AI-Based Mango Pest and Disease Detection Using MobileNetV2 with a Web-Integrated Farmer Decision Support Platform

DOI

10.2991/978-94-6239-791-0_14How to use a DOI?

Keywords

Artificial Intelligence in Agriculture; Mango Leaf Pest Detection; Deep Learning; CNN; MobileNetV2; Transfer Learning; Precision Agriculture

Abstract

Plant diseases and pest infestations have a measurable impact on agricultural productivity, which results in substantial economic losses and decreased food quality across the world. To prevent these losses and to get the most out of the produce, early detection of such pests is essential. Mango, being one of the most important fruits economically, is vulnerable to several fungal diseases and pests that degrade the quality of the fruit. Traditional methods for detecting strongly rely on manual inspection by experts, which makes it time consuming, subjective and inaccessible to a huge population of farmers, especially in the rural regions of our country. The proposed system uses a MobileNetV2 convolutional neural network trained using transfer learning. The trained model is deployed using tenser flow, which can be used in a web-browser without any interference in real time. The platform consists of more than just automated detection. It also has other features such as weather monitoring, research resources regarding pests, pesticide recommendations, information about government subsidies and agricultural supply locators. The suggested system aims to provide accessibility to precision agriculture technologies and support sustainability using deep learning-based detection with an extensive understanding of the agricultural domain.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Harsh Walke
AU  - Shambhavi Govindpurkar
AU  - Yash Phadtare
AU  - Dipak Musane
AU  - Himani Deshpande
AU  - Vidula Meshram
PY  - 2026
DA  - 2026/10/09
TI  - AI-Based Mango Pest and Disease Detection Using MobileNetV2 with a Web-Integrated Farmer Decision Support Platform
BT  - Proceedings of the International Conference on Artificial Intelligence and Machine Learning for Agro-Ecosystems (ICAIML-AE 2026)
PB  - Atlantis Press
SP  - 159
EP  - 175
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-791-0_14
DO  - 10.2991/978-94-6239-791-0_14
ID  - Walke2026
ER  -