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


  • 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



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


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.


Anderl, R. (2015). Industrie 4.0 – technological approaches, use cases, and implementation. At - Automatisierungstechnik, 63(10), 753–765.

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.

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.

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.

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.

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.

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.

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.

Data Commons. (2018). Nigerian Statistical report 2018.

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.

Edureka. (2018, June 8). AI vs Machine Learning vs Deep Learning. Edureka.

Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318.

FMARD. (2016). The National e-Agriculture Web Portal [Governmental].

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.

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.

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.

IBM. (2021, November 5). Machine Learning.

ICTWorks. (2021). 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.

Kitchenham, B., & Brereton, P. (2013). A systematic review of systematic review process research in software engineering. Information and Software Technology, 55(12), 2049–2075.

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.

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.

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

Linker, R. (2018). Machine learning based analysis of night-time images for yield prediction in apple orchard. Biosystems Engineering, 167, 114–125.

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.

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.

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.

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.

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.

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

Padarian, J., Minasny, B., & McBratney, A. B. (2019). Using deep learning to predict soil properties from regional spectral data. Geoderma Regional, 16, 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.

Pantazi, X.-E., Moshou, D., & Bravo, C. (2016). Active learning system for weed species recognition based on hyperspectral sensing. Biosystems Engineering, 146, 193–202.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.