Publicação

Peaches detection using a deep learning technique — A contribution to yield estimation resources management, and circular economy.

Ver documento

Detalhes bibliográficos
Resumo:Fruit detection is crucial for yield estimation and fruit picking system performance. Many state-of-the-art methods for fruit detection use convolutional neural networks (CNNs). This paper presents the results for peach detection by applying a faster R-CNN framework in images captured from an outdoor orchard. Although this method has been used in other studies to detect fruits, there is no research on peaches. Since the fruit colors, sizes, shapes, tree branches, fruit bunches, and distributions in trees are particular, the development of a fruit detection procedure is specific. The results show great potential in using this method to detect this type of fruit. A detection accuracy of 0.90 using the metric average precision (AP) was achieved for fruit detection. Precision agriculture applications, such as deep neural networks (DNNs), as proposed in this paper, can help to mitigate climate change, due to horticultural activities by accurate product prediction, leading to improved resource management (e.g., irrigation water, nutrients, herbicides, pesticides), and helping to reduce food loss and waste via improved agricultural activity scheduling.
Autores principais:Assunção, Eduardo
Outros Autores:Gaspar, Pedro Dinis; Mesquita, Ricardo; Simões, M.P.; Ramos, António Santos; Proença, Hugo; Inácio, Pedro R.M.
Assunto:Convutional neural network Deep learning Fruit detection Precision agriculture Sustainability
Ano:2022
País:Portugal
Tipo de documento:artigo
Tipo de acesso:acesso aberto
Instituição associada:Instituto Politécnico de Castelo Branco
Idioma:inglês
Origem:Repositório Científico do Instituto Politécnico de Castelo Branco

Registos relacionados

Registos relacionados