International Journal of Advanced Technology and Engineering Exploration ISSN (Print): 2394-5443    ISSN (Online): 2394-7454 Volume-12 Issue-122 January-2025
  1. 4774
    Citations
  2. 2.8
    CiteScore
Enhancing crop recommendation with deep residual networks optimized by chronological sparrow search algorithm

D. Madhu Sudhan Reddy1 and N. Usha Rani2

Research Scholar, Department of Computer Science and Engineering,Sri Venkateswara University, Tirupati-517502, Andhra Pradesh,India1
Associate Professor, Department of Computer Science and Engineering,Sri Venkateswara University, Tirupati-517502, Andhra Pradesh,India2
Corresponding Author : D. Madhu Sudhan Reddy

Recieved : 21-March-2024; Revised : 28-January-2025; Accepted : 30-January-2025

Abstract

In India, agriculture plays a crucial role in feeding the population and boosting the country's gross domestic product. Farmers often face challenges in choosing the right crop based on soil conditions, which can greatly influence the success of their cultivation. By selecting the most suitable crop, farmers can maximise yield potential, leading to increased economic profitability. Precision agriculture (PA) utilizes machine learning (ML) techniques to address challenges in agriculture. The concept of PA enables using advanced technologies and data-driven approaches to effectively enhance agricultural production. A model for crop recommendation was developed using a deep residual network (DRN) optimized by the chronological sparrow search algorithm (CSSA). Initially, the input dataset undergoes data preprocessing, including mean imputation to handle missing values and Z-score normalization to standardize feature values. This process enhances model performance by accounting for various factors such as , content in the soil, as well as temperature and rainfall. Subsequently, the deep maxout network (DMN) with Canberra similarity performs feature fusion to reduce the dimensionality of data. The DRN is train the model using the CSSA, which optimizes hyperparameters to improve the model's performance. The CSSA–DRN model was observed to outperform other models. The proposed model achieved a precision of 85.4%, a recall of 87.5%, an F-measure of 81.3%, and an accuracy of 92.7%. The CSSA-DRN enhances crop recommendations, maximizing yield and boosting profitability.

Keywords

Crop recommendation, Precision agriculture, Chronological sparrow search algorithm, Deep residual network, Machine learning.

Cite this article

Reddy DMS, Rani NU. Enhancing crop recommendation with deep residual networks optimized by chronological sparrow search algorithm. International Journal of Advanced Technology and Engineering Exploration. 2025;12(122):147-162. DOI : 10.19101/IJATEE.2024.111100341

References
[1]
Dhanaraju M, Chenniappan P, Ramalingam K, Pazhanivelan S, Kaliaperumal R. Smart farming: internet of things (IoT)-based sustainable agriculture. Agriculture. 2022; 12(10):1-26.
[Crossref] [Google Scholar]
[2]
Sharma S, Verma K, Hardaha P. Implementation of artificial intelligence in agriculture. Journal of Computational and Cognitive Engineering. 2023; 2(2):155-62.
[Crossref] [Google Scholar]
[3]
Gulati A, Paroda R, Puri S, Narain D, Ghanwat A. Food system in India challenges, performance and promise. Science and Innovations for Food Systems Transformation. 2023: 813-28.
[Google Scholar]
[4]
Akhter R, Sofi SA. Precision agriculture using IoT data analytics and machine learning. Journal of King Saud University-Computer and Information Sciences. 2022; 34(8):5602-18.
[Crossref] [Google Scholar]
[5]
Abiri R, Rizan N, Balasundram SK, Shahbazi AB, Abdul-hamid H. Application of digital technologies for ensuring agricultural productivity. Heliyon. 2023; 9(2023):1-21.
[Google Scholar]
[6]
Younes A, Abou EZE, El MO, Abou EDE, Majid ED. The application of machine learning techniques for smart irrigation systems: a systematic literature review. Smart Agricultural Technology. 2024:1-13.
[Crossref] [Google Scholar]
[7]
Attri I, Awasthi LK, Sharma TP, Rathee P. A review of deep learning techniques used in agriculture. Ecological Informatics. 2023; 77:102217.
[Crossref] [Google Scholar]
[8]
Sudhan RDM, Rani NU. Bird’s eye view of machine learning, deep learning in agriculture. Proceedings of the 2nd Indian international conference on industrial engineering and operations management Warangal, Telangana 2022 (pp.1355-63). IEOM Society International.
[Google Scholar]
[9]
Albahar M. A survey on deep learning and its impact on agriculture: challenges and opportunities. Agriculture. 2023; 13(3):1-22.
[Crossref] [Google Scholar]
[10]
Bharman P, Saad SA, Khan S, Jahan I, Ray M, Biswas M. Deep learning in agriculture: a review. Asian Journal of Research in Computer Science. 2022; 13(2):28-47.
[Crossref] [Google Scholar]
[11]
Madhuri J, Indiramma M. Artificial neural networks based integrated crop recommendation system using soil and climatic parameters. Indian Journal of Science and Technology. 2021; 14(19):1587-97.
[Crossref] [Google Scholar]
[12]
Jyothika P, Ramana KV, Narayana CL. Crop recommendation system to maximize crop yield using deep neural network. Journal of Engineering Sciences. 2021; 12(11):119-30.
[Google Scholar]
[13]
Mythili K, Rangaraj R. Deep learning with particle swarm based hyper parameter tuning based crop recommendation for better crop yield for precision agriculture. Indian Journal of Science and Technology. 2021; 14(17):1325-37.
[Crossref] [Google Scholar]
[14]
Mythili K, Rangaraj R. Crop recommendation for better crop yield for precision agriculture using ant colony optimization with deep learning method. Annals of the Romanian Society for Cell Biology. 2021: 4783-94.
[Google Scholar]
[15]
Apat SK, Mishra J, Raju KS, Padhy N. An artificial intelligence-based crop recommendation system using machine learning. Journal of Scientific & Industrial Research. 2023; 82(5):558-67.
[Crossref] [Google Scholar]
[16]
Mahale Y, Khan N, Kulkarni K, Wagle SA, Pareek P, Kotecha K, et al. Crop recommendation and forecasting system for Maharashtra using machine learning with LSTM: a novel expectation-maximization technique. Discover Sustainability. 2024; 5(1):1-23.
[Crossref] [Google Scholar]
[17]
Varshitha DN, Choudhary S. An artificial intelligence solution for crop recommendation. Indonesian Journal of Electrical Engineering and Computer Science. 2022; 25(3):1688-95.
[Crossref] [Google Scholar]
[18]
Hasan M, Marjan MA, Uddin MP, Afjal MI, Kardy S, Ma S, et al. Ensemble machine learning-based recommendation system for effective prediction of suitable agricultural crop cultivation. Frontiers in Plant Science. 2023; 14:1-18.
[Crossref] [Google Scholar]
[19]
Manjula E, Djodiltachoumy S. Efficient prediction of recommended crop variety through soil nutrients using deep learning algorithm. Journal of Postharvest Technology. 2022; 10(2):66-80.
[Google Scholar]
[20]
Reddy DM, Neerugatti UR. A comparative analysis of machine learning models for crop recommendation in India. Revue Dintelligence Artificielle. 2023; 37(4):1081-90.
[Crossref] [Google Scholar]
[21]
Anguraj K, Thiyaneswaran B, Megashree G, Shri JP, Navya S, Jayanthi J. Crop recommendation on analyzing soil using machine learning. Turkish Journal of Computer and Mathematics Education. 2021; 12(6):1784-91.
[Google Scholar]
[22]
Ali SM, Das B, Kumar D. Machine learning based crop recommendation system for local farmers of Pakistan. Revista Geintec-Gestao Inovacao E Tecnologias. 2021; 11(4):5735-46.
[Google Scholar]
[23]
Usha RN, Gowthami G. Smart crop suggester. In advances in computational and bio-engineering: proceeding of the international conference on computational and bio engineering, 2019 (pp. 401-13). Springer International Publishing.
[Crossref] [Google Scholar]
[24]
Elbasi E, Zaki C, Topcu AE, Abdelbaki W, Zreikat AI, Cina E, et al. Crop prediction model using machine learning algorithms. Applied Sciences. 2023; 13(16):1-20.
[Crossref] [Google Scholar]
[25]
Rawat P, Bajaj M, Vats S, Sharma V. An analysis of crop recommendation systems employing diverse machine learning methodologies. In international conference on device intelligence, computing and communication technologies 2023 (pp. 619-24). IEEE.
[Crossref] [Google Scholar]
[26]
Samuel P, Sahithi B, Saheli T, Ramanika D, Kumar NA. Crop price prediction system using machine learning algorithms. Quest Journals Journal of Software Engineering and Simulation. 2020; 6(1):14-20.
[Google Scholar]
[27]
Gupta T, Maggu S, Kapoor B. Crop prediction using machine learning. IRE Journals. 2023; 6(9):279-84.
[Google Scholar]
[28]
Chandana C, Parthasarathy G. Efficient machine learning regression algorithm using naïve bayes classifier for crop yield prediction and optimal utilization of fertilizer. International Journal of Performability Engineering. 2022; 18(1):47-55.
[Crossref] [Google Scholar]
[29]
Bandara P, Weerasooriya T, Ruchirawya T, Nanayakkara W, Dimantha M, Pabasara M. Crop recommendation system. International Journal of Computer Applications. 2020; 975:22-5.
[Google Scholar]
[30]
Al-faiz MZ, Ibrahim AA, Hadi SM. The effect of Z-Score standardization (normalization) on binary input due the speed of learning in back-propagation neural network. Iraqi Journal of Information and Communication Technology. 2018; 1(3):42-8.
[Google Scholar]
[31]
Sun W, Su F, Wang L. Improving deep neural networks with multi-layer maxout networks and a novel initialization method. Neurocomputing. 2018; 278:34-40.
[Crossref] [Google Scholar]
[32]
Cha SH. Comprehensive survey on distance/similarity measures between probability density functions. International Journal of Mathematical Models and Methods in Applied Sciences. 2007; 1(4):300-7.
[Google Scholar]
[33]
Chen Z, Chen Y, Wu L, Cheng S, Lin P. Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions. Energy Conversion and Management. 2019; 198:111793.
[Crossref] [Google Scholar]
[34]
Xue J, Shen B. A novel swarm intelligence optimization approach: sparrow search algorithm. Systems Science & Control Engineering. 2020; 8(1):22-34.
[Crossref] [Google Scholar]