An Overview of Machine and Deep Learning Technologies Application in Agriculture: Opportunities and Challenges in Nigeria

Authors

  • Mohammed Aminu Umar E-Extension Department, NAERLS, Ahmadu Bello University, Zaria, Nigeria
  • Bashir Muhammad Sani E-Extension Department, NAERLS, Ahmadu Bello University, Zaria, Nigeria
  • Usman Suleiman E-Extension Department, NAERLS, Ahmadu Bello University, Zaria, Nigeria
  • Muhammad Lawal Tijjani E-Extension Department, NAERLS, Ahmadu Bello University, Zaria, Nigeria

DOI:

https://doi.org/10.56471/slujst.v4i1&2.273

Keywords:

e-Agriculture, Artificial Intelligence, Machine Learning, Deep Learning

Abstract

Globally agriculture has remained a key factor in food security, employment, and several other favorable economic indices. However, factors like rising world population, trade globalization, and climate variabilities have created the need for modernization and optimization to boost production and livelihood. Machine learning allows machines to read from a pool of available data and provide data-centric results. This has opened up a new and promising perspective. The paper examines recent proven works in machine learning technology application in agriculture to establish the modest contribution of machine learning and emerging deep learning technologies in this field to highlight the need for its adoption in the Nigerian agricultural ecosystem. Therefore, a systematic review was carried out using a categorization model of key agricultural subsectors/activities. Findings have shown a widespread of its application with significant positive impact in almost every aspect of agriculture with new works showing higher result efficiency in deep learning technologies application. Insightful recommendation from these technologies has proven capable of boosting agriculture on various fronts.  Thus, the adoption of ML/DL technologies in Nigeria’s Agriculture will go a long way in helping the country attain food sufficiency.

References

Anderl, R. (2015). Industrie 4.0 – technological approaches, use cases, and implementation. At - Automatisierungstechnik, 63(10), 753–765. https://doi.org/10.1515/auto-2015-0025

Benos, L., Tagarakis, A. C., Dolias, G., Berruto, R., Kateris, D., & Bochtis, D. (2021). Machine Learning in Agriculture: A Comprehensive Updated Review. Sensors, 21(11), 3758. https://doi.org/10.3390/s21113758

Chakrabortty, R., Pal, S. C., Sahana, M., Mondal, A., Dou, J., Pham, B. T., & Yunus, A. P. (2020). Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India. Natural Hazards, 104(2), 1259–1294. https://doi.org/10.1007/s11069-020-04213-3

Chavan, T. R., & Nandedkar, A. V. (2018). AgroAVNET for crops and weeds classification: A step forward in automatic farming. Computers and Electronics in Agriculture, 154, 361–372. https://doi.org/10.1016/j.compag.2018.09.021

Chen, H., Chen, A., Xu, L., Xie, H., Qiao, H., Lin, Q., & Cai, K. (2020). A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources. Agricultural Water Management, 240, 106303. https://doi.org/10.1016/j.agwat.2020.106303

Chen, Y., Lee, W. S., Gan, H., Peres, N., Fraisse, C., Zhang, Y., & He, Y. (2019). Strawberry Yield Prediction Based on a Deep Neural Network Using High-Resolution Aerial Orthoimages. Remote Sensing, 11(13), 1584. https://doi.org/10.3390/rs11131584

Chung, C.-L., Huang, K.-J., Chen, S.-Y., Lai, M.-H., Chen, Y.-C., & Kuo, Y.-F. (2016). Detecting Bakanae disease in rice seedlings by machine vision. Computers and Electronics in Agriculture, 121, 404–411. https://doi.org/10.1016/j.compag.2016.01.008

Coopersmith, E. J., Minsker, B. S., Wenzel, C. E., & Gilmore, B. J. (2014). Machine learning assessments of soil drying for agricultural planning. Computers and Electronics in Agriculture, 104, 93–104. https://doi.org/10.1016/j.compag.2014.04.004

Data Commons. (2018). Nigerian Statistical report 2018. https://datacommons.org/place/country/NGA?utm_medium=explore&mprop=count&popt=Person&hl=en

Dutta, R., Smith, D., Rawnsley, R., Bishop-Hurley, G., Hills, J., Timms, G., & Henry, D. (2015). Dynamic cattle behavioural classification using supervised ensemble classifiers. Computers and Electronics in Agriculture, 111, 18–28. https://doi.org/10.1016/j.compag.2014.12.002

Edureka. (2018, June 8). AI vs Machine Learning vs Deep Learning. Edureka. https://www.edureka.co/blog/ai-vs-machine-learning-vs-deep-learning/

Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318. https://doi.org/10.1016/j.compag.2018.01.009

FMARD. (2016). The National e-Agriculture Web Portal [Governmental]. www.eagriculture.gov.ng/eAgricPortal

Grinblat, G. L., Uzal, L. C., Larese, M. G., & Granitto, P. M. (2016). Deep learning for plant identification using vein morphological patterns. Computers and Electronics in Agriculture, 127, 418–424. https://doi.org/10.1016/j.compag.2016.07.003

Hansen, M. F., Smith, M. L., Smith, L. N., Salter, M. G., Baxter, E. M., Farish, M., & Grieve, B. (2018). Towards on-farm pig face recognition using convolutional neural networks. Computers in Industry, 98, 145–152. https://doi.org/10.1016/j.compind.2018.02.016

Hu, H., Pan, L., Sun, K., Tu, S., Sun, Y., Wei, Y., & Tu, K. (2017). Differentiation of deciduous-calyx and persistent-calyx pears using hyperspectral reflectance imaging and multivariate analysis. Computers and Electronics in Agriculture, 137, 150–156. https://doi.org/10.1016/j.compag.2017.04.002

IBM. (2021, November 5). Machine Learning. https://www.ibm.com/cloud/learn/machine-learning

ICTWorks. (2021). 11 AgriTech Findings From West African Smallholder Farmers. https://www.ictworks.org/11-agritech-findings-from-west-african-smallholder-farmers

Immaculate, J. H., Ebenanjar, E. P., Sivaranjani, K., & Terence, S. J. (2020). Applications of Machine Learning Algorithms in Agriculture. TEST Engineering & Management, 82, 9312–9320.

Johann, A. L., de Araújo, A. G., Delalibera, H. C., & Hirakawa, A. R. (2016). Soil moisture modeling based on stochastic behavior of forces on a no-till chisel opener. Computers and Electronics in Agriculture, 121, 420–428. https://doi.org/10.1016/j.compag.2015.12.020

Kitchenham, B., & Brereton, P. (2013). A systematic review of systematic review process research in software engineering. Information and Software Technology, 55(12), 2049–2075. https://doi.org/10.1016/j.infsof.2013.07.010

Lee, S., Hyun, Y., Lee, S., & Lee, M.-J. (2020). Groundwater Potential Mapping Using Remote Sensing and GIS-Based Machine Learning Techniques. Remote Sensing, 12(7), 1200. https://doi.org/10.3390/rs12071200

Li, P., Zha, Y., Shi, L., Tso, C.-H. M., Zhang, Y., & Zeng, W. (2020). Comparison of the use of a physical-based model with data assimilation and machine learning methods for simulating soil water dynamics. Journal of Hydrology, 584, 124692. https://doi.org/10.1016/j.jhydrol.2020.124692

Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine Learning in Agriculture: A Review. Sensors, 18(8). https://doi.org/10.3390/s18082674

Linker, R. (2018). Machine learning based analysis of night-time images for yield prediction in apple orchard. Biosystems Engineering, 167, 114–125. https://doi.org/10.1016/j.biosystemseng.2018.01.003

Maione, C., Batista, B. L., Campiglia, A. D., Barbosa, F., & Barbosa, R. M. (2016). Classification of geographic origin of rice by data mining and inductively coupled plasma mass spectrometry. Computers and Electronics in Agriculture, 121, 101–107. https://doi.org/10.1016/j.compag.2015.11.009

Mehdizadeh, S., Behmanesh, J., & Khalili, K. (2017). Using MARS, SVM, GEP and empirical equations for estimation of monthly mean reference evapotranspiration. Computers and Electronics in Agriculture, 139, 103–114. https://doi.org/10.1016/j.compag.2017.05.002

Meshram, V., Patil, K., Meshram, V., Hanchate, D., & Ramkteke, S. D. (2021). Machine learning in agriculture domain: A state-of-art survey. Artificial Intelligence in the Life Sciences, 1, 100010. https://doi.org/10.1016/j.ailsci.2021.100010

Mohammadi, K., Shamshirband, S., Motamedi, S., Petković, D., Hashim, R., & Gocic, M. (2015). Extreme learning machine based prediction of daily dew point temperature. Computers and Electronics in Agriculture, 117, 214–225. https://doi.org/10.1016/j.compag.2015.08.008

Morales, I. R., Cebrián, D. R., Blanco, E. F., & Sierra, A. P. (2016). Early warning in egg production curves from commercial hens: A SVM approach. Computers and Electronics in Agriculture, 121, 169–179. https://doi.org/10.1016/j.compag.2015.12.009

NITDA. (2020). National Adopted Village for Smart Agriculture [Government]. www.navsa.ng

Padarian, J., Minasny, B., & McBratney, A. B. (2019). Using deep learning to predict soil properties from regional spectral data. Geoderma Regional, 16, e00198. https://doi.org/10.1016/j.geodrs.2018.e00198

Pantazi, X. E., Tamouridou, A. A., Alexandridis, T. K., Lagopodi, A. L., Kontouris, G., & Moshou, D. (2017). Detection of Silybum marianum infection with Microbotryum silybum using VNIR field spectroscopy. Computers and Electronics in Agriculture, 137, 130–137. https://doi.org/10.1016/j.compag.2017.03.017

Pantazi, X.-E., Moshou, D., & Bravo, C. (2016). Active learning system for weed species recognition based on hyperspectral sensing. Biosystems Engineering, 146, 193–202. https://doi.org/10.1016/j.biosystemseng.2016.01.014

Patil, A. P., & Deka, P. C. (2016). An extreme learning machine approach for modeling evapotranspiration using extrinsic inputs. Computers and Electronics in Agriculture, 121, 385–392. https://doi.org/10.1016/j.compag.2016.01.016

Rahman, A., Smith, D. V., Little, B., Ingham, A. B., Greenwood, P. L., & Bishop-Hurley, G. J. (2018). Cattle behaviour classification from collar, halter, and ear tag sensors. Information Processing in Agriculture, 5(1), 124–133. https://doi.org/10.1016/j.inpa.2017.10.001

Raithatha, R. (2020). AgriTech in Nigeria Investment opportunities and challenges. GSM Association, London.

Sabzi, S., & Abbaspour-Gilandeh, Y. (2018). Using video processing to classify potato plant and three types of weed using hybrid of artificial neural network and partincle swarm algorithm. Measurement, 126, 22–36. https://doi.org/10.1016/j.measurement.2018.05.037

Su, Y., Xu, H., & Yan, L. (2017). Support vector machine-based open crop model (SBOCM): Case of rice production in China. Saudi Journal of Biological Sciences, 24(3), 537–547. https://doi.org/10.1016/j.sjbs.2017.01.024

van Klompenburg, T., Kassahun, A., & Catal, C. (2020). Crop yield prediction using machine learning: A systematic literature review. Computers and Electronics in Agriculture, 177, 105709. https://doi.org/10.1016/j.compag.2020.105709

Wu, H., Wiesner-Hanks, T., Stewart, E. L., DeChant, C., Kaczmar, N., Gore, M. A., Nelson, R. J., & Lipson, H. (2019). Autonomous Detection of Plant Disease Symptoms Directly from Aerial Imagery. The Plant Phenome Journal, 2(1), 190006. https://doi.org/10.2135/tppj2019.03.0006

Wu, T., Luo, J., Dong, W., Sun, Y., Xia, L., & Zhang, X. (2019). Geo-Object-Based Soil Organic Matter Mapping Using Machine Learning Algorithms With Multi-Source Geo-Spatial Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(4), 1091–1106. https://doi.org/10.1109/JSTARS.2019.2902375

Yang, Q., Xiao, D., & Lin, S. (2018). Feeding behavior recognition for group-housed pigs with the Faster R-CNN. Computers and Electronics in Agriculture, 155, 453–460. https://doi.org/10.1016/j.compag.2018.11.002

Zhang, M., Li, C., & Yang, F. (2017). Classification of foreign matter embedded inside cotton lint using short wave infrared (SWIR) hyperspectral transmittance imaging. Computers and Electronics in Agriculture, 139, 75–90. https://doi.org/10.1016/j.compag.2017.05.005

Zhou, Y., Luo, J., Feng, L., Yang, Y., Chen, Y., & Wu, W. (2019). Long-short-term-memory-based crop classification using high-resolution optical images and multi-temporal SAR data. GIScience & Remote Sensing, 56(8), 1170–1191. https://doi.org/10.1080/15481603.2019.1628412

Published

2022-07-20