Year: 2024
Type: conference-paper
Abstract: Orange pests pose a significant threat to citrus crops, leading to reduced yield and quality, making effective control essential for healthy orchards and agricultural productivity. In this paper the novel idea of The development of the Dr_ Orange application which is facilitates real-time monitoring of pest infestations, enabling timely farmer interventions that improve crop health, reduce pesticide usage, and enhance productivity. To make the Application the Personal image classifier along the interface used to make the model. the have some features like Chart, report and QR generating which can help the farmers to detect the pests faster and also follow up the process of curiness. To test the app two type of test investigated and the The app has been rigorously tested on 313 images, over the five repeatable pests yielding strong overall performance with an average accuracy and recall of 0.75, perfect precision of 1.00, and an F1 score of 0.856. As the final test the application was evaluated in an orange orchard, successfully identifying and reporting the occurrence of Citrus Leafminer pests in 10 trees. Although the app demonstrates high precision, challenges such as variations in lighting conditions, the angle of the photographs, and data quality can affect result accuracy. Repeatedly taking pictures can help mitigate these effects and improve the interface's impact on detection accuracy. based on obtained results, the proposed App enhances pest management efficiency for orange crops while also promoting improved agricultural productivity.
Authors: Ata Jahangir Moshayedi, Mohammad Jalil Jawadi, Atanu Shuvam Roy, Elahe Rostami, Zhonghua Wang, Liqin Yang, Qun Xu
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
OpenAlex ID: W4409095263