Artificial intelligence-driven classification method of grapevine phenology using conventional RGB imaging

Abstract

The phenological stage of the grapevine (Vitis vinifera L.) represents a fundamental element in vineyard management, since it determines key practices such as fertilization, irrigation, phytosanitary interventions and optimal harvest time (Mullins et al., 1992). Phenology can be understood as the study of the phenological events, or the stages of plant development that occur during their active lifecycle, in response to climatic conditions. The phenological development of grapevines is mainly influenced by climatic elements, such as temperature, solar radiation, and precipitation, which also influence the production and the quality of grape berries. Temperature is the main forcing element in the phenology of grapevines, in this sense, the projected increases in temperature under future likely climate change scenarios may lead to the advancement of 6 to 25 days for different grapevine varieties in mediterranean climate regions (Reis et al., 2020).

To standardize the description of these stages, several phenological scales have been developed, including the Baggiolini scale, the Eichhorn-Lorenz (E-L) scale, and the extended BBCH scale. The Baggiolini scale, initially used for planning pesticide applications, was limited in scope as it only covered early development stages. In contrast, the E-L scale introduced 47 numerical codes describing 22 phenological stages from winter bud to leaf fall, providing greater detail and flexibility to incorporate sub-stages (Coombe, 1995). The extended BBCH scale increased precision by detailing phenological macro and micro-stages, facilitating its application across multiple crops and standardizing its use internationally (Lorenz et al., 1995).

Despite advances in these descriptive tools, phenological identification traditionally relies on manual observations by an experienced technical person. This method is time-intensive, subjective, and often unable to capture spatial variability within the vineyard, which can lead to suboptimal decisions (Verdugo-Vásquez et al., 2016). In addition, climatic variations, edaphic conditions and agronomic practices can further complicate correct identification (Altimiras et al., 2024).

In this context, emerging technologies, such as computer vision and deep learning, offer a promising solution. These tools provide a possibility to automatize the classification and monitoring of phenological stages by capturing and analysing large-scale images. Algorithms based on convolutional neural networks (CNNs) have proven to be effective for image classification techniques in agriculture, with accuracies exceeding 88% in applications such as grapevine phenological stage identification (Schieck et al., 2023). In addition, these technologies have also been successfully used in the detection of grape bunches under various occlusion conditions (Íñiguez et al., 2024) and in the assessment of diseases such as downy mildew (Hernández et al., 2021). Systems that integrate proximity sensors and IoT platforms are integrating the capacity for continuous and real-time monitoring in vineyards, reducing costs and improving decision making (Mendes et al., 2022).