29th World Congress on Engineering (WCE 2022) , 6 - 08 July 2022, pp.108-113
The change in the climate conditions and the increase of the consumption with the increased population,forced change in the agricultural field. This change brings a question of how to reach enough natural products even in small areas. Vertical farming option emerged as one of the sustainable options with the short supply chain processes. Besides, it helps to decrease the effects of climate change and improve the sustainability since it utilizes less water and avoids problems such as arid soil, soil infertility etc. Technological developments started to spread rapidly in the agricultural field and led to various digital transformations according to the needs. Recently, the new concept of smart agriculture makes agriculture more efficient and effective thanks to highprecision algorithms. The most significant farming applications are irrigation management, pest and disease control, greenhouse condition monitoring, soil and water quality monitoring, precision agriculture and dairy management. In this study, machine learning methods are applied such as CNN image processing model which can detect and identifying diseases on plants. In this research, AI based lettuce diseases detection system is proposed. An AI model is developed to identify different lettuce diseases. The model was built with ResNet50 and ImageNet on Tensorflow. Various over fitting prevention methods are also applied to the model to compensate for the limited training dataset and the results of the study are discussed. It will guide people towards enhancing awareness of the importance and necessity of using various machine learning techniques and various alternatives to traditional agriculture for the sustainability.