Evaluation Methods of Bird Repellent Devices in Optimizing Crop Production in Agriculture

Roihan, Ahmad; Hasanudin, Muhaimin; Sunandar, Endang

doi:10.1088/1742-6596/1477/3/032012

Cited by 7 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the idea of implementing deep learning algorithms on embedded platform, we briefly describe some of the works that utilizes deep learning on embedded platforms related to Agriculture. The use of RPi has also been demonstrated by [43] by using devices to repel birds in optimizing crop production in Agriculture. RPi and NVIDIA Jetson TX2 were used in [44] to monitor and detect Asian citrus psyllid pests in orchards using convolutional neural networks.…”

Section: Related Workmentioning

confidence: 99%

Design, Development and Evaluation of an Intelligent Animal Repelling System for Crop Protection Based on Embedded Edge-AI

Adami

Ojo²,

Giordano

2021

IEEE Access

View full text Add to dashboard Cite

In recent years, edge computing has become an essential technology for real-time application development by moving processing and storage capabilities close to end devices, thereby reducing latency, improving response time and ensuring secure data exchange. In this work, we focus on a Smart Agriculture application that aims to protect crops from ungulate attacks, and therefore to significantly reduce production losses, through the creation of virtual fences that take advantage of computer vision and ultrasound emission. Starting with an innovative device capable of generating ultrasound to drive away ungulates and thus protect crops from their attack, this work provides a comprehensive description of the design, development and assessment of an intelligent animal repulsion system that allows to detect and recognize the ungulates as well as generate ultrasonic signals tailored to each species of the ungulate. Taking into account the constraints coming from the rural environment in terms of energy supply and network connectivity, the proposed system is based on IoT platforms that provide a satisfactory compromise between performance, cost and energy consumption. More specifically, in this work, we deployed and evaluated various edge computing devices (Raspberry Pi, with or without a neural compute stick, and NVIDIA Jetson Nano) running real-time object detector (YOLO and Tiny-YOLO) with custom-trained models to identify the most suitable animal recognition HW/SW platform to be integrated with the ultrasound generator. Experimental results show the feasibility of the intelligent animal repelling system through the deployment of the animal detectors on power efficient edge computing devices without compromising the mean average precision and also satisfying real-time requirements. In addition, for each HW/SW platform, the experimental study provides a cost/performance analysis, as well as measurements of the average and peak CPU temperature. Best practices are also discussed and lastly, this article discusses how the combined technology used can help farmers and agronomists in their decision making and management process.

show abstract

Section: Related Workmentioning

confidence: 99%

Design, Development and Evaluation of an Intelligent Animal Repelling System for Crop Protection Based on Embedded Edge-AI

Adami

Ojo²,

Giordano

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The literature, by extension, includes comprehensive articles addressing both the detection and the scaring of birds [ 30 ]. One of these sources is referenced in study [ 31 ], which characterizes the prototype of a device that comprises PIR sensors as the detector to track moving birds and uses LC oscillator type colpitts with a piezo ultrasonic sensor as the repeller.…”

Section: Previous Researchmentioning

confidence: 99%

A System Using Artificial Intelligence to Detect and Scare Bird Flocks in the Protection of Ripening Fruit

Marcoň

Janoušek

Pokorny

et al. 2021

Sensors

View full text Add to dashboard Cite

Flocks of birds may cause major damage to fruit crops in the ripening phase. This problem is addressed by various methods for bird scaring; in many cases, however, the birds become accustomed to the distraction, and the applied scaring procedure loses its purpose. To help eliminate the difficulty, we present a system to detect flocks and to trigger an actuator that will scare the objects only when a flock passes through the monitored space. The actual detection is performed with artificial intelligence utilizing a convolutional neural network. Before teaching the network, we employed videocameras and a differential algorithm to detect all items moving in the vineyard. Such objects revealed in the images were labeled and then used in training, testing, and validating the network. The assessment of the detection algorithm required evaluating the parameters precision, recall, and F1 score. In terms of function, the algorithm is implemented in a module consisting of a microcomputer and a connected videocamera. When a flock is detected, the microcontroller will generate a signal to be wirelessly transmitted to the module, whose task is to trigger the scaring actuator.

show abstract

“…The comparison results found that YOLO has the highest speed performance, while mask R-CNN has the best detection ability 31 . Mask R-CNN and YOLOv3 were used to detect birds, and the results indicated mask R-CNN had superior detection capabilities to YOLOv3 32 .…”

Section: Introductionmentioning

confidence: 99%

Automatic wild bird repellent system that is based on deep-learning-based wild bird detection and integrated with a laser rotation mechanism

Chen,

Chu,

Hsieh

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Wild bird repulsion is critical in agriculture because it helps avoid agricultural food losses and mitigates the risk of avian influenza. Wild birds transmit avian influenza in poultry farms and thus cause large economic losses. In this study, we developed an automatic wild bird repellent system that is based on deep-learning-based wild bird detection and integrated with a laser rotation mechanism. When a wild bird appears at a farm, the proposed system detects the bird’s position in an image captured by its detection unit and then uses a laser beam to repel the bird. The wild bird detection model of the proposed system was optimized for detecting small pixel targets, and trained through a deep learning method by using wild bird images captured at different farms. Various wild bird repulsion experiments were conducted using the proposed system at an outdoor duck farm in Yunlin, Taiwan. The statistical test results of our experimental data indicated that the proposed automatic wild bird repellent system effectively reduced the number of wild birds in the farm. The experimental results indicated that the developed system effectively repelled wild birds, with a high repulsion rate of 40.3% each day.

show abstract

Evaluation Methods of Bird Repellent Devices in Optimizing Crop Production in Agriculture

Cited by 7 publications

References 9 publications

Design, Development and Evaluation of an Intelligent Animal Repelling System for Crop Protection Based on Embedded Edge-AI

Design, Development and Evaluation of an Intelligent Animal Repelling System for Crop Protection Based on Embedded Edge-AI

A System Using Artificial Intelligence to Detect and Scare Bird Flocks in the Protection of Ripening Fruit

Automatic wild bird repellent system that is based on deep-learning-based wild bird detection and integrated with a laser rotation mechanism

Contact Info

Product

Resources

About