A Smart Prediction Platform for Agricultural Crops Using Machine Learning

Ammar Rafiq; Muhammad Usman  Younus; Syed Aqeel Haider; Kalsom Safdar; Najaf Ali; Nusrat Husain; Muhammad Asgher Nadeem; Zeeshan Arfeen; Faisal Mumtaz

Authors

Ammar Rafiq Department of Computer Sciences, NFC-Institute of Engineering and Fertilizers Research Faisalabad, Pakistan
Muhammad Usman Younus 2Department of Computer Science and IT, Baba Guru Nanak University, Nankana Sahib, Pakistan. Ecole Math´ematiques, Informatique, T´el´ecommunications de Toulouse, 31000 Universit´e de Toulouse, France.
Syed Aqeel Haider Department of Computer & Information Systems Engineering, Faculty of Computer & Electrical Engineering, N.E.D. University of Engineering and Technology, Karachi 75270, Pakistan.
Kalsom Safdar Department of Computer Science and IT, University of Jhang, Jhang, Pakistan. Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis (UniMAP), 02600 Perlis, Malaysia
Najaf Ali School of Computer Science and Technology. University of Science and Technology of China.
Nusrat Husain Department of Electronics & Power Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.
Muhammad Asgher Nadeem Department of Computer Science and IT, Thal University Bhakhar, Punjab – Pakistan.
Zeeshan Arfeen Department of Electrical Engineering, The Islamia University of Bahawalpur (IUB), Bahawalpur - 63100, Pakistan.
Faisal Mumtaz Department of Computer Science and IT, University of Jhang, Jhang, Pakistan.

Keywords:

Irrigation, Fertilizer, Economic, Climate, Kaggle, Soil

Abstract

It is very critical to have the economic development of emerging countries, like Pakistan. Pakistan, while being one of the world’s main suppliers of a wide range of commodities, continues to employ traditional techniques. Pakistani farmers have challenges not just in coping with changing climatic circumstances, but also in meeting increased demands for higher food output of excellent quality. Farmers must be mindful of shifting meteorological circumstances to produce quality crops. Operations are greatly affected by a variety of factors, including the availability of water, the type of soil, the climate, and fertilizer. Farmers in conventional farming must decide on all of these aspects. What to grow, how to use the irrigation schedule, and the kinds of fertilizer are all covered in this event. Decisions made by farmers are primarily dependent on their experience, which can lead to the waste of expensive resources like water, fertilizers, time, effort, etc. Additionally, cultivating crops that are not the best fit for a given soil type and climate by using standard farming methods might arise problems, which can reduce production and profit. The application of machine learning in crop prediction is very widespread. The most popular method is irrigation. The major goal of this paper is to efficiently develop an E-business online platform to enhance farmer’s productivity and circulation cycle. In this paper, we develop a platform for smart crop predictions. The platform will help farmers by assisting them in obtaining suggestions based on several metrics like humidity, temperature, pH, moisture, and rainfall. Additionally, the user of our platform will be able to get precise advice about what crop to plant depending on variables like humidity, pH, and other characteristics. The user will also be able to get connected with the buyers of their crops and meet their requirements in an efficient manner.

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