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Subject: Computer Engineering × Author: Neha × End date: 2021 × Start date: 2020 × Type: Thesis ×
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    ThesisItemOpen Access
    Use of drone in disease identification from leaves by deep learning through YOLO v3 and CNN architecture
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-02) Neha; Singh, Rajeev
    In recent years Machine Learning and Deep Learning have played a crucial role in the field of agriculture. There are many methods that are adopted in farming so that the yield and production increases. Smart agriculture is itself growing and developing. In automated farming, smart agriculture helps to collect data from the field and then analyze it so that the farmer can make precise decisions to grow high-quality crops. For better agricultural productivity and food management, an agriculture monitoring system is needed. Precision agriculture is also used as new technology for the decision making process. In this work, we have used drone for collecting data in real time from the field. ML algorithm are then used to take optimal decisions which helps in cutting the cost of procedure. Drone systems are also used reliably for operations like UREA spraying wherein involvement of the sensors enables a reliable safe operation with good satisfaction of customer. However, this field is open for improvements majorly in decision support system which helps in converting large amount of data into useful recommendations. Deep learning is a subset of machine learning. It can be used for precision farming, identification of diseases, classification of images etc. This research deals with identification of wheat plant leaf diseases by accessing the leaf morphology of crops by means of drone photography and further analysis of captured images by computer means using YOLO V3 and CNN architecture.