# Automated Pest Detection with DNN on the Edge for Precision Agriculture

arXiv: https://arxiv.org/abs/2108.00421 · HTML: https://mlsanity.com/paper/2108.00421/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 61 references that are in the map.
- First posted on arXiv 1 Aug 2021.

## Builds on (most cited first, 10 of 10)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [EdgeDRNN: Recurrent Neural Network Accelerator for Edge Inference](https://mlsanity.com/paper/2012.13600/) (2020)
- [Automatic Horizontal Fusion for GPU Kernels](https://mlsanity.com/paper/2007.01277/) (2020)
- [Automatic Moth Detection from Trap Images for Pest Management](https://mlsanity.com/paper/1602.07383/) (2016)
- [Pre-Defined Sparse Neural Networks with Hardware Acceleration](https://mlsanity.com/paper/1812.01164/) (2018)
- [Slotted ALOHA Overlay on LoRaWAN: a Distributed Synchronization Approach](https://mlsanity.com/paper/1809.02234/) (2018)
- [Lightweight Synchronization Algorithm with Self-Calibration for Industrial LORA Sensor Networks](https://mlsanity.com/paper/1809.05604/) (2018)
- [Slotted ALOHA on LoRaWAN - Design, Analysis, and Deployment](https://mlsanity.com/paper/1902.09468/) (2019)

## Built on by (most cited first, 5 of 5)

- [Incremental Online Learning Algorithms Comparison for Gesture and Visual Smart Sensors](https://mlsanity.com/paper/2209.00591/) (2022)
- [Optimizing Multi-DNN Inference on Mobile Devices through Heterogeneous Processor Co-Execution](https://mlsanity.com/paper/2503.21109/) (2025)
- [FruitProm: Probabilistic Maturity Estimation and Detection of Fruits and Vegetables](https://mlsanity.com/paper/2510.24885/) (2025)
- [ReinDSplit: Reinforced Dynamic Split Learning for Pest Recognition in Precision Agriculture](https://mlsanity.com/paper/2506.13935/) (2025)
- [Clustered Edge Intelligence: Beyond Just Convergence of Edge Computing and AI](https://mlsanity.com/paper/2607.20937/) (2026)

## Abstract (arXiv)

Artificial intelligence has smoothly penetrated several economic activities, especially monitoring and control applications, including the agriculture sector. However, research efforts toward low-power sensing devices with fully functional machine learning (ML) on-board are still fragmented and limited in smart farming. Biotic stress is one of the primary causes of crop yield reduction. With the development of deep learning in computer vision technology, autonomous detection of pest infestation through images has become an important research direction for timely crop disease diagnosis. This paper presents an embedded system enhanced with ML functionalities, ensuring continuous detection of pest infestation inside fruit orchards. The embedded solution is based on a low-power embedded sensing system along with a Neural Accelerator able to capture and process images inside common pheromone-based traps. Three different ML algorithms have been trained and deployed, highlighting the capabilities of the platform. Moreover, the proposed approach guarantees an extended battery life thanks to the integration of energy harvesting functionalities. Results show how it is possible to automate the task of pest infestation for unlimited time without the farmer's intervention.