AI and IoT-Based Model for the Detection and Treatment of Sweet Potato Pests and Diseases in Precision Farming
Keywords: Artificial Inteligence, IoT, Deep learning
TL;DR: using AI and IoT for improving crop yields through early detection of pests and diseases.
Abstract: Pests and disease detection and treatment at an early stage are essential in agriculture to ensure food security in Africa. Farmers face various challenges when identifying and controlling pests and diseases. In this work, a pests and diseases detection and treatment system, using Artificial Intelligence (AI) and Internet of Things (IoT) will be developed. Pheromone will be used to monitor and capture sweet potatoes farm pests especially Cylas puncticollis. Images of sweet potato virus diseases (Ipomoea batatas (L.) and sweet potato chlorotic stunt virus gotten online will be used to train the AI model using Convolutional Neural Networks. Data augmentation and pre-processing will be done. To test the performance of the developed AI model in real time, an (IoT) device will be built and set up on the farm to monitor and capture the pests and disease parameters. The IoT will send the parameters to the AI model deployed to the cloud for prediction.
Submission Category: Machine learning algorithms
Submission Number: 55
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