Prediction of Pest Insect Appearance Using Sensors and Machine Learning

Marković, Dušan; Vujicic, Dejan; Tanasković, Snežana; Đorđević, Branislav; Randic, Sinisa; Stamenković, Zoran

doi:10.3390/s21144846

Cited by 37 publications

(9 citation statements)

References 35 publications

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“…Topic Solution [74], [78]- [80] Pest Control Pest Classification [75]- [77], [81]- [84] Pest Control Pest Identification or Detection [85] Pest Control Pest Prediction…”

Section: Referencesmentioning

confidence: 99%

A Systematic Review of Current Trends in Artificial Intelligence for Smart Farming to Enhance Crop Yield

Widianto

Ardimansyah²,

Pohan³

et al. 2022

Journal of Robotics and Control

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Current technology has been widely applied for development, one of which has an Artificial Intelligence (AI) applied to Smart Farming. AI can give special capabilities to be programmed as needed. In cooperation with agricultural systems, AI is part of improving the quality of agriculture. This technology is no stranger to being applied in basic fields such as agriculture. This smart technology is needed to increase crop yields for various regions by utilizing the current trends paper. This is necessary because less land is available for agriculture, and there is a greater need for food sources. Therefore, this systematic review aims to collect the current trends in AI studies for Smart Farming papers using the latest year features from 2018-2022. This paper is handy for researchers and industry in looking for the latest papers on research to enhance crop yields. The authors utilized Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) of 534 articles from IEEE, ACM, MDPI, IAES, and ScienceDirect. After going through a careful process, 67 papers were found that were judged according to the criteria. After the authors got some of the current trends, the author has discussed several factors regarding the results obtained to enhance crop yields, such as Weather, Soil, Irrigation, Unmanned Aerial Vehicle (UAV), Pest Control, Weed Control, and Disease Control.

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“…Topic Solution [74], [78]- [80] Pest Control Pest Classification [75]- [77], [81]- [84] Pest Control Pest Identification or Detection [85] Pest Control Pest Prediction…”

Section: Referencesmentioning

confidence: 99%

A Systematic Review of Current Trends in Artificial Intelligence for Smart Farming to Enhance Crop Yield

Widianto

Ardimansyah²,

Pohan³

et al. 2022

Journal of Robotics and Control

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“…Based on anomaly data they used to predict borer attack in rice crop. Markovic et al [4] used IoT sensors with camera on insect trap to detect which type of insects are present in the field. Environmental data collected from sensors and insects detected from images is used to train machine learning algorithm and finally a prediction is achieved using captured data helping farmers to know possible pest attack and reduce efforts to physically visit farms.…”

Section: Related Workmentioning

confidence: 99%

Pest Prediction in Rice using IoT and Feed Forward Neural Network

2022

KSII TIIS

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Rice is a fundamental staple food commodity all around the world. Globally, it is grown over 167 million hectares and occupies almost 1/5 th of total cultivated land under cereals. With a total production of 782 million metric tons in 2018. In Pakistan, it is the 2 nd largest crop being produced and 3 rd largest food commodity after sugarcane and rice. The stem borers a type of pest in rice and other crops, Scirpophaga incertulas or the yellow stem borer is very serious pest and a major cause of yield loss, more than 90% damage is recorded in Pakistan on rice crop. Yellow stem borer population of rice could be stimulated with various environmental factors which includes relative humidity, light, and environmental temperature. Focus of this study is to find the environmental factors changes i.e., temperature, relative humidity and rainfall that can lead to cause outbreaks of yellow stem borers. this study helps to find out the hot spots of insect pest in rice field with a control of farmer's palm. Proposed system uses temperature, relative humidity, and rain sensor along with artificial neural network to predict yellow stem borer attack and generate warning to take necessary precautions. result shows 85.6% accuracy and accuracy gradually increased after repeating several training rounds. This system can be good IoT based solution for pest attack prediction which is cost effective and accurate.

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“…The logical model tree, RF, SVM, simple logistic regression, multilayer perceptron, and AdaBoost algorithms can classify the species and gender of the pests, but the performance of the RF and SVM algorithms is low [35]. Furthermore, the machine learning model combined with the temperature and humidity conditions can predict the probability of pest occurrence, but the prediction accuracy is only 76.5% [36]. The prediction accuracy of plant diseases combined with machine learning algorithms and visible light data is also low.…”

Section: Application Potential Of Machine Learning Algorithm In Yellow Leaf Disease Of Arecanut Monitoringmentioning

confidence: 99%

Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors

et al. 2021

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Unmanned aerial vehicle (UAV) remote sensing technology can be used for fast and efficient monitoring of plant diseases and pests, but these techniques are qualitative expressions of plant diseases. However, the yellow leaf disease of arecanut in Hainan Province is similar to a plague, with an incidence rate of up to 90% in severely affected areas, and a qualitative expression is not conducive to the assessment of its severity and yield. Additionally, there exists a clear correlation between the damage caused by plant diseases and pests and the change in the living vegetation volume (LVV). However, the correlation between the severity of the yellow leaf disease of arecanut and LVV must be demonstrated through research. Therefore, this study aims to apply the multispectral data obtained by the UAV along with the high-resolution UAV remote sensing images to obtain five vegetation indexes such as the normalized difference vegetation index (NDVI), optimized soil adjusted vegetation index (OSAVI), leaf chlorophyll index (LCI), green normalized difference vegetation index (GNDVI), and normalized difference red edge (NDRE) index, and establish five algorithm models such as the back-propagation neural network (BPNN), decision tree, naïve Bayes, support vector machine (SVM), and k-nearest-neighbor classification to determine the severity of the yellow leaf disease of arecanut, which is expressed by the proportion of the yellowing area of a single areca crown (in percentage). The traditional qualitative expression of this disease is transformed into the quantitative expression of the yellow leaf disease of arecanut per plant. The results demonstrate that the classification accuracy of the test set of the BPNN algorithm and SVM algorithm is the highest, at 86.57% and 86.30%, respectively. Additionally, the UAV structure from motion technology is used to measure the LVV of a single areca tree and establish a model of the correlation between the LVV and the severity of the yellow leaf disease of arecanut. The results show that the relative root mean square error is between 34.763% and 39.324%. This study presents the novel quantitative expression of the severity of the yellow leaf disease of arecanut, along with the correlation between the LVV of areca and the severity of the yellow leaf disease of arecanut. Significant development is expected in the degree of integration of multispectral software and hardware, observation accuracy, and ease of use of UAVs owing to the rapid progress of spectral sensing technology and the image processing and analysis algorithms.

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Prediction of Pest Insect Appearance Using Sensors and Machine Learning

Cited by 37 publications

References 35 publications

A Systematic Review of Current Trends in Artificial Intelligence for Smart Farming to Enhance Crop Yield

A Systematic Review of Current Trends in Artificial Intelligence for Smart Farming to Enhance Crop Yield

Pest Prediction in Rice using IoT and Feed Forward Neural Network

Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors

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