In response to significant shifts in dietary and lifestyle preferences, the global demand for fruits has increased dramatically, especially for apples, which are consumed worldwide. Growing apple orchards of more productive and higher quality with limited land resources is the way forward. Precise planting age identification and yield prediction are indispensable for the apple market in terms of sustainable supply, price regulation, and planting management. The planting age of apple trees significantly determines productivity, quality, and yield. Therefore, we integrated the time-series spectral endmember and logistic growth model (LGM) to accurately identify the planting age of apple orchard, and we conducted planting age-driven yield prediction using a neural network model. Firstly, we fitted the time-series spectral endmember of green photosynthetic vegetation (GV) with the LGM. By using the four-points method, the environmental carrying capacity (ECC) in the LGM was available, which serves as a crucial parameter to determine the planting age. Secondly, we combined annual planting age with historical apple yield to train the back propagation (BP) neural network model and obtained the predicted apple yields for 12 counties. The results show that the LGM method can accurately estimate the orchard planting age, with Mean Absolute Error (MAE) being 1.76 and the Root Mean Square Error (RMSE) being 2.24. The strong correlation between orchard planting age and apple yield was proved. The results of planting age-driven yield prediction have high accuracy, with the MAE up to 2.95% and the RMSE up to 3.71%. This study provides a novel method to accurately estimate apple orchard planting age and yields, which can support policy formulation and orchard planning in the future.

Southeast Asia plays a crucial role in global food production and trade, yet it grapples with challenges related to food security, regional stability, and security. Cultivated land is the material foundation for ensuring food production. With the development of society and the economy, people’s food consumption has undergone significant changes. This paper employs a comprehensive approach to analyze trends in food consumption, the cultivated land footprint, and associated land pressures in Southeast Asia over the period 1961–2019. The main findings are as follows: (1) Between 1961 and 2019, the total food consumption in Southeast Asia surged by 3.1 times. Notably, the proportion of livestock-based foods increased steadily from 6.62% in 1961 to 16.82% in 2019. (2) Due to advancements in agricultural productivity across Southeast Asia, the cultivated land footprint for food consumption only increased by 0.7 times, showcasing a diminishing demand for grain-cultivated land. (3) On the whole, the pressure of food consumption on cultivated land in Southeast Asia is on the decline, albeit with considerable variations among different countries. The Philippines is facing a relative undersupply, whereas Thailand has experienced the lowest cultivated land pressure. (4) Encouraging a shift towards a Mediterranean-style diet, aligned with existing dietary patterns, holds promise for reducing future pressures on cultivated land and promoting better health outcomes for the populace in Southeast Asia.