Analysis of pest incidence on apple trees validated by unsupervised machine learning algorithms

Abstract

Integrated pest control is a practice commonly used in apple orchards in southern Brazil. This type of management is an important tool to help improve quality and increase yields. This study aimed to identify areas with higher and lower incidence of aerial pests in a commercial apple orchard, regarding data collected from three different crops using georeferenced traps. Geostatistical analyses were performed, based on the modeling of semivariograms and spatial interpolation using the kriging method; and clustering, based on specific unsupervised machine learning algorithms for count data. The algorithms were selected from measures of stability, connectivity and homogeneity, seeking to identify areas with different incidence of pests that could help farmer decision making regarding insect population control using pesticides. The geostatistical analysis verified the presence of individual pest infestations in specific sites of the study area. Additionally, the analysis using machine learning allowed the identification of areas with incidence above the average for all analyzed pests, especially in the central area of the map. The process of evaluation described in this study can serve as an aid for risk analysis, promoting management benefits and reducing cost in the farms.

Maize Crop Detection through Geo-Object-Oriented Analysis Using Orbital Multi-Sensors on the Google Earth Engine Platform

“…Remote sensing and geoprocessing are the most use techniques for land use data generation over time, as they allow the evaluation of changes in the landscape [9]. Remote sensing imagery provides important historical series for crop dynamics identification, and spatial resolution allows precision agriculture-level interventions [10] Geographic object-based image analysis (GEOBIA) has emerged as a powerful methodology for image analysis and classification, proving effective in accurately identifying and classifying land cover types, mapping and monitoring deforestation, monitoring vegetation health and growth, predicting crop yields, and classifying remote sensing imagery [11]. By utilizing spectral, spatial, textural and topological features, GEOBIA enables comprehensive image analysis, providing valuable information about the characteristics of and changes in natural and agricultural landscapes [12].…”