Detecção automática de doenças em frutos do mamão a partir da análise de imagens por meio de redes neurais profundas

• Autor(a) principal: Moraes, Jairo Lucas de
• Data de Publicação: 2023
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Repositório Institucional da Universidade Federal do Espírito Santo (riUfes)
• Texto Completo: http://repositorio.ufes.br/handle/10/17171
• topic: Ciência da Computação; Visão computacional em dispositivos móveis; Agricultura de precisão; Doenças em frutas; Aprendizado Profundo; CBAM; Mamão; Carica Papaya; subject.br-rjbn

Horticulture plays an essential role in the economies of various countries, serving as a significant source of income and job creation, particularly in developing nations. Within this sector, papaya holds substantial importance, being cultivated in over 60 countries, including Brazil, which stands as the second-largest producer of this fruit. Papaya is a delicate and climacteric fruit, leading to considerable post-harvest losses, underscoring the pivotal role of early detection and accurate classification of fruit injuries in quality control and loss mitigation. Presently, papaya quality control is conducted manually, demanding exhaustive and repetitive efforts, often necessitating specialized knowledge that may not always be readily available to small-scale farmers or small fruit processing facilities. Given this backdrop, the implementation of autonomous or semi-autonomous system solutions aimed at assisting in papaya quality control, including disease detection and physical damage identification, is highly desirable. Such solutions could effectively mitigate industry losses, offering a more efficient, precise, and reliable approach to ensuring fruit quality and maximizing productivity in the sector. In this thesis, we propose a comprehensive solution spanning from the creation of an unprecedented dataset in the literature to the development of a mobile application. This includes the implementation of novel convolutional neural network (CNN) architectures utilizing the Convolutional Block Attention Module (CBAM). Our dataset comprises more than 23,000 examples of eight types of injuries (Anthracnose, Phytophthora, Chocolate Spot, Sticky Disease, Black Spot, Physiological Spot, Mechanical Damage, and Scar) affecting papaya fruits, alongside examples of healthy fruits. The developed detector achieves a new state-ofthe-art in papaya fruit disease detection, with an average precision (mAP) of 86.2%. This performance significantly surpasses that of human experts, who achieved an average precision of 67.3%. Lastly, we optimized the structure and weights of our detector for use on mobile devices and created a robust mobile application that can run on common smartphones. It can detect diseases in papaya fruits at a rate of up to 6 frames per second without requiring additional resources.