Research Article Open Access

Identifying and Predicting Pests on Tea Leafs using Image Processing

Abstract

Quality serves as the paramount factor for success in the global market export arena, thus it becomes crucial to mechanize and employ intelligent methods for identification and detection of pests in the leaves of agriculture products. It is important to ensure high quality production and timely control of pests in goods ready for export. To achieve this objective, various activities are being pursued, including the development of a system for detecting and identifying pests in the mentioned plant. This paper addresses a system that involves maintaining a database of photos depicting both healthy and infested plants in the leaf area, which are continuously monitored through CCTV cameras or robots. The captured images are then sent to a central server, where the proposed method recognizes the type of pest. One of the keys aims of this system is to establish HAAR-wavelet transform in color and the shape of the tea leaf diseases, which is the timely diagnosis of the diseases and reduce the error rate in diagnosis. Ultimately, this approach applied boosting classifier to identify pests in the leaf areas. The proposed method has been evaluated using created database and the recognition rate 93.7% in experimental results on this dataset shows that can be comparison with former approaches.

Majid Ashouri*, Vahid Rostami

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