In this paper, we propose seasonal long short-term memory (SLSTM), which is a method for predicting the sales of agricultural products, to stabilize supply and demand. The SLSTM model is trained using the seasonality attributes of week, month, and quarter as additional inputs to historical time-series data. The seasonality attributes are entered into the SLSTM network model individually or in combination. The performance of the proposed SLSTM model was compared with those of auto_arima, Prophet, and a standard LSTM in terms of three performance metrics (mean absolute error (MAE), root mean squared error (RMSE), and normalization mean absolute error (NMAE)). The experimental results show that the error rate of the proposed SLSTM model is significantly lower than those of other classical methods.

Fruit trees in orchards are typically placed at equal distances in rows; therefore, their branches are intertwined. The precise segmentation of a target tree in this situation is very important for many agricultural tasks, such as yield estimation, phenotyping, spraying, and pruning. However, our survey on tree segmentation revealed that no study has explicitly addressed this intertwining situation. This paper presents a novel dataset in which a precise tree region is labeled carefully by a human annotator by delineating the branches and trunk of a target apple tree. Because traditional rule-based image segmentation methods neglect semantic considerations, we employed cutting-edge deep learning models. Five recently pre-trained deep learning models for segmentation were modified to suit tree segmentation and were fine-tuned using our dataset. The experimental results show that YOLOv8 produces the best average precision (AP), 93.7 box AP@0.5:0.95 and 84.2 mask AP@0.5:0.95. We believe that our model can be successfully applied to various agricultural tasks.

Accurate estimation of fruit quantity is essential for efficient harvest management, storage, transportation, and marketing in the agricultural industry. To address the limited generalizability of single-species models, this study presents a comprehensive deep learning-based framework for multi-species fruit-load estimation, leveraging the MetaFruit dataset, which contains images of five fruit species collected under diverse orchard conditions. Four representative object detection and regression models—YOLOv8, RT-DETR, Faster R-CNN, and a U-Net-based heatmap regression model—were trained and compared as part of the proposed multi-species learning strategy. The models were evaluated on both the internal MetaFruit dataset and two external datasets, NIHS-JBNU and Peach, to assess their generalization performance. Among them, YOLOv8 and the RGBH heatmap regression model achieved F1-scores of 0.7124 and 0.7015, respectively, on the NIHS-JBNU dataset. These results indicate that a deep learning-based multi-species training strategy can significantly enhance the generalizability of fruit-load estimation across diverse field conditions.