A Framework for Agricultural Pest and Disease Monitoring Based on Internet-of-Things and Unmanned Aerial Vehicles

Abstract

With the development of information technology, Internet-of-Things (IoT) and low-altitude remote-sensing technology represented by Unmanned Aerial Vehicles (UAVs) are widely used in environmental monitoring fields. In agricultural modernization, IoT and UAV can monitor the incidence of crop diseases and pests from the ground micro and air macro perspectives, respectively. IoT technology can collect real-time weather parameters of the crop growth by means of numerous inexpensive sensor nodes. While depending on spectral camera technology, UAVs can capture the images of farmland, and these images can be utilize for analyzing the occurrence of pests and diseases of crops. In this work, we attempt to design an agriculture framework for providing profound insights into the specific relationship between the occurrence of pests/diseases and weather parameters. Firstly, considering that most farms are usually located in remote areas and far away from infrastructure, making it hard to deploy agricultural IoT devices due to limited energy supplement, a sun tracker device is designed to adjust the angle automatically between the solar panel and the sunlight for improving the energy-harvesting rate. Secondly, for resolving the problem of short flight time of UAV, a flight mode is introduced to ensure the maximum utilization of wind force and prolong the fight time. Thirdly, the images captured by UAV are transmitted to the cloud data center for analyzing the degree of damage of pests and diseases based on spectrum analysis technology. Finally, the agriculture framework is deployed in the Yangtze River Zone of China and the results demonstrate that wheat is susceptible to disease when the temperature is between 14 • C and 16 • C, and high rainfall decreases the spread of wheat powdery mildew.

Applications of Biotechnology for Characterization of Plants and Pests as the Key Components of Plant Protection and Production Strategies: A Review

“…These systems have been used in collecting, mapping, analyzing the distribution and predicting the scenario of pests and crop yield (Sabtu et al, 2018;Anwer and Singh, 2019;Gebeyehu, 2019). With the development of information technology, Internet-of-Things (IoT) and low-altitude remote-sensing technology represented by Unmanned Aerial Vehicles (UAVs) are widely used in environmental monitoring fields (Gao et al, 2020).…”

A Sparse Deep Transfer Learning Model and Its Application for Smart Agriculture

“…Traditional in-field plant pest and disease identification methods rely on human's experience of manual observation and evaluation, with relatively low accuracy and efficiency in detection. With the development of intelligent agriculture, more technologies, i.e., remote sensing, IoT devices, computer vision [4], and unmanned aerial vehicle (UAV) [5], are providing new tools for in-field plant pest and disease detection based on automated image recognition, which can help with large-scale early identification. At the same time, the data is growing.…”

Aplicando Redes Neurais Convolucionais em Imagens para Reconhecimento Automatizado de Abelhas Melíferas (Apis mellifera L.)

“…Dentre os meios de se elevar a produtividade de plantios sem desmatar mais áreas, existe a introdução e o manejo de agentes polinizadores nas áreas de plantio [Milfont et al 2013, Bomfim et al 2016. Outra forma é por meio da utilização de tecnologias para monitoramento e identificação precoce de pragas nos plantios [Gao et al 2020, Iost Filho et al 2020, como o Veículo Aéreo Não tripulado (VANT ou drone) e a visão computacional, que por sua vez possui a finalidade de detectar, segmentar, reconhecer e/ou identificar detalhes de regiões de interesse em uma determinada imagem, de modo automático ou semiautomático [Gonzalez andWoods 2011, Tian et al 2020].…”