ABSTRACT
Our population is increasing day by day, and at the same time, the response to climate change has put enormous pressure on the agricultural sector to increase productivity and food production. The agricultural land is gradually reducing in most of the country and it is nearly impossible to increase it back again. Agricultural automation is the only option for precision farming in today’s era and is also the demand of today’s time. Artificial Intelligence (AI) have started contributing and capitalizing on precision farming and all industries long back. The use of digital technologies has revolutionized agriculture, which is helpful for precision farming by providing smart interfaces that can guide small farmers for new crops. Profitable crops and provide solutions for their crops-related queries. The important application of AI is to achieve a better yield, as well as to increase the quality of the crop, detection of diseases, weeds control, pest detection, an application of fertilizer at the right time, greenhouse, cultivation, crop health monitoring etc. These aspects have been discussed in this article. The main objective of this paper is how agriculture is being operated with digital technology in the field of agriculture. Through this paper, those researches have been observed, and the major applications that have been made so far in the field of agricultural science are to be identified with the help of AI.
- [1] G. S. Patel, A. Rai, N. N. Das, and R. P. Singh, Eds.,, CRC Press, Boca Raton, FL, USA, 1st edition,(2021), Smart Agriculture: Emerging Pedagogies of Deep Learning, Machine Learning and Internet of Tings.Google Scholar
- [2] R. Sujatha, J. M. Chatterjee, N. Jhanjhi, and S. N. Brohi, Microprocessors and Microsystems, vol. 80, p. 103615(2021). Performance of deep learning vs machine learning in plant leaf disease detection.Google Scholar
- [3] A. Gharaei, M. Karimi, and S. A. Hoseini ShekarabiApplied Mathematical Modelling, vol. 69, pp. 223–254, 2019, An integrated multi-product, multi-buyer supply chain under penalty, green, and quality control polices and a vendor managed inventory with consignment stock agreement: the outer approximation with equality relaxation and augmented penalty algorithm.Google ScholarCross Ref
- [4] Charlotte Doidge a, Eamonn Ferguson b, Fiona Lovatt a, Jasmeet Kaler a,* Available online 28 6November 2020 0167-5877/©(2020).Google Scholar
- [5] Rayda Ben Ayed, Mohsen Hanana, Published Hindawi Journal of food Quality, 22 April (2021), Understanding farmers’ naturalistic decision making around prophylactic antibiotic use in lambs using a grounded theory and natural languageprocessing approach, Artificial Intelligence to Improve the food and Agriculture Sector”Google Scholar
- [6] Kirtan Jha a, Aalap Doshi b, Poojan Patel c, Manan Shah d, ⁎, Artificial Intelligence in Agriculture (2019), “A comprehensive review on automation in agriculture using artificial intelligenceGoogle Scholar
- [7] A. Crane-Droesch, Environmental Research Letters, vol. 13, no. 11, p. 114003, (2018), Machine learning methods for crop yield prediction and climate change impact assessment in agriculture.Google ScholarCross Ref
- [8] A. Suprem, N. Mahalik, and K. Kim, Computer Standards & Interfaces, vol. 35, no. 4, pp. 355–364, 2013, “A review on application of technology systems, standards and interfaces for agriculture and food sector.Google ScholarDigital Library
- [9] Oludare Isaac Abiodun a, Aman Jantan b, Abiodun Esther Omolara c, Kemi Victoria Dada d, Nachaat AbdElatif Mohamed e, Humaira Arshad f, Heliyon 4 (2018), State-of-the-art in artificial neural network applications: A survey.Google Scholar
- [10] Guoming Li 1, Yanbo Huang 2, Zhiqian Chen 3, Gary D. Chesser Jr. 1,*, Joseph L. Purswell 4, John Linhoss 1 and Yang Zhao 5,*, Published: 21 February (2021), Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A Review.Google Scholar
- [11] Araújo S O, Peres R S, Barata J, Lidon F Math Ramalho J C 11(667): 1-37. https://doi.org/10.3390/agronomy11040667 ((2021), Characterising the Agriculture 4.0 Landscape-Emerging Trends, Challenges and Opportunities.Google ScholarCross Ref
- [12] Mehmet Ali DAYIOĞLUa*, Ufuk TÜRKERa, Received: 24 August 2021 / Revised: 29 October 2021 / Accepted: 01 November2021 / Online: 04 December 2021, Digital Transformation for Sustainable Future - Agriculture 4.0: A reviewGoogle Scholar
- [13] Prof. Mrs. J.R.Prasad, Prof. R.S.Prasad, Dr. U.V.Kulkarni, Proceedings of the International MultiConference of Engineers and Computer Scientists in Hong Kong, 2008 Vol I IMECS 2008, 19-21 March, 2008, A Decision Support System for Agriculture Using Natural Language Processing.Google Scholar
- [14] Nikhil Tiwari, Anmol Singh, International Conference on Computational Performance Evaluation, North-Eastern Hill University, Shillong, Meghalaya, India. July 2–4, 2020, A Novel Study of Rainfall in the Indian States and Predictive Analysis using Machine Learning Algorithms.Google Scholar
- [15] Vasileios Moysiadis, Panagiotis Sarigiannidis, Vasileios Vitsas, Adel Khelifi, 1574-0137/© [2020] Published by Elsevier Inc, Smart Farming in EuropeGoogle Scholar
- [16] Antti Raatevaara, Heikki Korpunen, Markku Tiitta, Laura Tomppo, Sampo Kuljuc, Jukka Antikainen, Jori Uusitalo, 31 July 2020, “Electrical impedance and image analysis methods in detecting and measuring Scots pine heartwood from a log end during tree harvesting.Google Scholar
- [17] Xijia Zhou, Pengxin Wang, Kevin Tansey, Shuyu Zhang, Hongmei Li, Huiren Tian, 1 August 2020, Reconstruction of time series leaf area index for improving wheat yield estimates at field scales by fusion of Sentinel-2, -3 and MODIS imagery.Google Scholar
- [18] E.M.M. van der Heide, C. Kamphuis, R.F. Veerkamp, I.N. Athanasiadis, G. Azzopardi, M.L. van Pelt, B.J. Ducro, 20 August 2020, “Improving predictive performance on survival in dairy cattle using an ensemble learning approach.Google Scholar
- [19] Hugo Storm†,*, Kathy Baylis and Thomas Heckelei, 21 August 2019, “Machine learning in agricultural and applied economics.Google Scholar
- [20] Banerjee, P.S., Chakraborty, B., Anand, U. et al, 117, 769–807 (2021). https://doi.org/10.1007/s11277-020-07896-w,. Trainable Framework for Information Extraction, Structuring and Summarization of Unstructured Data, Using Modified NER. Wireless Pers Commun.Google ScholarCross Ref
- [21] Banerjee, P.S., Chakraborty, B., Tripathi, D. et al. 108, 1909–1931 (2019). https://doi.org/10.1007/s11277-019-06501-z, A Information Retrieval Based on Question and Answering and NER for Unstructured Information Without Using SQL. Wireless Pers Commun.Google ScholarDigital Library
- [22] Banerjee, P.S., Ghosh, A., Gupta, A., Chakraborty, B. In: Hassanien, A., Bhatnagar, R., Darwish, A. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2020. Advances in Intelligent Systems and Computing, vol 1141. Springer, Singapore. Natural Language Information Extraction Through Non-Factoid Question and Answering System.Google Scholar
- [23] Yumiao Wang 1,2, Zhou Zhang 1,*, Luwei Feng 1,2, Qingyun Du 2,3,4,5 and Troy Runge 1, Received: 23 February 2020; Accepted: 10 April 2020; Published: 12 April 2020, Combining Multi-Source Data and Machine Learning Approaches to Predict Winter Wheat Yield in the Conterminous United States.Google Scholar
- [24] Thomas van Klompenburga, Ayalew Kassahuna, Cagatay Catalb, Received 29 January 2020; Received in revised form 21 July 2020; Accepted 9 August 202⁎ Corresponding author.E-mail address: [email protected] (C. Catal). Computers and Electronics in Agriculture 177 (2020) 105709 Available online 18 August 2020 0168-1699/ © 2020 Elsevier B.V. All rights reserved,,⁎, Crop yield prediction using machine learning: A systematic literature review.Google Scholar
- [25] Solemane Coulibaly a,b,1,*, Bernard Kamsu-Foguem b,2, Dantouma Kamissoko a, Daouda Traore a,3, Received 16 April 2022; Received in revised form 8 July 2022; Accepted 21 July 2022, Available online 28 July 2022, “Deep learning for precision agriculture: A bibliometric analysis.Google Scholar
- [26] Douglas K. Bolton, Mark A. Friedl, Received 14 September 2012 Received in revised form 30 December 2012 Accepted 22 January 2013, Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics.Google Scholar
- [27] Maitiniyazi Maimaitijianga, Vasit Sagana,⁎, Paheding Sidikea,b, Sean Hartlinga, Flavio Espositoc, Felix B. Fritschid, Received 17 May 2019; Received in revised form 2 December 2019; Accepted 7 December 2019, Soybean yield prediction from UAV using multimodal data fusion and deep learning.Google Scholar
- [28] Chenjie Lin 1,2, Yueming Hu 2,3,4, Zhenhua Liu 1,2, Yiping Peng 1,2, Lu Wang 2,3,4,* and Dailiang Peng 5, Received: 20 December 2021 Accepted: 5 January 2022 Published: 11 January 2022,, Estimation of Cultivated Land Quality Based on Soil Hyperspectral Data.Google Scholar
- [29] Andrew Crane-Droesch1, RECEIVED 31 May 2018 REVISED 24 August 2018 ACCEPTED FOR PUBLICATION 14 September 2018 PUBLISHED 26 October 2018, Machine learning methods for crop yield prediction and climate change impact assessment in agriculture.Google Scholar
- [30] Hughes, D. P., and Salath’e, M.. CoRR abs/1511.08060, 2015. An open access repository of images on plant health to enable the development of mobile disease diagnostics through machine learning and crowdsourcingGoogle Scholar
- [31] SSladojevic, S., Arsenovic, M., Anderla A., Culibrk, D., and Stefanovic, D. (Jun 2016), 3289801. Deep neural networks based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience.Google Scholar
- [32] Joao Paulo Schwarz Schuler, Mohamed Abdel-nasser, Santiago Romaní, June 2022, Color-Aware Two-Branch DCNN for Efficient Plant Disease Classification.Google Scholar
Index Terms
- Identifying the Suitability of Artificial Intelligence Technology for Modern Farming
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