A Mobile App for Detecting Potato Crop Diseases

作     者：Pineda Medina, Dunia Miranda Cabrera, Ileana de la Cruz, Rolisbel Alfonso Guerra Arzuaga, Lizandra Cuello Portal, Sandra Bianchini, Monica 

作者机构：Ctr Nacl Sanidad Agr San Jose De Las Lajas 11300 Cuba Univ Siena Dipartimento Ingn Informaz & Sci Matemat I-53100 Siena Italy 

出 版 物：《JOURNAL OF IMAGING》 (J. Imaging)

年 卷 期：2024年第10卷第2期

页      面：47-47页

核心收录：

主　　题：deep neural networks image processing classification of potato crop diseases 

摘      要：Artificial intelligence techniques are now widely used in various agricultural applications, including the detection of devastating diseases such as late blight (Phytophthora infestans) and early blight (Alternaria solani) affecting potato (Solanum tuberorsum L.) crops. In this paper, we present a mobile application for detecting potato crop diseases based on deep neural networks. The images were taken from the PlantVillage dataset with a batch of 1000 images for each of the three identified classes (healthy, early blight-diseased, late blight-diseased). An exploratory analysis of the architectures used for early and late blight diagnosis in potatoes was performed, achieving an accuracy of 98.7%, with MobileNetv2. Based on the results obtained, an offline mobile application was developed, supported on devices with Android 4.1 or later, also featuring an information section on the 27 diseases affecting potato crops and a gallery of symptoms. For future work, segmentation techniques will be used to highlight the damaged region in the potato leaf by evaluating its extent and possibly identifying different types of diseases affecting the same plant.