A Smart Prediction Platform for Agricultural Crops Using Machine Learning

Ammar Rafiq ()
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Ammar Rafiq: Department of Computer Sciences, NFC-Institute of Engineering and Fertilizers ResearchFaisalabad, Pakistan

International Journal of Innovations in Science & Technology, 2025, vol. 7, issue 1, 98-110

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 cause 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 farmers’ 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 efficiently meet their requirements.

Keywords: Irrigation; fertilizer; economic; climate; kaggle; and soil. (search for similar items in EconPapers)
Date: 2025
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