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An efficient IoT based crop disease prediction and crop recommendation for precision agriculture

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Abstract

Internet of Things (IoT) frameworks generates data for large and remote agricultural areas through sensors and use this data for crop predictions by several machine learning algorithms. Farming is the practice of producing crops, rearing of livestock, cultivation of soil and it is important for economic development of the country. Farmers have been following traditional farming practices till now. These techniques were imprecise and reduced productivity and time consumption. Determining the steps that essential for practicing at its appropriate season helps to increase the productivity of precision farming. In this research, the primary objectives are to enhance precision farming practices by introducing a comprehensive IoT-based framework and employing advanced machine learning algorithms. Therefore, this research work introduces crop recommendation and disease prediction that helps farmers to increase productivity and reduce manual labor. The proposed Multi-level Kronecker Guided Pelican Convolutional Neural Network (MKGPCNN) focuses on crop recommendation, providing forecasts for suitable crops in the agricultural sector. Simultaneously, the Combined Graph Sample and Aggregate Attention Network (CGSAAN) are introduced for crop disease identification and recommending appropriate fertilizers to manage diseases and enhance harvests. The evaluation of both systems on publicly accessible datasets, namely the crop recommendation dataset and the new plant diseases dataset, demonstrates higher accuracy rates of 99% and 98%, precision of 99.5% and 99%, recall of 99.6% and 98.9%. The results suggest that the introduced system have the potential to significantly assist farmers in smarter crop management and harvesting, contributing to increased productivity and reduced manual labor.

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References

  1. Mishra, D., Khan, A., Tiwari, R., Upadhay, S.: Automated Irrigation System-IOT based approach. 2018 3rd International Conference Int. Things: Smart Innovation Usages (IoT-SIU). (2018)

  2. Garg, G., Gupta, S., Mishra, P., Vidyarthi, A., Singh, A., Ali, A.: CROPCARE: an intelligent real-time sustainable IOT system for crop disease detection using mobile vision. IEEE Internet Things J. 10(4), 2840–2851 (2023)

    Article  Google Scholar 

  3. Sharma, R.P., Ramesh, D., Pal, P., Tripathi, S., Kumar, C.: IOT-enabled IEEE 802.15.4 WSN monitoring infrastructure-driven fuzzy-logic-based crop pest prediction. IEEE Internet Things J. 9(4), 3037–3045 (2022)

    Article  Google Scholar 

  4. Muangprathub, J., Boonnam, N., Kajornkasirat, S., Lekbangpong, N., Wanichsombat, A., Nillaor, P.: IOT and agriculture data analysis for smart farm. Comput. Electron. Agric. 156, 467–474 (2019)

    Article  Google Scholar 

  5. Verma, P., Tiwari, R., Hong, W.-C., Upadhyay, S., Yeh, Y.-H.: Fetch: a deep learning-based fog computing and IOT integrated environment for healthcare monitoring and diagnosis. IEEE Access. 10, 12548–12563 (2022)

    Article  Google Scholar 

  6. Kishan Das Menon, H., Mishra, D., Deepa, D.: Automation and integration of growth monitoring in plants (with disease prediction) and crop prediction. Mater. Today Proc. 43, 3922–3927 (2021)

    Article  Google Scholar 

  7. Navarro, E., Costa, N., Pereira, A.: A systematic review of IOT solutions for smart farming. Sensors 20, 4231 (2020)

    Article  PubMed  PubMed Central  Google Scholar 

  8. Keswani, B., Mohapatra, A.G., Keswani, P., Khanna, A., Gupta, D., Rodrigues, J.: Improving weather dependent zone specific irrigation control scheme in IOT and big data enabled self driven precision agriculture mechanism. Enterp. Inf. Syst. 14(9–10), 1494–1515 (2020)

    Article  Google Scholar 

  9. Khan, A.A., Faheem, M., Bashir, R.N., Wechtaisong, C., Abbas, M.Z.: Internet of things (IOT) assisted context aware fertilizer recommendation. IEEE Access 10, 129505–129519 (2022)

    Article  Google Scholar 

  10. Boursianis, A.D., Papadopoulou, M.S., Diamantoulakis, P., Liopa-Tsakalidi, A., Barouchas, P., Salahas, G., Karagiannidis, G., Wan, S., Goudos, S.K.: Internet of things (IOT) and agricultural unmanned aerial vehicles (uavs) in smart farming: a comprehensive review. Internet of Things 18, 100187 (2022)

    Article  Google Scholar 

  11. Hu, W.-J., Fan, J., Du, Y.-X., Li, B.-S., Xiong, N., Bekkering, E.: MDFC–resnet: an agricultural IOT system to accurately recognize crop diseases. IEEE Access 8, 115287–115298 (2020)

    Article  Google Scholar 

  12. Mishra, M., Choudhury, P., Pati, B.: Modified ride-NN optimizer for the IOT based plant disease detection. J. Ambient. Intell. Humaniz. Comput. 12(1), 691–703 (2020)

    Article  Google Scholar 

  13. Zhao, Y., Liu, L., Xie, C., Wang, R., Wang, F., Bu, Y., Zhang, S.: An effective automatic system deployed in agricultural internet of things using multi-context fusion network towards crop disease recognition in the wild. Appl. Soft Comput. 89, 106128 (2020)

    Article  Google Scholar 

  14. Azfar, S., Nadeem, A., Ahsan, K., Mehmood, A., Almoamari, H., Alqahtany, S.S.: Iot-based cotton plant pest detection and smart-response system. Appl. Sci. 13(3), 1851 (2023)

    Article  CAS  Google Scholar 

  15. Nagasubramanian, G., Sakthivel, R.K., Patan, R., Sankayya, M., Daneshmand, M., Gandomi, A.H.: Ensemble classification and IOT-based pattern recognition for crop disease monitoring system. IEEE Internet Things J. 8(16), 12847–12854 (2021)

    Article  Google Scholar 

  16. Delnevo, G., Girau, R., Ceccarini, C., Prandi, C.: A deep learning and social IOT approach for plants disease prediction toward a sustainable agriculture. IEEE Internet Things J. 9(10), 7243–7250 (2022)

    Article  Google Scholar 

  17. Dagar, R., Som, S., Khatri, S.K.: Smart farming—IOT in agriculture. 2018 Int. Conference Inventive Res. Comput. Appl. (ICIRCA). (2018)

  18. Kumar, S., Patil, R.R., Rani, R.: Smart IOT-based pesticides recommendation system for rice diseases. In: Kulkarni, A.J., Mirjalili, S., Udgata, S.K. (eds.) Lect Notes Electr Eng, pp. 17–25. Springer, Singapore (2023)

    Google Scholar 

  19. Ramakrishnam Raju, S.V., Dappuri, B., Ravi Kiran Varma, P., Yachamaneni, M., Verghese, D.M., Mishra, M.K.: Design and implementation of smart hydroponics farming using IOT-based AI controller with mobile application system. J. Nanomater. (2022). https://doi.org/10.1155/2022/2903385

    Article  Google Scholar 

  20. Fegade, T.K., Pawar, B.V.: Crop prediction using artificial neural network and support vector machine. Data Manage. Anal. Innov.: Proc. of ICDMAI 2019(2), 311–324 (2020)

    Google Scholar 

  21. Gosai, D., Raval, C., Nayak, R., Jayswal, H., Patel, A.: Crop recommendation system using machine learning. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol. (2021). https://doi.org/10.32628/CSEIT2173129

    Article  Google Scholar 

  22. Ahmed, U., Lin, J.C.-W., Srivastava, G., Djenouri, Y.: A nutrient recommendation system for soil fertilization based on evolutionary computation. Comput. Electron. Agric. 189, 106407 (2021)

    Article  Google Scholar 

  23. Senapaty, M.K., Ray, A., Padhy, N.: IOT-enabled soil nutrient analysis and crop recommendation model for precision agriculture. Computers 12(3), 61 (2023)

    Article  Google Scholar 

  24. Kiruthika, S., Karthika, D.: IOT-based professional crop recommendation system using a weight-based long-term memory approach. Meas. Sens. 27, 100722 (2023)

    Article  Google Scholar 

  25. Shingade, S.D., Mudhalwadkar, R.P.: Sensor information-based crop recommendation system using machine learning for the fertile regions of Maharashtra. Concurr. Comput. Pract. Exp. (2023). https://doi.org/10.1002/cpe.7774

    Article  Google Scholar 

  26. Choudhury, S.S., Pandharbale, P.B., Mohanty, S.N., Jagdev, A.K.: An acquisition based optimised crop recommendation system with machine learning algorithm. ICST Trans. Scalable Info. Syst. (2023)

  27. Cristin, R., Kumar, B.S., Priya, C., Karthick, K.: Deep neural network based rider-cuckoo search algorithm for plant disease detection. Artif. Intell. Rev. 53(7), 4993–5018 (2020)

    Article  Google Scholar 

  28. Chillakuru, P., Divya, D., Ananthajothi, K.: Enhanced segmentation with optimized nine-layered CNN-based classification of leaf diseases: an automatic approach for plant disease diagnosis. Cybern. Syst. (2022). https://doi.org/10.1080/01969722.2022.2151173

    Article  Google Scholar 

  29. Sahu, K., Minz, S.: Self-adaptive-deer hunting optimization-based optimal weighted features and hybrid classifier for automated disease detection in plant leaves. Exp. Syst. 39, 7 (2022)

    Google Scholar 

  30. Sun, X., Li, G., Qu, P., Xie, X., Pan, X., Zhang, W.: Research on plant disease identification based on CNN. Cognit. Robot. 2, 155–163 (2022)

    Article  Google Scholar 

  31. Sahu, K., Minz, S.: Adaptive fusion of K-means region growing with optimized deep features for enhanced LSTM-based multi-disease classification of plant leaves. Geocarto Int. 38, 1 (2023)

    Article  Google Scholar 

  32. Sowmiya, M., Krishnaveni, S.: IOT enabled prediction of Agriculture’s plant disease using IMPROVEDΠ quantum whale optimization DRDNN approach. Measure. Sensors 27, 100812 (2023)

    Article  Google Scholar 

  33. Sanida, M.V., Sanida, T., Sideris, A., Dasygenis, M.: An efficient hybrid CNN classification model for tomato crop disease. Technologies 11, 10 (2023)

    Article  Google Scholar 

  34. Du, J., Raza, S.H., Ahmad, M., Alam, I., Dar, S.H., Habib, M.A.: Digital Forensics as advanced ransomware pre-attack detection algorithm for endpoint data protection. Secur. Commun. Netw. 2022, 1–16 (2022)

    Google Scholar 

  35. Ali, M.J., Raza, B., Shahid, A.R.: Multi-level Kronecker convolutional neural network (ML-KCNN) for glioma segmentation from multi-modal MRI volumetric data. J. Digit. Imaging 34(4), 905–921 (2021)

    Article  PubMed  PubMed Central  Google Scholar 

  36. Kusuma, P.D., Prasasti, A.L.: Guided pelican algorithm. Int. J. Intell. Eng. Syst. 15(6), 179–190 (2022)

    Google Scholar 

  37. Kumar, M.P., Poornima, B., Nagendraswamy, H.S., Manjunath, C.: Structure-preserving NPR framework for image abstraction and stylization. J. Supercomput. 77(8), 8445–8513 (2021)

    Article  Google Scholar 

  38. Tong, X., Wei, J., Sun, B., Su, S., Zuo, Z., Wu, P.: ASCU-net: attention gate, spatial and channel attention U-Net for skin lesion segmentation. Diagnostics 11(3), 501 (2021)

    Article  PubMed  PubMed Central  Google Scholar 

  39. Zhang, K., Ma, C., Xu, Y., Chen, P., Du, J.: Feature extraction method based on adaptive and concise empirical wavelet transform and its applications in bearing fault diagnosis. Measure 172, 108976 (2021)

    Google Scholar 

  40. Ding, Y., Zhao, X., Zhang, Z., Cai, W., Yang, N.: Graph sample and aggregate-attention network for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2022)

    Google Scholar 

  41. https://www.kaggle.com/datasets/atharvaingle/crop-recommendation-dataset

  42. https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset

  43. Shingade, S.D., Mudhalwadkar, R.P.: Hybrid deep-q elman neural network for crop prediction and recommendation based on environmental changes. Concurr. Comput. Pract. Exp. (2022). https://doi.org/10.1002/cpe.6991

    Article  Google Scholar 

  44. Mellit, A., Benghanem, M., Herrak, O., Messalaoui, A.: Design of a novel remote monitoring system for smart greenhouses using the internet of things and deep convolutional neural networks. Energies 14(16), 5045 (2021)

    Article  Google Scholar 

  45. de Souza, P.S., Rubin, F.P., Hohemberger, R., Ferreto, T.C., Lorenzon, A.F., Luizelli, M.C., Rossi, F.D.: Detecting abnormal sensors via machine learning: an IOT farming WSN-based architecture case study. Measure 164, 108042 (2020)

    Google Scholar 

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  1. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India

    Gunaganti Sravanthi & Nageswara Rao Moparthi

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  1. Gunaganti Sravanthi
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Sravanthi, G., Moparthi, N.R. An efficient IoT based crop disease prediction and crop recommendation for precision agriculture. Cluster Comput (2024). https://doi.org/10.1007/s10586-023-04246-w

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