Automated Pest Detection With DNN on the Edge for Precision Agriculture

Albanese, Andrea; Nardello, Matteo; Brunelli, Davide

doi:10.1109/jetcas.2021.3101740

Cited by 71 publications

(37 citation statements)

References 50 publications

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“…There are many hardware choices we can take to face the task but in order to meet the low-cost, high power-sufficiency we need to go down to the level of microcontrollers such as the ESP32. The use of a microcontroller with a small amount of memory and the need for power sufficiency restrains us from importing sophisticated but large libraries of object detection models with large weights that require substantial computational resources [27][28][29]. In Appendix A we compare ESP32 with other more advanced hardware platforms running the same software and list the technical capabilities and their corresponding costs.…”

Section: Methodsmentioning

confidence: 99%

Edge Computing for Vision-Based, Urban-Insects Traps in the Context of Smart Cities

Saradopoulos

Potamitis

Ntalampiras

et al. 2022

Sensors

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Our aim is to promote the widespread use of electronic insect traps that report captured pests to a human-controlled agency. This work reports on edge-computing as applied to camera-based insect traps. We present a low-cost device with high power autonomy and an adequate picture quality that reports an internal image of the trap to a server and counts the insects it contains based on quantized and embedded deep-learning models. The paper compares different aspects of performance of three different edge devices, namely ESP32, Raspberry Pi Model 4 (RPi), and Google Coral, running a deep learning framework (TensorFlow Lite). All edge devices were able to process images and report accuracy in counting exceeding 95%, but at different rates and power consumption. Our findings suggest that ESP32 appears to be the best choice in the context of this application according to our policy for low-cost devices.

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Section: Methodsmentioning

confidence: 99%

Edge Computing for Vision-Based, Urban-Insects Traps in the Context of Smart Cities

Saradopoulos

Potamitis

Ntalampiras

et al. 2022

Sensors

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“…Insect pests are responsible for 20% of the annual crop losses that occur across the world [ 1 ]. With the advancement of computer vision technology, machine learning and deep learning have been used in the agricultural pest identification field [ 2 , 3 , 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

A New Pest Detection Method Based on Improved YOLOv5m

Dai

Dorjoy

Miao

et al. 2023

Insects

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Pest detection in plants is essential for ensuring high productivity. Convolutional neural networks (CNN)-based deep learning advancements recently have made it possible for researchers to increase object detection accuracy. In this study, pest detection in plants with higher accuracy is proposed by an improved YOLOv5m-based method. First, the SWin Transformer (SWinTR) and Transformer (C3TR) mechanisms are introduced into the YOLOv5m network so that they can capture more global features and can increase the receptive field. Then, in the backbone, ResSPP is considered to make the network extract more features. Furthermore, the global features of the feature map are extracted in the feature fusion phase and forwarded to the detection phase via a modification of the three output necks C3 into SWinTR. Finally, WConcat is added to the fusion feature, which increases the feature fusion capability of the network. Experimental results demonstrate that the improved YOLOv5m achieved 95.7% precision rate, 93.1% recall rate, 94.38% F1 score, and 96.4% Mean Average Precision (mAP). Meanwhile, the proposed model is significantly better than the original YOLOv3, YOLOv4, and YOLOv5m models. The improved YOLOv5m model shows greater robustness and effectiveness in detecting pests, and it could more precisely detect different pests from the dataset.

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“…An example of TinyML on the edge is presented in refs. [25]. In this paper, a computer vision solution with automated continuous pest detection inside fruit orchards with a DNN model is presented.…”

Section: Introductionmentioning

confidence: 99%

Tiny machine learning on the edge: A framework for transfer learning empowered unmanned aerial vehicle assisted smart farming

Hayajneh,

Aldalahmeh,

Alasali

et al. 2023

IET Smart Cities

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Emerging technologies are continually redefining the paradigms of smart farming and opening up avenues for more precise and informed farming practices. A tiny machine learning (TinyML)‐based framework is proposed for unmanned aerial vehicle (UAV)‐assisted smart farming applications. The practical deployment of such a framework on the UAV and bespoke internet of things (IoT) sensors which measure soil moisture and ambient environmental conditions is demonstrated. The key objective of this framework is to harness TinyML for implementing transfer learning (TL) using deep neural networks (DNNs) and long short‐term memory (LSTM) ML models. As a case study, this framework is employed to predict soil moisture content for smart agriculture applications, guiding optimal water utilisation for crops through time‐series forecasting models. To the best of authors’ knowledge, a framework which leverages UAV‐assisted TL for the edge internet of things using TinyML has not been investigated previously. The TL‐based framework employs a pre‐trained data model on different but similar applications and data domains. Not only do the authors demonstrate the practical deployment of the proposed framework but they also quantify its performance through real‐world deployment. This is accomplished by designing a custom sensor board for soil and environmental sensing which uses an ESP32 microcontroller unit. The inference metrics (i.e. inference time and accuracy) are measured for different ML model architectures on edge devices as well as other performance metrics (i.e. mean square error and coefficient of determination [R2]), while emphasising the need for balancing accuracy and processing complexity. In summary, the results show the practical feasibility of using drones to deliver TL for DNN and LSTM models to ultra‐low performance edge IoT devices for soil humidity prediction. But in general, this work also lays the foundation for further research into other applications of TinyML usage in many different aspects of smart farming.

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Automated Pest Detection With DNN on the Edge for Precision Agriculture

Cited by 71 publications

References 50 publications

Edge Computing for Vision-Based, Urban-Insects Traps in the Context of Smart Cities

Edge Computing for Vision-Based, Urban-Insects Traps in the Context of Smart Cities

A New Pest Detection Method Based on Improved YOLOv5m

Tiny machine learning on the edge: A framework for transfer learning empowered unmanned aerial vehicle assisted smart farming

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