Empowering Bell Pepper Farmers with Smart Polytunnel Technology for Powdery Mildew Disease Detection and Management

Abstract

Powdery mildew disease significantly impacts crop productivity and financial losses in agriculture. Detecting and diagnosing the disease is challenging and requires expertise. Bell pepper production is particularly vulnerable, affecting both quality and quantity. To ensure effective treatment and control measures, the disease must be quickly identified and investigated. A comprehensive system has been developed to help farmers make informed decisions about crop management, improving overall quality and production. A smartphone app is developed to help farmers detect powdery mildew infection in bell pepper plants. Utilizing deep learning algorithms, the app processes photos through classification, feature extraction, and image segmentation. The program uses a powerful algorithm to identify illnesses, utilizing object detection, transfer learning, and Mask R-CNN to improve disease detection early. The smart polytunnel system, developed by this research, can identify and examine plant powdery mildew disease early on. This technology can improve agricultural yields and crop quality, enhancing sustainability and profitability. By enabling proactive and informed crop management decisions, the system contributes to a more robust and successful future for bell pepper growers and the agricultural sector.

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