# Optimizing Agricultural Data Analysis Techniques through AI-Powered Decision-Making Processes
## 3 IoT ML Models
### Machine Learning in Agriculture  
In agriculture, machine learning algorithms are commonly employed in various ways, such as diagnosing diseases that may occur on agricultural land, deciding on the most suitable crops by conducting soil analysis, and using data obtained and collected through IoT applications to develop product prediction models. Additionally, these algorithms are used to examine and monitor the plant development using images captured by drones. Efficient and effective irrigation systems can be created using various machine learning methodologies, which are effectively utilized to classify agriculture data, significantly improving productivity and quality. The significance of machine learning methods is indispensable in intelligent systems in numerous agricultural fields.  
Many studies and applications in this area aim to enhance yield and product quality in agriculture, diagnose diseases caused by insects and precipitation, and implement preventive measures. Machine learning (ML) is widely used across various domains to analyze historical data and make accurate predictions. Algorithms that can calculate the performance of suitable agricultural models for fields include decision trees, Bayes networks, random forests, Hoeffding trees, support vector machines, and artificial neural networks. These algorithms can predict crop yields, detect diseases, and optimize irrigation schedules based on diverse agricultural factors. Watering systems are crucial in regulating water consumption levels in agriculture, thereby maximizing the efficient use of resources. Identifying soil characteristics using machine learning is vital for selecting the appropriate soil and minimizing fertilizer costs [22].  
Managing irrigation with machine learning is another method to enhance the production and control of water resources by economically limiting its usage for environmental protection. Drones are also increasingly important in agriculture, where they can be used for drip irrigation by employing a machine learning model trained on weather patterns [23]. Implementing this approach makes it possible to protect water resources and enhance production simultaneously. Also, drones can be used for pest control by using their cameras to capture images and analyze them with ML models to detect any diseases on the farm and during harvest and to identify defective crops in the fields. It is also crucial to assess the health of the soil in agricultural lands and determine if there are any mineral deficiencies [24].  
The key to constructing the most successful models lies in analyzing and comparing ML models. This approach has proven effective and is crucial for attaining the desired outcomes for intelligent farms. In this methodology, different ML algorithms are tested, and the most successful one is identified using an optimization method, thereby achieving a high success rate in agriculture problem domains. To assess the accuracy of the models, the data were initially processed, and then, inputted into the models. Subsequently, a comparison method was employed to evaluate the performance of these models. In our model, we proposed a method to select suitable algorithms according to the characteristics of the IoT-based data that were used in our experiments. The proposed model suggests that researchers can select suitable machine learning algorithms for their agricultural dataset.