# Prediction of Harvest Time of Apple Trees: An RNN-Based Approach
## 2 Related Works  
The prediction of phenological stages has been extensively investigated in the literature [1; 16; 17; 18; 19; 20; 21]. Many initiatives are found, which use different methods to offer solutions for the most varied cultures. Among the works related to apple phenology, in [PERSON] et al. [1], the authors present studies on the phenology of apple trees in subtropical climatic conditions. The study uses phenological and climatic data, using a statistical approach, and presents important conclusions about the impacts on fruit production under subtropical climate conditions. In [PERSON] et al. [16], the authors evaluated the phenological development of different fruit structures of apple trees with the objective of characterizing the behavior of the phenological period from the complete flowering until the beginning of the ripening of the apple. In [PERSON] and [PERSON] [18], the fruit growth of apple cultivars was evaluated. The authors recommend using the relationship between the diameters of fruits at the T stage and fruits reached at harvest maturity to predict the harvest time and variety yield of four apple cultivars. In general, these works use statistical approaches, based on experiments, observing the evolution of the stages to verify the effect of the amount of cold or heat and to characterize the behavior of the phenological period of the apple. The authors conclude that the greater the number of cold units in dormancy, the shorter the time and need for heat for sprouting. Due to the peculiarities of fruit growing, the approaches generally developed for commodities need adaptations. In particular, there is a need to use unique methods with specific cultivation characteristics, which makes it difficult to apply techniques from other cultures without adaptation. Thermal sum models, in the form of degree-days, have been adjusted to estimate heat accumulation [21]. These models represent the integration of effective temperatures for plant growth, which is fixed between the lower and upper limits. In [PERSON] et al. [19], the authors investigated the use of methods Fuzzy-based Time Series (FTS) to predict phenological stages of apple trees. The authors noted that the quality of results is improved by combining variables and using multivariate FTS methods. Studies, such as those by [PERSON] et al. [22], [PERSON] et al. [23], and [PERSON] and [PERSON] [21], evaluate the impacts of rising temperatures and climate change on the flowering season of apple trees due to global warming. These works produce statistics and relate meteorological and phenological data due to global warming and how much the initial phenological stages have changed due to climate change. Approaches using ML methods are present in [PERSON] et al. [20], [PERSON] et al. [24], [PERSON] et al. [25], and [PERSON] et al. [26]. Some authors have applied artificial neural networks (ANN) to predict phenological stages, as in [PERSON] et al. [17] and [PERSON] et al. [27]. In [PERSON] et al. [17], the authors used an ANN to anticipate different phonological phases of the apple. The authors identified that most of the error was related to the anticipation of the fruit development stage and concluded that it is possible to anticipate the phenological stages of the apple with acceptable accuracy using climatic parameters. In [PERSON] et al. [27], the authors present a study where an Artificial Neural Network was applied to predict the production of dry wheat. The results demonstrate precision and efficiency with a maximum error between 45 and 60 kg/ha. [PERSON] et al. [28] proposed a hybrid model using a data-driven model using knowledge-based predictions to predict soybean phenology. According to [PERSON] et al. [28], the potential that LSTM networks have to capture the impact depends on the sequence of environmental stimuli, which in the context of this work is analogous to, for example, a period of cold weather in plant development. In [PERSON] et al.