Identification of Coffee Borer and Rust in Coffee Plants Using Digital Image Processing and Convolutional Neural Networks - BRAGA VENTURA , CHARLY (2024)
The global demand for coffee increases every year, and Brazil is one of the largest producers worldwide. With the high volume of production, there is a growing need to improve the quality of the product due to the demands of both national and international markets. However, pests such as the coffee borer (Leucoptera coffeella) and rust (Hemileia vastatrix) cause significant damage to coffee plantations, leading to annual crop losses. Several methods and techniques have been developed and applied to assess infestation levels and control these pests. Among these techniques are the use of computer vision and convolutional neural networks (CNN). Thus, the goal of this work was to develop a computational tool to correctly identify the presence of pests, reducing evaluation time, the evaluator's error, and labor costs. The accuracy of the developed methods ranged from 99.67% to 97.00%.