Detection of snail pest in citrus orchard under different lighting conditions using deep neural networks

The control pests and diseases is considered one of the most important operations of Citrus in the protection stage. Today, a lot of research has been done in various fields of agriculture, including the diagnosis of plant pests and diseases by using machine vision methods. One of the problems that reduce the accuracy of the machine for detecting pests in farm conditions is the presence of adverse factors such as shade and changes in light intensity at different times of the day. In this study, in order to find the appropriate light intensity at different times of the day and increase the brightness of the shady parts of the trees, lighting by a lamp at the imaging site has been used. For detect pest-infected trees (in this snail study) has been used to Deep learning method which has been studied and evaluated by various optimization algorithms such as RMSProp, Adam and SGDm. To evaluate and test the algorithm used, 8000 images were examined in 9 farm conditions and one laboratory state In farm conditions, the lowest detection accuracy of algorithms with 64.32% related to imaging in cloudy days with light intensity of 350 to 700 lux was obtained using RMSProp algorithm, which Detection accuracy was improved up to 95.25% using SGDm algorithm by creating a light intensity controlled by a lamp (approximately 9000 lux). In laboratory conditions where the images were prepared in a controlled environment with constant light intensity, the detection accuracy was Obtained 98.73% with SGDm algorithm.