Open Access Open Access  Restricted Access Subscription Access

Agri-Crop Intelligent System for Detecting Crop Disease and Recommending Soil Nutrition Value Based on Soil Testing Using Machine Learning

R. Raja Sekar, K. Bavani, Nagaraju B., Anurag Sinha, Jibran Gulzar, Siva Sai Krishna S., Yarrapathruni Shanmukha Kumar

Abstract


This research examines the economic importance of agriculture for nations like India as well as the ways in which innovation might advance agriculture. In order to assist farmers in increasing their production, the application can classify leaf diseases by evaluating provided photos and that will propose compatible crops and fertilisers according to soil characteristics and current meteorological data. The aim of this research is to develop a website that will help farmers detect plant diseases, prescribe fertiliser, and produce better crops. We will give soil nutrition values according to the results of the soil testing to improve crop recommendations.


Keywords


Pooling, padding, Relu, dropout, dense, CNN, random forest, Naïve Bayes, logistic regression, XGBoost

Full Text:

PDF

References


Sawant D, Jaiswal A, Singh J, Shah P. AgriBot-An intelligent interactive interface to assist farmers in agricultural activities. In 2019 IEEE Bombay section signature conference (IBSSC). 2019 Jul 26; 1–6.

Mendes J, Pinho TM, Neves dos Santos F, Sousa JJ, Peres E, Boaventura-Cunha J, Cunha M, Morais R. Smartphone applications targeting precision agriculture practices—A systematic review. Agronomy. 2020 Jun 16; 10(6): 855.

Javaid M, Haleem A, Singh RP, Suman R. Enhancing smart farming through the applications of Agriculture 4.0 technologies. International Journal of Intelligent Networks (IJIN). 2022 Jan 1; 3: 150–64.

Sinha BB, Dhanalakshmi R. Recent advancements and challenges of Internet of Things in smart agriculture: A survey. Future Gener Comput Syst. 2022 Jan 1; 126: 169–84.

Hassan SI, Alam MM, Illahi U, Al Ghamdi MA, Almotiri SH, Su’ud MM. A systematic review on monitoring and advanced control strategies in smart agriculture. IEEE Access. 2021 Feb 8; 9: 32517–48.

Kulkarni O. Crop disease detection using deep learning. In 2018 IEEE 4th international conference on computing communication control and automation (ICCUBEA). 2018 Aug 16; 1–4.

Blesslin Sheeba T, Anand LD, Manohar G, Selvan S, Wilfred CB, Muthukumar K, Padmavathy S, Ramesh Kumar P, Asfaw BT. Machine Learning Algorithm for Soil Analysis and Classification of Micronutrients in IoT-Enabled Automated Farms. J Nanomater. 2022 Jun 8; 2022: 5343965.

Kanuru L, Tyagi AK, Aswathy SU, Fernandez TF, Sreenath N, Mishra S. Prediction of pesticides and fertilizers using machine learning and Internet of Things. In 2021 IEEE International Conference on Computer Communication and Informatics (ICCCI). 2021 Jan 27; 1–6.

Kannan E. An Efficient Deep Neural Network for Disease Detection in Rice Plant Using XGBOOST Ensemble Learning Framework. Int J Intell Syst Appl Eng. 2022 Oct 1; 10(3): 116–28.

Deepa K, Karthi M, Kavin P, Rahulsankar S, Vengaimani E. A Prediction System for Agricultural Crops Using Supervised Learning. In Computer Networks and Inventive Communication Technologies: Proceedings of 5th ICCNCT 2022. 2022 Oct 14; 433–444. Singapore: Springer Nature Singapore.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Journal of Computer Technology & Applications