Intelligent Crop Selection and Soil Nutrient Management Using Machine Learning

Machine learning employs a wide range of techniques to create models that can foresee yet-to-be-seen data in the future and derive prediction rules from known data, with the use of the same we generate a system that is being suggested to combine information from the meteorological service, grain depository, and soil data to anticipate the most suited crops based on the present state of the environment. The choice of the appropriate crop for the farm’s soil conditions is one of the most crucial factors in agriculture. To aid these farmers, a Python prototype of a real-time crop recommendation programme that combines machine learning and data analytics was developed. The ability for computers to learn and adapt without explicit programming is provided by the field of machine learning, falls under the category of artificial intelligence (AI). Traditional disease detection and nutrient value estimation methods are being replaced by a new, more advanced approach that takes advantage of dataset upgrades and looks at the patterns different crops create in response to different climatic conditions. We are collecting data that includes values for the following elements: Nitrogen (N), Potassium (K), Phosphorous (P), Iron (Fe), Zinc (Zn), Copper (Cu), Manganese (Mn), Boron (B), and Sulphur (S) pH, temperature, humidity, rainfall, with the label of crop type for the various districts. High crop yields and the production of high-quality goods depend on the availability of soil nutrients. Therefore, the primary goal of this paper is to provide farmers with a remedy since they frequently administer fertilizer at random without realizing its poor quality or quantity. In order to produce new features, extract them, scale them for the specific ML model, and choose the best one, we provide and place our systematic dataset under numerous machine learning models. In light of this, automatic identification and monitoring are crucial for ensuring sustained agricultural income.