MÉTODOS PARA ANÁLISE DE DANO FOLIAR E RECONHECIMENTO DE PRAGAS NA AGRICULTURA USANDO TÉCNICAS COMPUTACIONAIS

Abstract

The application of computer techniques in agriculture has significantly improved rural activities, particularly crop monitoring, plant protection, and overall yield. This thesis emphasizes leaf analysis as a valuable tool for inspecting and continually improving plantations, as well as supporting decision-making and agricultural management interventions. Changes in leaves can lead to irreparable losses in productivity, the delivery of low-quality products, and significant economic impacts. To prevent production failures, it is crucial to efficiently monitor and identify whether pests are affecting productivity or remaining within acceptable levels. However, damage to the leaf silhouette can limit automated analysis, and the diversity in leaf shape and damage levels makes it challenging to delineate the compromised edge regions. This study introduces original computer-based methods for defoliation estimate, damage detection, leaf surface reconstruction, and pest classification that are prepared to address damage to the leaf boundaries. Notable aspects of this study include template matching for pattern recognition and pest classification using only traces of leaf damage. The methodological design of the study consists of a literature review, investigation of digital image processing techniques, computer vision and machine learning, software development, and formulation of experimental tests. The results indicate high accuracy in estimating leaf area loss with a linear correlation of 0.98, damage detection and pest classification with assertiveness above 90%, and visual restoration of regions affected by herbivory with SSIM scores between 0.68 and 0.94.