Maize (Zea mays L) is one of the most saleable cereal crops grown worldwide and a dominant staple food in many developing countries. The severe outbreak of fall armyworm in maize causes massive yield loss. Modern technologies, including smartphones, can assist in detecting recognising the fall armyworm infestation in maize. The objective of this study was to develop an automated Artificial Intelligence Powered Expert System (AIPES) for identifying fall armyworm infestation in maize. In addition, it put forward a deep learning-based model that is trained on photographs of healthy and fall armyworm infested leaves, cobs and tassels from a dataset and furnished an application that will be detecting maize fall armyworm infestation using Convolutional Neural Network (CNN) architecture and Mobile Net V 2 framework model. The study developed an Artificial Intelligence (AI) based maize fall armyworm infestation detection system using a DCNN (Deep Convolutional Neural Network) to support maize cultivating farmers. The model executed the objective by accurately identifying the fall armyworm infested maize plant and also classified them vis-c-vis the healthier crop. The deep learning models were trained to detect and recognise fall armyworm infection using more than 11000 images of fall armyworm infested leaves, cobs, and tassels. The created application (AIPES for identifying fall armyworm infestation in maize) using CNN detected and recognised the fall armyworm infestation in maize with a 100 per cent training accuracy rate and 87 per cent validation accuracy. So, the detection of maize fall armyworm and the treatment of fall armyworm-infested maize could lead to a higher maize crop yield.
Artificial intelligence, Convolutional neural networks, Deep learning, Fall armyworm infestation detection, Maize, Mobile app
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