Prediction of Pest Insect Appearance Using Sensors and Machine Learning

The appearance of pest insects can lead to a loss in yield if farmers do not respond in a timely manner to suppress their spread. Occurrences and numbers of insects can be monitored through insect traps, which include their permanent touring and checking of their condition. Another more efficient way is to set up sensor devices with a camera at the traps that will photograph the traps and forward the images to the Internet, where the pest insect’s appearance will be predicted by image analysis. Weather conditions, temperature and relative humidity are the parameters that affect the appearance of some pests, such as Helicoverpa armigera. This paper presents a model of machine learning that can predict the appearance of insects during a season on a daily basis, taking into account the air temperature and relative humidity. Several machine learning algorithms for classification were applied and their accuracy for the prediction of insect occurrence was presented (up to 76.5%). Since the data used for testing were given in chronological order according to the days when the measurement was performed, the existing model was expanded to take into account the periods of three and five days. The extended method showed better accuracy of prediction and a lower percentage of false detections. In the case of a period of five days, the accuracy of the affected detections was 86.3%, while the percentage of false detections was 11%. The proposed model of machine learning can help farmers to detect the occurrence of pests and save the time and resources needed to check the fields.

[1]  Siti Khairunniza Bejo,et al.  Machine-Learning Approach Using SAR Data for the Classification of Oil Palm Trees That Are Non-Infected and Infected with the Basal Stem Rot Disease , 2021, Agronomy.

[2]  Syed Ali Hassan,et al.  Precision Agriculture Techniques and Practices: From Considerations to Applications , 2019, Sensors.

[3]  Zhang,et al.  Machine Learning-Based Spectral Library for Crop Classification and Status Monitoring , 2019, Agronomy.

[4]  Frederick W. Maier,et al.  Using Machine Learning and Feature Selection for Alfalfa Yield Prediction , 2021, AI.

[5]  L. V. Nedorezov,et al.  Host–parasitoid population density prediction using artificial neural networks: diamondback moth and its natural enemies , 2010 .

[6]  Ioannis Malounas,et al.  Testing the Suitability of Automated Machine Learning for Weeds Identification , 2021, AI.

[7]  P. Sreekanth,et al.  Predicting the oriental fruit fly Bactrocera dorsalis (Diptera: Tephritidae) trap catch using artificial neural networks: a case study , 2011 .

[8]  Hong Sun,et al.  Machine learning-based in-season nitrogen status diagnosis and side-dress nitrogen recommendation for corn , 2021 .

[9]  Matteo Golfarelli,et al.  Crop Management with the IoT: An Interdisciplinary Survey , 2021, Agronomy.

[10]  Jayme Garcia Arnal Barbedo,et al.  Detecting and Classifying Pests in Crops Using Proximal Images and Machine Learning: A Review , 2020, AI.

[11]  P. Robert.  Precision agriculture: a challenge for crop nutrition management , 2002, Plant and Soil.

[12]  Ningbo Cui,et al.  Estimation of soil temperature from meteorological data using different machine learning models , 2019, Geoderma.

[13]  Y. Cohen,et al.  Predicting Heliothis (Helicoverpa armigera) pest population dynamics with an age-structured insect population model driven by satellite data , 2018 .

[14]  T. Scholten,et al.  Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models , 2020, Agronomy.

[15]  Salah Sukkarieh,et al.  Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review , 2018, Comput. Electron. Agric..

[16]  James C. Schnable,et al.  Plant segmentation by supervised machine learning methods , 2020, The Plant Phenome Journal.

[17]  Giuseppe Pirlo,et al.  Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement , 2018, Machines.

[18]  L. Parent,et al.  Humboldtian Diagnosis of Peach Tree (Prunus persica) Nutrition Using Machine-Learning and Compositional Methods , 2020, Agronomy.

[19]  Tripathi,et al.  Predicting Rice Pest Population Occurrence with Satellite-Derived Crop Phenology, Ground Meteorological Observation, and Machine Learning: A Case Study for the Central Plain of Thailand , 2019, Applied Sciences.

[20]  Jayme Garcia Arnal Barbedo,et al.  A Study on CNN-Based Detection of Psyllids in Sticky Traps Using Multiple Image Data Sources , 2020, AI.

[21]  K. Lazányi.  Content , 2018, European Neuropsychopharmacology.

[22]  Yong He,et al.  A survey on the 5G network and its impact on agriculture: Challenges and opportunities , 2021, Comput. Electron. Agric..

[23]  David W. Franzen,et al.  Application of Machine Learning Methodologies for Predicting Corn Economic Optimal Nitrogen Rate , 2018, Agronomy Journal.

[24]  Mohsen Shahhosseini,et al.  Maize yield and nitrate loss prediction with machine learning algorithms , 2019, Environmental Research Letters.

[25]  Chi-Hua Chen,et al.  Deep Learning Techniques for Agronomy Applications , 2019, Agronomy.

[26]  Anca L. Ralescu,et al.  Confusion Matrix-based Feature Selection , 2011, MAICS.

[27]  Christophe Chesneau,et al.  The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach , 2021, Agronomy.

[28]  Lutz Plümer,et al.  A review of advanced machine learning methods for the detection of biotic stress in precision crop protection , 2014, Precision Agriculture.

[29]  Srinivasarao Cherukumalli,et al.  Prediction of Helicoverpa armigera Hubner on pigeonpea during future climate change periods using MarkSim multimodel data , 2016 .

[30]  Tin Kam Ho,et al.  Machine Learning Made Easy: A Review of Scikit-learn Package in Python Programming Language , 2019, Journal of Educational and Behavioral Statistics.

[31]  Constantino Valero,et al.  Automatic Detection and Monitoring of Insect Pests—A Review , 2020, Agriculture.

[32]  Leonardo Felipe Maldaner,et al.  Carrot Yield Mapping: A Precision Agriculture Approach Based on Machine Learning , 2020 .

[33]  Aitazaz A. Farooque,et al.  Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms , 2020, Agronomy.

[34]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[35]  François Waldner,et al.  Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods , 2020 .

[36]  Albert Y. Zomaya,et al.  Forecasting yield by integrating agrarian factors and machine learning models: A survey , 2018, Comput. Electron. Agric..