R. K. Godara, B. S. Chauhan, “Weed management using crop competition in the United States: A Review”, Crop Protection, Vol. 95, pp. 31–37, 2017

P. Milberg, E. Hallgren, “Yield loss due to weeds in cereals and its large-scale variability in Sweden”, Field Crops Research, Vol. 86, No. 2–3, pp. 199–209, 2004

C. J. Swanton, K. N. Harker, R. L. Anderson, “Crop losses due to weeds in Canada”, Weed Technology, Vol. 7, No. 2, pp. 537–542, 1993

A. M. Tobal, S. A. Mokhtar, “Weeds identification using evolutionary artificial intelligence algorithm”, Journal of Computer Science, Vol. 10, No. 8, pp. 1355-1361, 2014

P. Moallem, N. Razmjooy, “A multi-layer perception neural network trained by invasive weed optimization for potato color image segmentation”, Trends in Applied Sciences Research, Vol. 7, No. 6, pp. 445-455, 2012

M. Brazeau, “Fighting Weeds: Can we Reduce, or Even Eliminate, Herbicides by Utilizing Robotics and AI”, available at: https://geneticliteracyproject.org/2018/12/12/fighting-weeds-can-we-reduce-or-even-eliminate-herbicide-use-through-robotics-and-ai/, 2018

M. P. Ortiz, P. A. Gutierrez, J. M. Pena, J. T. Sanchez, F. L. Granados, C. H. Martinez, “Machine Learning Paradigms for Weed Mapping Via Unmanned Aerial Vehicles”, Symposium Series on Computational Intelligence, Athens, Greece, December 6–9, 2016

L. Stigliani, C. Resina, “Seloma: Expert system for weed management in herbicide-intensive crops”, Weed Technology, Vol. 7, No. 3, pp. 550-559, 1993

Y. Karimi, S. O. Prasher, R. M. Patel, S. H. Kim, “Application of support vector machine technology for weed and nitrogen stress detection in corn”, Computers and Electronics in Agriculture, Vol. 51, No. 1-2, pp. 99-109, 2006

R. Gerhards, S. Christensen, “Real-time weed detection, decision-making and patch-spraying in maize, sugarbeet, winter wheat and winter barley”, Wiley Online Library, Vol. 43, No. 6, pp. 385-392, 2003

F. L. Granados, “Weed detection for site-specific weed management: Mapping and real-time approaches”, Weed Research, Vol. 51, No. 1, pp. 1-11, 2011

C. C. Yang, S. O. Prasher, J. Laundry, H. S. Ramaswamy, “Development of neural networks for weed recognition in corn fields”, American Society of Agricultural and Biological Engineers, Vol. 45, No. 3, pp. 859-864, 2002

E. G. Rajotte, T. Bowser, J. W. Travis, R. M. Crassweller, W. Musser, D. Laughland, C. Sachs, “Implementation and Adoption of an Agricultural Expert System: The Penn State Apple Orchard Consultant”, in: International Symposium on Computer Modelling in Fruit Research and Orchard Management, ISHS, 1992

S. L. Teal, A. I. Rudnicky, “A Performance Model of System Delay and User Strategy Selection”, Conference on Human Factors in Computing Systems, California, USA, May 3-7, 1992

R. Washington, B. H. Roth, “Input Data Management in Real-Time AI System”, 11th International Joint Conference on Artificial Intelligence, Michigan, USA, August 20-25, 1989

P. Mowforth, I. Bratko, AI and Robotics: Flexibility and Integration, Cambridge University Press, 1987

D. G. Panpatte, Artificial Intelligence in Agriculture: An Emerging Era of Research, Anand Agricultural University, 2018

T. Duckett, S. Pearson, S. Blackmore, B. Grieve, Agricultural Robotics: The Future of Robotic Agriculture, UK-RAS, 2018




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