In modern digital agricultural applications, automatic identification and diagnosis of plant diseases using artificial intelligence is becoming popular and widespread. Deep learning is a promising tool in pattern recognition and machine learning and it can be used to identify and classify diseases in paddy rice. In this study, 2 different paddy rice diseases, including rice blast and brown spot, were investigated in the district of İpsala in the province of Edirne between the 2020 and 2021 production seasons by collecting 1569 images. These diseases are very common and important in Edirne province and surrounding rice production areas. Therefore, practical methods are needed to identify and classify these two diseases. A Convolutional Neural Network (CNN) model was created by applying pre-processing techniques such as rescaling, rotation, and data augmentation to the paddy rice disease images. The classification model was created in Google Colab, which is a web-based Python editor using Tensorflow and Keras libraries. The CNN model was able to classify rice blast and brown spot diseases with high accuracy of 91.70%.

Paddy rice irrigation takes an important role in water consumption. Therefore, the savings to be made in paddy rice irrigation will have significant impacts. In the sustainable use of water resources, both the irrigation methods and the methods to be used in the planning of water resources are critical. Hence, the use of drip irrigation should be expanded. On the other hand, the use of modern satellite technologies and machine learning models should be used while planning irrigation. In this study, Google Earth Engine (GEE), which is a cloud-based image processing platform was employed in the calculation of paddy rice cultivation areas. Random Forest (RF) and Support Vector Machines (SVM) machine learning algorithms were applied. The results showed that RF algorithm can calculate the paddy cultivation areas with an accuracy of 97%. A difference of 27.69 km2 was found between the officially declared cultivation areas and the calculated area. This can yield a miscalculation of water requirement with an error of 33.8, 38.1 and 155 million m3, in subsurface drip irrigation, drip irrigation and basin irrigation methods, respectively. Results showed that accurate calculation of paddy rice cultivation areas and drip irrigation will both minimize this error and allow 4 times more area to be irrigated.