# Crop Prediction Model Using Machine Learning Algorithms
## 3 Machine Learning in Smart Farming  
Innovative farming methods have profoundly transformed agriculture using advanced technologies to increase productivity, enhance sustainability, and lessen environmental harm. Machine learning (ML), a vital component of this change, has enabled various applications that simplify farming operations and better inform decision-making processes. ML applications are widely used in livestock, water, soil, and crop management. ML improves animal welfare for livestock and boosts production, increasing sustainability via predictive modeling and real-time health monitoring [35]. ML is exploited to optimize irrigation and water usage for water management by analyzing various parameters. The application of newly emerging ML technologies and large amounts of available weather and water data makes it easier to manage water resources. This is especially helpful because natural events can be unpredictable, and the relationships between them can be complicated [36]. In soil management, ML helps analyze soil health, predict nutrient needs, and conclude the factors affecting soil distribution controls [37]. As in crop management, ML detects diseases and weeds, evaluates crop quality, recognizes species, and predicts crop yield. Crop yield prediction is immensely important for farmers and policymakers, governments concerned with food security, and food marketing organizations [38]. These stakeholders can use yield prediction models to make data-driven decisions and develop strategies for efficient resource allocation, food distribution, and price stabilization. This leads to a more resilient food system that can be assured by anticipating crop production changes. However, predicting crop production is a complex task, as it is affected by many factors, such as weather conditions, the kind of fertilizer used, soil type, and the variety of seeds. Consequently, tackling this task necessitates the incorporation of diverse datasets and a range of attribute types. Supervised learning techniques benefit crop yield prediction among the numerous ML categories. This is due to their robust predictive capabilities and ability to handle different attribute types. These methods employ labeled data to forecast outcomes based on specific inputs. For example, they project crop yields based on weather conditions and soil quality data. Forecasting crop yield can be achieved using a broad spectrum of ML techniques, including Artificial Neural Networks (ANNs), Support Vector Machines, and Random Forests [39; 40; 41; 42]. These algorithms' ability to process historical and up-to-date information regarding weather, soil conditions, and crop health facilitates precise crop yield predictions. The resulting insights enable farmers to make educated decisions about planting, irrigation, and fertilization, ultimately leading to optimized yields and less resource wagagge. Machine learning is already playing an important role in providing farmers with information to make agriculture more efficient and productive, hence maximizing their profits. Therefore, farmers are highly encouraged to apply ML algorithms and techniques efficiently, especially while collecting, processing, and analyzing data. ML technology can provide a solution to most challenges farmers face [43]. It can help them predict the weather more accurately, decrease waste, boost output, and increase profit margins. In this regard, farmers are motivated to use the advanced technology to collect data, such as autonomous vehicles, variable rate technology, GPS-based soil sampling, automated hardware, telematics, software, sensors, cameras, robots, drones, GPS guidance, and control systems [44]. According to [45], two-thirds of the farmers worldwide struggle to use technology, and more than 50% are unaware of the existing solutions. Teaching farmers to work with machine learning can be a transformative step in modernizing agriculture and improving productivity.