An AIoT Based Smart Agricultural System for Pests Detection

Decrease in crop yield and degradation in product quality due to plant diseases such as rust and blast in pearl millet is the cause of concern for farmers and the agriculture industry. The stipulation of expert advice for disease identification is also a challenge for the farmers. The traditional techniques adopted for plant disease detection require more human intervention, are unhandy for farmers, and have a high cost of deployment, operation, and maintenance. Therefore, there is a requirement for automating plant disease detection and classification. Deep learning and IoT-based solutions are proposed in the literature for plant disease detection and classification. However, there is a huge scope to develop low-cost systems by integrating these techniques for data collection, feature visualization, and disease detection. This research aims to develop the ‘Automatic and Intelligent Data Collector and Classifier’ framework by integrating IoT and deep learning. The framework automatically collects the imagery and parametric data from the pearl millet farmland at ICAR, Mysore, India. It automatically sends the collected data to the cloud server and the Raspberry Pi. The ‘Custom-Net’ model designed as a part of this research is deployed on the cloud server. It collaborates with the Raspberry Pi to precisely predict the blast and rust diseases in pearl millet. Moreover, the Grad-CAM is employed to visualize the features extracted by the ‘Custom-Net’. Furthermore, the impact of transfer learning on the ‘Custom-Net’ and state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19 is shown in this manuscript. Based on the experimental results, and features visualization by Grad-CAM, it is observed that the ‘Custom-Net’ extracts the relevant features and the transfer learning improves the extraction of relevant features. Additionally, the ‘Custom-Net’ model reports a classification accuracy of 98.78% that is equivalent to state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19. Although the classification of ‘Custom-Net’ is comparable to state-of-the-art models, it is effective in reducing the training time by 86.67%. It makes the model more suitable for automating disease detection. This proves that the proposed model is effective in providing a low-cost and handy tool for farmers to improve crop yield and product quality.

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“…To identify the suitable sensors for the AIDCC, the authors referred to the system proposed in [ 57 ]. They used four sensors viz.…”

Section: Methodsmentioning

confidence: 99%

IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet

Kundu

Rani

Dhaka

et al. 2021

Sensors

140

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“…However, the number of samples of the two categories of eggs and nymphs hatched in the past 30 mins was only about 50-80, far less than the other categories nymphs hatched more than 30 mins ago, older nymphal instars, and adultswhich had more than 200 samples each. To improve the accuracy of YOLOV3 and Tiny-YoLov3 models in identifying the five categories of stages and instars of T. papillosa, the images of eggs and nymphs hatched in the past 30 mins were increased by the image augmentation method [28] to make them equivalent in number to the other three categories. Then we used the sample compensation method to reach a total number of training samples of about 5000 images, which included all five categories.…”

Section: Figure 2 the Different Life Stages And Instars Of T Papillosamentioning

confidence: 99%

Identification of Fruit Tree Pests With Deep Learning on Embedded Drone to Achieve Accurate Pesticide Spraying

et al. 2021

Self Cite

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Tessaratoma papillosa (Drury) first invaded Taiwan in 2009. Every year, T. papillosa causes severe damage to the longan crops. Novel applications for edge intelligence are applied in this study to establish an intelligent pest recognition system to manage this pest problem. We used a detecting drone to photograph the pest and employed a Tiny-YOLOv3 neural network model built on an Embedded system NVIDIA Jetson TX2 to recognize T. papillosa in the orchard to determine the position of the pests in realtime. The pests' positions are then used to plan the optimal pesticide spraying route for the agricultural drone. Apart from planning the optimized spraying of pesticide for the spraying drone, the TX2 embedded platform also transmits the position and generation of pests to the cloud to record and analyze the growth of longan with a computer or mobile device. This study enables farmers to understand the pest distribution and take appropriate precautions in real-time. The agricultural drone sprays pesticides only where needed, which reduces pesticide use, decreases damage to the environment, and increases crop yield.

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“…Garbage collectors are used in a variety of settings, including businesses, offices, schools, hospitals, and streets [1]. Waste management plays a crucial role in creating a clean environment in cities across the globe [2].…”

Section: Introductionmentioning

confidence: 99%

Smart Garbage Bin Based on AIoT

Sung¹,

Devi²,

Hsiao³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Waste management and monitoring is a major concern in the context of the environment, and has a significant impact on human health. The concept of the Artificial Intelligence of Things (AIoT) can help people in everyday tasks in life. This study proposes a smart trash bin to help solve the problem of waste management and monitoring. Traditional methods of garbage disposal require human labor, and pose a hazard to the worker. The proposed smart garbage bin can move itself by using ultrasonic sensors and a web camera, which serves as its "eyes." Because the smart garbage bin is designed for indoor use and mobility, we use a Bluetooth beacon for location tracking, whereby the bin transmits a constant signal that the other devices in the environment can detect. A smart garbage bin was developed in this study to be used in the E315 research room in the Electrical Engineering Department of the National Chin-Yi University of Technology. An Android application was also developed to help users monitor waste in the smart bin.

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An AIoT Based Smart Agricultural System for Pests Detection

Cited by 146 publications

References 10 publications

IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet

IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet

Identification of Fruit Tree Pests With Deep Learning on Embedded Drone to Achieve Accurate Pesticide Spraying

Smart Garbage Bin Based on AIoT

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