Version 1
: Received: 21 November 2023 / Approved: 22 November 2023 / Online: 23 November 2023 (16:49:13 CET)
How to cite:
Pineda Medina, D.; Miranda Cabrera, I.; De la Cruz, R.A.; Guerra Arzuaga, L.; Cuello Portal, S.; Bianchini, M. A Mobile App for Detecting Pests in Potato Crops. Preprints2023, 2023111438. https://doi.org/10.20944/preprints202311.1438.v1
Pineda Medina, D.; Miranda Cabrera, I.; De la Cruz, R.A.; Guerra Arzuaga, L.; Cuello Portal, S.; Bianchini, M. A Mobile App for Detecting Pests in Potato Crops. Preprints 2023, 2023111438. https://doi.org/10.20944/preprints202311.1438.v1
Pineda Medina, D.; Miranda Cabrera, I.; De la Cruz, R.A.; Guerra Arzuaga, L.; Cuello Portal, S.; Bianchini, M. A Mobile App for Detecting Pests in Potato Crops. Preprints2023, 2023111438. https://doi.org/10.20944/preprints202311.1438.v1
APA Style
Pineda Medina, D., Miranda Cabrera, I., De la Cruz, R.A., Guerra Arzuaga, L., Cuello Portal, S., & Bianchini, M. (2023). A Mobile App for Detecting Pests in Potato Crops. Preprints. https://doi.org/10.20944/preprints202311.1438.v1
Chicago/Turabian Style
Pineda Medina, D., Sandra Cuello Portal and Monica Bianchini. 2023 "A Mobile App for Detecting Pests in Potato Crops" Preprints. https://doi.org/10.20944/preprints202311.1438.v1
Abstract
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 potato crop pest detection 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. An exploratory analysis of the architectures used for early and late blight diagnosis in potatoes was performed, achieving an accuracy of 98.76%, with MobileNetv2. Based on the results obtained, an offline mobile application was implemented, supported on devices with Android 4.1 or later, also featuring an information section on the 27 pests 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 pests affecting the same plant.
Keywords
Deep neural networks; Image processing; Classification of potato crop pests
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
