Review of machine learning and deep learning models in agriculture

Abstract

Machine learning (ML) refers to the processes that enable computers to think based on various learning methods. It can be also called domain which is a subset of Artificial Intelligence (AI). Deep learning (DL) has been a promising, new and modern technique for data analysis in recent years. It can be shown as the improved version of Artificial Neural Networks (ANN) which is one of the popular AI methods of today. The population of the world is increasing day by day and the importance of agriculture is also increasing in parallel. Because of this, many researchers have focused on this issue and have tried to apply machine learning and deep learning methods in agriculture under the name of smart farm technologies both to increase agricultural production and to solve some challenges of agriculture. In this study, it is aimed to give detailed information about these up-to-date studies. 77 articles based on machine learning and deep learning algorithms in the agriculture field and published in IEEE Xplore, ScienceDirect, Web of Science and Scopus publication databases between 2016 and 2020 years were reviewed. The articles were classified under five categories as plant recognition, disease detection, weed and pest detection, soil mapping-drought index, and yield forecast. They were examined in detail in terms of machine learning/deep learning architectures, data sets, performance metrics (Accuracy, Precision, Recall, F-Score, R2, MAPE, RMSE, MAE), and the obtained experimental results. Based on the examined articles, the most popular methods, used data sets/types, chosen performance criteria, and performance results among the existing studies are presented. It is seen that the number of AI-based applications related to agriculture is increasing compared to the past and the sustainability in productivity is so promising.

Keywords

• Analysis
• Artificial Intelligence
• Agriculture
• Deep Learning
• Machine Learning
• Prediction
• Smart Farm

References

1. 1. Elmas, Ç., Yapay Zekâ Uygulamaları [Artificial Intelligence Applications], Turkey: Seçkin Yayınları, 2016, s. 21. (In Turkish).
2. 2. Oleron, P., Zekâ [L’Intelligence], İletişim Yayınları/Çeviri [Translator]– Ela Güngören, 1996, s. 26. (In Turkish).
3. 3. Nabiyev, V. V., Yapay Zekâ [Artificial Intelligence], Turkey: Seçkin Yayınları, 2016, s. 25. (In Turkish).
4. 4. Gülgönül, A. and Akiş, E., Sınır Ötesi Tarım Yatırımlarının Geleceği ve Ülkelerin Sınır Ötesi Tarım Yatırımı İhtiyacının Tespiti İçin Bir Yaklaşım. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 2020. 34 (2): p. 531-553.
5. 5. Liakos, K. G., Busato, P., Moshou, D., Pearson, S. and Bochtis, D., Machine Learning in Agriculture: A Review. Sensors, 2018. 18(8): p. 2674.
6. 6. Avinash, C. Tyagi, Towards a second green revolution. Irrigation and Drainage, 2016. 65(4): p. 388-389.
7. 7. Michael, S., Ronay, A. and Thomas, H., A survey of the advancing use and development of machine learning in smart manufacturing. Journal of Manufacturing Systems, 2018. 48: p. 170-179.
8. 8. Kaya, U., Yılmaz, A. and Dikmen, Y., Sağlık alanında kullanılan derin öğrenme yöntemleri. European Journal of Science and Technology, 2019. 16: p. 792-808.

9. 9. Mitchell, T. M., Machine learning, McGrawHill, [Cited 2021, 9 April]; Available from: http://www.cs.cmu.edu/~tom/
10. 10. Goodfellow, I., Bengio. Y. and Courville, A., Deep Learning [Cited 2021, 9 April]; Available from: https://www.deeplearningbook.org/
11. 11. Schmidhuber J., Deep learnnig in neural networks: An overview. IDSIA, 2020, 61: p. 85-117.
12. 12. Şeker, A., Diri, B. and Balık, H. H., Derin öğrenme yöntemleri ve uygulamaları hakkında bir inceleme. Gazi Mühendislik Bilimleri Dergisi, 2017. 3(3): p. 47-64.
13. 13. Zhao, C., Lee, W. S. and He, D., Immature green citrus detection based on colur feature and sum of absolute transformed difference (SATD) using color images in the citrus grove. Computers and Electronics in Agriculture, 2016. 124: p. 243-253.
14. 14. Dyrmann, M., Karstoft, H. and Midtiby, H. S., Plant classification using convolutional neural networks. Biosystems Engineering, 2016. 151: p. 72-80.
15. 15. Yalcin, H., Plant phenology recognition using deep learning: Deep-Pheno. 2017 6th International Conference on Agro-Geoinformatics, 2017: p. 1-5.
16. 16. Chemura, A., Mutanga, O. and Sibanda, M., Machine learning prediction of coffee rust severity on leaves using spectroradiometer data. Tropical Plant Pathology, 2018. 43: p. 117-127.
17. 17. Siwen, F. and Jianjun, Z., Crop type identification and mapping using machine learning algorithms and Sentinel-II time series data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019. 12(9): p. 1-12.
18. 18. Tavakoli, H. and Gebbers, R., Assesing nitrogen and water status of winter wheat using a digital camera. Computers and Electronics in Agriculture, 2019. 157: p. 558-567.
19. 19. Kao, I. H. et al., Determination of Lycopersicon maturity using convolutional autoencoders. Scientia Horticulturae, 2019. 256: p. 108538.
20. 20. Gao, J. and Andrew, P. F., Deep convolutional networks for image-based Convolvulus sepium detection in sugar beet fields. Plant Methods 16, 2020. 29.
21. 21. Samrendra, K. and Sriram, K., Machine learnt image processing to predict weight and size of rice kernels. Journal of Food Engineering, 2020. 274: p. 109828.
22. 22. Jahanbakhshi, A., Momeny, M., Mahmoudi, M. and Zhang, Y. D., Classification of sour lemons based on apparent defects using stochastic pooling mechanism in deep convolutional neural networks. Scientia Horticulturae, 2020. 263: p. 109133.
23. 23. Chaudhary, A. and Kolhe, S,. A hybrid ensemble for classification in multiclass datasets: An application to oilseed disease dataset. Cımputers and Electronics in Agriculture, 2016. 124: p. 65-72.
24. 24. Perez-Bueno M. L., Pineda, M., Francisco, C. M. and Baron, M., Multicolor Fluorescence imaging as candidate for disease detection in plant phenotyping. Frontiers in Plant Science, 2016. 7: p. 1790.
25. 25. Hassanien, A. E. and Gaber, T., An improved moth flame optimization algorithm based on rough sets for tomato diseases detection. Computers and Electronics in Agriculture, 2017. 136: p. 86-96.
26. 26. Pooja, V., Das, R. and Kanchana, V., Identification of plant leaf diseases using image processing techniques. 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), 2017: pp. 130-133.
27. 27. Ma J. et al., A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Computers and Electronics in Agriculture, 2018. 154: p 18-24.
28. 28. Perez-Bueno M. L., Pineda, M., Francisco, C. M. and Baron, M., Detection of bacterial infection in melon plants by classification methods based on imaging data. Frontiers in Plant Science, 2018. 9: p. 164.
29. 29. Heim, R. et al., Detecting myrtle rust (Austropuccinia psidii) on lemon myrtle trees using spectral signatures and machine learning, Plant pathology, 2018: 67(5): p. 1114-1121.
30. 30. Kumar, S. and Sharma, B., Plant leaf disease identification using exponential spider monkey optimization. Sustainable Computing: Informatics and Systems, 2018. 28: p. 100283.
31. 31. Cruz, A., Ampatzidis, Y., Materazzi, A. and Luvisi, A., Detection of gropevine yellows symptoms in Vitis vinifera L. with artificial intelligence. Computers and Electronic in Agriculture, 2019. 157: p. 63-67.
32. 32. Singh, U. P., Chouhan, S. S. and Jain, S., Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease. IEEE Access, 2019. 7: p. 43721-43729.
33. 33. Dharmar, V., Application of Machine Learning in Detection of Blast Disease in South Indian Rice Crops. Journal of Phytology, 2019. 11(1): p. 31-37.
34. 34. Cynthia, S. T. et al., Automated Detection of Plant Diseases Using Image Processing and Faster R-CNN Algorithm. 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI), 2019: p. 1-5.
35. 35. Pantazi, X.E., Moshou, D. and Tamourido A. A., Automated leaf disease detection in different crop species through image features analysis and One Class Classifiers. Computers and Electronics in Agriculture, 2019. 156: p. 96-104.
36. 36. Kim, W. S. et al., Machine vision-based automatic disease symptom detection of oninon downy mildew. Computers and Electronics Agriculture, 2020. 168: p. 105538.
37. 37. Yang, H. et al., Experimental analysis and evaluation of wide residual networks based agricultural disease identification in smart agriculture system. J Wireless Com Network, 2019. 292.
38. 38. Sambasivam, G. and Opiyo G. G., A predictive machine learning application in agriculture: Cassave diease detection and classification with imbalanced dataset using CNNs. Egyptian Informatics Journal, 2020. 22(1): p. 27-34.
39. 39. Zhu, L. Q. et al., Hybrid deep learning for automated lepidopteran insect image classification. Oriental Insects, 2017. 51(2): p. 79-91.
40. 40. Leonardo M. M. et al., Mid-level Image Representation for Fruit Fly Identification (Diptera: Tephritidae). 2017 IEEE 13th International Conference on e-Science (e-Science), 2017: p. 202-209.
41. 41. Cheng, X. et al., Pest identification via deep residual learning in complex background. Computers and Electronics in Agriculture, 2017. 141: p. 351-356.
42. 42. dos Santos Ferreira, A., Freitas, D. M., da Silva, G. G., Pistori, H. and Folhes, M. T., Weed detection in soybean crops using ConvNets. Computers and Electronics in Agriculture, 2017. 143: p. 314-324.
43. 43. Xie C. et al., Multi-level learning features for automatic classification of field crop pests. Computers and Electronics in Agriculture, 2018. 152: p. 233-241.
44. 44. Borja E. G. et al., Machine learning for automatic rule classification of agricultural regulations: A case study in Spain. Computers and Electronics in Agriculture, 2018. 150: p. 343-352.
45. 45. Akbarzadeh, S., Paap, A., Ahderom, S., Apopei, B. and Alameh, K., Plant discrimination by SVM classifier based on spectral reflactance. Computers and Electronics in Agriculture, 2018. 148: p. 250-258.
46. 46. Ramendra, P., Deo, R. C., Li, Y. and Marasemi, T., Soil moisture forecasting by a hybrid machine learning tecnique: ELM integrated with ensemble mode decomposition. Geoderma, 2018. 330: p. 136-161.
47. 47. Huang H. et al., Deep learning versus Object-based Image Analysis (OBIA) in weed mapping of UAV imagery. International Journal of Remote Sensing, 2020. 41(9): p. 3446-3479.
48. 48. Zhang, C. et al., Automatic Identification of Center Pivot Irrigation Systems from Landsat Images Using Convolutional Neural Networks. Agriculture, 2018. 8: p. 100147.
49. 49. Dong, W. et al., Digital Mapping of Soil Available Phosphorus Supported by AI Technology for Precision Agriculture. 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics), Hangzhou, 2018: p. 1-5.
50. 50. Park, H., Kim, K. and Lee D. K., Prediction of Severe Drought Area Based on Random Forest: Using Satellite Image and Topography Data. Water, 2019. 11: p. 705.
51. 51. Termite, L. F., Garinei, A., Marini, A., Marconi, M. and Biondi, L., Combining satellite data and Machine Learning techniques for irrigation Decision Support Systems. 2019 IEEE International Workshop on Metrology for Agriculture and Forestry, 2019: p. 291-296.
52. 52. Zhang, Z. et al., Deep learning-based automatic recognition network of agricultural machinery images. Computers and Electronics in Agriculture, 2019. 166: p. 104978.
53. 53. Li, W. et al., Evaluation of Machine Learning Algorithms in Spatial Downscaling of MODIS Land Surface Temperature. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019. 12(7): p. 2299-2307.
54. 54. Knoll, F. J., Vitali, C., Harders, L. O. and Hussmann, S., Real-time classification of weeds in organic carrot production using deep learning algorithms. Computers and Electronics in Agriculture, 2019. 167: p. 105097.
55. 55. Perea, R. G., Poyato E. C., Montesinos, P. and Diaz J. A. R., Prediction of irrigation event occurence of farm level using optimal decision trees. Computers and Electronics in Agriculture, 2019. 157: p. 173-180.
56. 56. Feng, P. et al., Machine learning-based integraiton of remoted-sensed droguht factors can improve this the estimation of agricultural drought in South-Eastern Australia. Agricultural Systems, 2019. 173: p. 303-316.
57. 57. Khaledian, Y. and Miller, B. A., Selecting appropriate machine learning methods for digital soil mapping. Applied Mathematical Modelling, 2020. 81: p. 401-418.
58. 58. Rahmati, O. et al., Machine learning approaches for spatial modeling of agricultural droughts is the south-east region of Queensland Australia. Science of The Total Environment, 2020. 699: p. 134230.
59. 59. Pedrero, A. G., Saavedra, M. L., Esparragon, D. R. and Martin, C. G., Deep Learning for Automatic Outlining Agricultural Parcels: Exploiting the Land Parcel Identification System. IEEE Access, 2019. 7: p. 158223-158236.
60. 60. Su, J. et al., Machine learning-based crop drought mapping system by UAV remote sensing RBG imagery. World Scientific, 2020. 08: p. 71-83.
61. 61. Gallego, J. G. et al., Segmentation of Multiple Tree Leaves Pictures with Natural Backgrounds using Deep Learning for Image-Based Agriculture Applications, Applied Sciences, 2020. 10: p. 202.
62. 62. Liu, X. et al., A remote sensing and artificial neural network-based integrated agricultural drought index: Index development and applications. Catena, 2020. 186: p. 104934.
63. 63. Shin, J. Y., Kim, K. R. and Ha, J. C., Seasonal forecasting of daily mean air temperatures using a coupled global climate model and machine learning algorithm for field-scale agriculture management. Agricultural and Forest Meteorology, 2020. 281: p. 107858.
64. 64. Pantazi, X. E., Moshou, D. and Alexandridis, T., Wheat yield production using machine learning and advanced sensing techniques. Computers and Electronics in Agriculture, 2016. 121: p. 57-65.
65. 65. Kang, L. et al., Forecasting vegetation index based on vegetation-meteorological factor interactions with artificial neural network. 2016 Fifth International Conference on Agro-Geoinformatics, 2016: p. 1-6.
66. 66. Natarajan, R., Subramanian J. and Papageorgiou, E. I., Hybrid learning of fuzzy cognitive maps for sugarcane yield classification. Computers and Electronics in Agriculture, 2016. 127: p. 147-157.
67. 67. Diaz, I. et al., Machine learning applied to the prediction of citrus production, Spanish Journal of Agricultural Research, 2017. 15(2): p. e0205.
68. 68. Feng, Y. et al., Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data. Computers and Electronics in Agriculture, 2017. 136: p. 71-78.
69. 69. Khanal, S., Fulton, J., Klopfenstein, A., Douridas, N. and Shearer, S., Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties. Computers and Electronics in Agriculture, 2018. 153: p. 213-225.
70. 70. Sirsat, M. S., Cernadas, E., Delgado, M. F. and Barro, S., Automatic prediction of village-wise soil fertility for several nutrients in india using a wide range of regression methods. Computers and Electronics in Agriculture, 2018. 154: p. 120-133.
71. 71. Haghverdi, A., Allen, R. A. W., Leib, B. G., Prediction of cotton lint yield from phenology of crop indices using ANN. Computers and Electronics in Agriculture, 2018. 152: p. 186-197.
72. 72. Niedbala, G. et al., Application of ANN for yield modeling of winter rapeseed based on combined quantitative and qualitative data. Agronomy, 2019. 9(12): p. 781.
73. 73. Kayad, A., Sozzi, M., Gatto, S., Marinello, F. and Pirotti, F., Monitoring Within-Field Variability of corn yield using Sentinel-II and Machine learning techniques. Remote Sensing, 2019. 11(23): p. 2873.
74. 74. Kamir, E., Waldner, F. and Hochman, Z., Estimating wheat yields in Australia using climate records satellite image time series and machine learning methods. ISPRS Journal of Photogrammetry and Remote Sensing, 2019. 160: p. 124-135.
75. 75. Li, Y. et al., Soybean Seed Counting Based on Pod Image Using Two-Column Convolution Neural Network. IEEE Access, 2019. 7: p. 64177-64185.
76. 76. Khaki, S. and Wang, L., Crop yield prediction using deep neural networks. Frontiers in Plant Science, 2019. 10: p. 621.
77. 77. Filippi, P. et al., An approach to forecast grain crop yield using multi-layered, multi-farm data sets and machine learning. Precision Agriculture, 2019: p. 1015-1029.
78. 78. Ampatzidis, Y., Partel, V., Meyering, B. and Albrecht, U., Citrus rootstock evaluation utilizing UAV based remote sensing and artificial intelligence. Computers and Electronics in Agriculture, 2019. 164: p. 104900.
79. 79. Bauer, A. et al., Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production. Horticulture Research, 2019. 70.
80. 80. Garcia, M. G., Lopez, C. F., Crespo, A. B. and Espana, R. M., Extreme learning machine-based prediction of uptake of pharmaceuticals inreclaimed water irrigation lettuces in the region of murcia, spain. Biosystems Engineering, 2019. 177: p. 78-89.
81. 81. Taewon M. et al., Interpolation of greenhouse environment data using multilayer perceptron. Computers and Electronics in Agriculture, 2019. 166: p. 105023.
82. 82. Zhang, C., Di, L., Lin, L. and Guo, L., Machine-learned prediction annual crop planting in the U.S. Corn belt based on historical crop planting maps. Computers and Electronics in Agriculture, 2019. 166: p. 104989.
83. 83. Reis, M. M. et al., Empirical and learning machine approaches to estimating reference evapotranspiration based on temperature data. Computers and Electronics in Agriculture, 2019. 165: p. 104937.
84. 84. Saggi, M. D. and Sushma, J., Reference evapotranspiration estimation and modeling of the Punjab Northern India using deep learning. Computers and Electronics in Agriculture, 2019. 156: p. 387-398.
85. 85. Majeed, Y. et al., Deep learning-based segmentation for automated training of apple trees on trellis wires, Computers and Electronics in Agriculture, 2020. 170: p. 105277.
86. 86. Romeiko, X. X., Guo, Z., Pang, Y., Lee, E. K. and Zhang, X., Comparing machine learning approaches for predicting spatially explicit life cycle global warming and Eutrophication impacts from corn production. Sustainability, 2020. 12(4): p. 1481.
87. 87. Lee, E. K. et al., Projecting life-cycle environmental impacts on corn production in the U.S. Midwest under future climate scenarios using a machine learning approach. Science of The Total Environment, 2020. 714: p. 136697.
88. 88. Ren, A. et al., Machine learning driven approach towards the quality assessment of fresh fruits using non-invasive Sensing. IEEE Sensors Journal, 2020. 20(4): p. 2075-2083.
89. 89. Helfer, G. A., Barbosa J. L., dos Santos, R. and da Costa A. B., A computaional model for soil fertility prediction in ubiquitous agriculture. Computers and Electronics in Agriculture, 2020. 175: p. 105602.
90. 90. Sevli, O. and Kemaloğlu, N., Turkish sign language digits classification with CNN using different optimizers, International Advanced Researches and Engineering Journal, 2020, 4(3): p. 200-207.