Smart IoT device for in field Black Sigatoka Disease recognition and mapping

• BSD causes streaking, lesions, and yellow and brown spots on banana leaves. • In this work, a physical field device was developed to identify the presence of BSD. • By implementing a CNN, samples were classified into 6 classes of the BSD infection. • The trained model showed 82 % accuracy in training and 78 % accuracy in validation. • BSD app allows for acquisition, identification of the georeferenced infection stage. Recently banana plantations have been affected by the Black Sigatoka Disease (BSD), producing streaks, lesions and yellow and brown spots on the leaves until the appearance of entire dead parts. The disease causes reductions in yield making it essential to assess infection by monitoring plants status and implementing agronomical measures. This work aims to develop a physical field device to identify the BSD presence. It consists in a 3D printed prototype embedding a smartphone acquiring and processing banana leaves images. An advanced Artificial Intelligence model was trained and implemented for real-time processing. The algorithm is a Convolutional Neural Network (CNN) able to classify the samples into 6 classes representative of different BSD stages infection. The trained model, showing an accuracy of 82 % in training and 78 % in validation, was integrated into a specifically developed mobile application for field use. The Android app allows to acquire, identify the georeferenced infection stage, sync all to a remote dedicated host from which the results can be mapped and exported to a .csv file for easy data management. The distinction between healthy and diseased leaves can be achieved using the Smart BSD device for real-time acquisition, establishing the right intervention strategy.