Akıllı tarım uygulamaları

With climate change, global warming and population growth, agricultural problems grew and product sustainability came under risk with classical agriculture. For this reason, digitalization has been directed in agriculture. It has been an effective approach that provides critical advantages to producers to make agriculture more sustainable. The main purpose of digitalization in agriculture is optimizing complex agricultural systems by applying information and data technologies. The basis of smart agriculture is the IoT technologies. However, the focus of smart agriculture is accessing data such as temperature and humidity in real time and processing collected data in useful way. The processing of data collected by using different sensors with machine learning is making the collected information meaningful and usable. The purpose of my project is instantly observing the temperature and humidity values collected through sensors, determining the optimum values suitable for agriculture and taking action as soon as the threshold point is exceeded. So DHT11 sensor, ESP8266 and Google Cloud are used for this. Today, cloud technologies provide effective solutions for real-time monitoring collected data from devices and machines. Different types of gateways are used transfering the collected data to cloud platforms. Here mosquitto was used as the MQTT server for the performance analysis. Near this machine learning methods used detecting disease in grown lettuce leaves. Detection of plant diseases is one of the most applied developments in agriculture determining whether the plant's leaves are healthy or ill. For this the data were preprocessed by image processing techniques using machine learning algorithm.