What does this research mean for the field?

The lightweight convolutional neural network (mangoNet) achieves 99.61% accuracy in monitoring mango leaf images for real-time disease detection in smart mango orchards. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to create a lightweight convolutional neural network, mangoNet, for monitoring mango trees in smart orchards.

March 1, 2026Scientific Reports0 citationsOpen Access

A lightweight convolutional neural network for real-time monitoring of smart mango orchard systems

MAMd Taimur AhadNorth South University NCNoman H. ChowdhuryNorth South University AAAinuddin AhmedNorth South University

Key Points

The aim is to create a lightweight convolutional neural network, mangoNet, for monitoring mango trees in smart orchards.
Developed lightweight CNN named mangoNet for image monitoring.
Evaluated mangoNet against six state-of-the-art CNNs.
Integrated mangoNet into a Streamlit web application and an Android app.
Utilized SHAP, LIME, and Grad-CAM visualizations for decision-making insights.
MangoNet achieved 99.61% accuracy with only 3,987,400 parameters.
Outperformed traditional state-of-the-art CNNs in accuracy and efficiency.
Reduced computational resource requirements and training time.

Abstract

Recent advancements in Convolutional Neural Networks (CNNs), combined with the growing adoption of farm-applicable Internet of Things (IoT) devices, have expanded the application of precision agriculture in mango orchards. The Smart Mango Orchard can play a crucial role in ensuring mango trees thrive and produce high-quality fruit. However, state-of-the-art (SOTA) CNNs are built on numerous layers and many parameters; therefore, they are challenging to deploy in IoT devices. However, the lightweight CNN is a possible solution. This study developed a lightweight CNN, mangoNet, to deploy in an innovative mango orchard environment. The mangoNet is expected to monitor the mango leaf images and report them to farmers via a mobile app using the IoT system. The study was conducted using the primary dataset collected from the mango gardens in Rajshahi, Bangladesh. The mangoNet benchmark was evaluated using six SOTA CNNs. The mangoNet, with only 3,987,400 Parameters, outperforms SOTA CNNs’ accuracy (99.61%). In addition, this study employed SHAP, LIME, and Grad-CAM visualizations to identify and depict the image regions that contribute to mangoNet’s decision-making process. The mangoNet is integrated into a Streamlit web application and an Android mobile app, as researchers suggest for the practical use of CNNs. The novelty of mangoNet lies in its balanced sequential architecture, with a careful selection of kernels and progressive filter expansion, enabling early layers to capture low-level features and deeper layers to extract high-level features. As a result, the proposed mangoNet achieved high accuracy while requiring fewer computational resources and reduced training time. In addition, the mangoNet-powered website and mobile application empower both farmers and farming stakeholders by making real-time disease detection. In the future, the prototype is expected to be commercialized as Bangladesh is the 8Th mango-producing country in the world.

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