Machine Learning-Based Crop Prediction: A Way Towards Smart Farming

The provision of a certain percentage of domestic goods to ensure food security makes agriculture essential to the Indian economy. Since it contains essential nutrients, the soil is the most crucial component for flourishing agriculture. However, due to abnormal climatic changes, food production and prediction are now becoming diminished, which will have a significant impact on farmers’ economies. Temperature, moisture, and other weather patterns are also important factors in enhancing soil nutrients because they are associated with the mechanisms of photosynthesis, seedling growth, and saturation. The major goal of this endeavour is to develop a web-based GUI for a crop forecast model based on Machine Learning Operations (MLOps). To help farmers by directing them to plant suitable crops, the suggested method can be utilised to assess the soil nutrient concentration by accessing the associative characteristics including the environmental parameters. XGBoost, a tree-focused ensemble machine learning algorithm, outperformed other classification algorithms by obtaining an accuracy of 99.318%.

I. Introduction

Agriculture is the primary source of supplies for supplying human requirements daily. It is also thought of as a main source of employment and as one of India’s most significant industrial sectors. However, farmers are utilizing traditional naked-eye inspection to produce healthy crops without using pesticides on their agricultural fields to maintain a healthy diversity. But right now, the weather is rapidly working against natural resources, causing food supplies to run low. There are differences between today’s seasonal events and those of the past. Even the possibility of future floods or water shortages is difficult to forecast. Furthermore, if a certain crop is chosen to be grown, farmers are not technologically adept enough to anticipate production. So it makes sense for Indian farmers to give more attention to efficient and accurate farming. In India, there are a variety of approaches to increase crop yield to maintain agricultural economic growth. As a result, one approach for determining crop yields concerning atmospheric and soil characteristics in agricultural farmland is to use one of the most current technological advancements, such as machine learning. Unlike decades ago, today’s climatic conditions are unpredictable, globalization is causing them to change on a daily basis. As illustrated in Fig. 1, the most often consumed elementary unit, nitrogen, is the building block of all proteins and chlorophyll molecules. However, phosphorus is the least often ingested dietary supplement supplemented with nucleic acid, as root is strengthened when orthophosphate ions are absorbed. Potassium is necessary for osmosis control and enzyme activation. Compared to macronutrients, secondary nutrients are needed in smaller amounts. As a result, calcium, magnesium, and sulphur are each credited with abilities in reinforcing the cell wall, maintaining electronic balance, and refining the amino acids, respectively. Micronutrient needs are less than 1%, but they have a big impact on plant development and agricultural productivity. Water and air are the sources of non-mineral elements like Carbon, Oxygen, and Hydrogen. Even though each nutrient has a different rate of use, they all contribute significantly to maintaining soil fertility. When predicting weather and crops, farmers have problems using climate data. There might be a logical classifier where a novice mathematician predicts the likelihood that each category would have members, such as the likelihood that knowledge will fall into a particular category. So crop forecasting utilizing historical data can be established by analyzing the farmer’s prior experience with weather circumstances. Therefore, it is essential to have correct knowledge of historical climatic conditions while making crop-related decisions. The planned initiative will therefore give farmers realistic possibilities for more prosperous farming.