Farming is a worldwide activity, and one of the processes that drive this activity is irrigation. Many manual irrigation processes are utilized worldwide, including surface, drip, and sprinkler irrigation. However, these processes have posed some problems which include overirrigation/underirrigation which leads to the damage of crops/land and the rise of utility rates, less monitoring by humans which leads to an inefficient irrigation process and the rise of costs and finally increased labor costs as people with enough expertise have to be hired to carry out the irrigation process. This chapter discusses strategies that can be used to implement a Smart Farming solution and eliminate these problems. The Internet of Things (IoT) is the first strategy to eliminate these problems. Hence, a mobile application that informs the user when to start the irrigation process and when to stop it is developed. The soil moisture is the variable used for this process, and a soil moisture sensor connected to an Arduino UNO is utilized to measure this variable. After that, an application is developed that measures the weather, humidity, and rainfall status of the city where the user is located with the help of the OpenWeatherMap API. With this information, the user will be notified whether or not it is useful to start the irrigation process. Finally, machine learning is seen as the final strategy of eliminating the problems caused by manual irrigation processes; hence, a plant disease detection model is developed using Convolutional Neural Networks. Five plant diseases are used as classes, and the model achieves 99% accuracy.