Northeast Thailand is the largest rice cultivation region in Thailand, but the rice yield there is quite low. Soil salinity is one of the major yield restricted factors, is derived from underground rock salt, and is predicted to expand in the future. This study focused on evaluating rice productivity related to salinity conditions in Khon Kaen Province, Northeast Thailand. The field investigations were conducted from 2017 to 2019 in farmer fields in severe, moderate, and slight soil salinity classes determined by the Land Development Department of Thailand. The soil salinity on the basis of the electric conductivity of saturated soil extract (ECe) varied year to year, which seemed to be associated with precipitation. The difference in soil salinity between classes was obvious only in the drought year 2018, and reflected in the rice yield, although severe drought devastated rice yield in some fields. Plenty of rainfall may have alleviated soil salinity and rice yield reduction in other years, causing differences in rice yield that were not significant among soil salinity classes. However, salinity level evaluation by the USDA based on ECe showed that rice yield was damaged depending on the level. This study indicates that ECe-based evaluation is recommended for soil salinity in relation to rice productivity. The spatial and temporal evaluation for rice production may benefit farmers. The results in this study also showed rice production largely varied even in similar salinity levels, implying that salinity damage can be alleviated by farmer management.

Rice is not merely a staple food but an important source of income in Cambodia. Rapid socioeconomic development in the country affects farmers’ management practices, and rice production has increased almost three-fold over two decades. However, detailed information about the recent changes in rice production is quite limited and mainly obtained from interviews and statistical data. Here, we analyzed MODIS LAI data (MCD152H) from 2003 to 2019 to quantify rice production changes in Pursat Province, one of the great rice-producing areas in Cambodia. Although the LAI showed large variations, the data clearly indicate that a major shift occurred in approximately 2010 after applying smoothing methods (i.e., hierarchical clustering and the moving average). This finding is consistent with the results of the interviews with the farmers, which indicate that earlier-maturing cultivars had been adopted. Geographical variations in the LAI pattern were illustrated at points analyzed along a transverse line from the mountainside to the lakeside. Furthermore, areas of dry season cropping were detected by the difference in monthly averaged MODIS LAI data between January and April, which was defined as the dry season rice index (DSRI) in this study. Consequently, three different types of dry season cropping areas were recognized by nonhierarchical clustering of the annual LAI transition. One of the cropping types involved an irrigation-water-receiving area supported by canal construction. The analysis of the peak LAI in the wet and dry seasons suggested that the increase in rice production was different among cropping types and that the stagnation of the improvements and the limitation of water resources are anticipated. This study provides valuable information about differences and changes in rice cropping to construct sustainable and further-improved rice production strategies.

In crop production, which is largely dependent on environmental conditions, various attempts at environmental or social changes have been highlighted, and many field experiments are needed for them. However, since field experiments in agricultural production are constrained by high labor and time consumption, alternative methods to respond to these constraints are required. In this study, to establish a new method for application to field experiments, we proposed the evaluation of the leaf area index (LAI) of all individual plants in an experimental sweetcorn field using an unmanned aerial vehicle (UAV). Small-scale field experiments were conducted over two years. In the first year, the nitrogen fertilizer level was changed, and the plant density and additional nitrogen fertilizer application time were changed in the next year. Three vegetation indices (VIs), namely, the normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), and simple ratio (SR), were validated to quantify the LAI estimation using a UAV for individual plants. For the evaluation of the individual plants, we used a plant-based method, which created all of the plant buffers based on the points of existing plants and the plant distance. To confirm the impact of the method, we additionally demonstrated the relationship between the LAI and yield, the results of statical analyses, and the difference of the center and the border of the field. Among the three VIs, index SR was found the most promising in the estimation of the LAI of the individual sweetcorn plants, providing the strongest correlation of yield with SR. Because a lot of data were obtained using the plant-based method, the statical differences in the LAI and yield were more easily detected for the plant density and fertilizer treatments. Furthermore, interesting differences between the center and the border of the field were found. These results indicate the availability and impact of plant-based evaluations using UAVs in near future field experiments.

Improving agricultural research and education is highly recommended to control agricultural development and environmental sustainability in Cambodia. Agricultural research mostly focuses on interviews with farmers as a first measure in developing countries, but a lack of quantitative accuracy remains one of the major constraints. In this situation, we conducted educational activities for master’s degree students of the Royal University of Agriculture (RUA) to append agronomic information with popular equipment in interdisciplinary fieldwork in Pursat Province, Cambodia. For the popular equipment, an RGB camera, a reflectometer as well as pH and EC meters were selected. The agronomic information collected by the students supported the results obtained during the interviews. For example, the difference in fertilizer application between the irrigated and nonirrigated areas was confirmed by the soil ammonium concentration evaluated with a reflectometer; the difference in rice growth among water conditions was confirmed by the leaf area percentage evaluated with an RGB camera. Since the majority of the students lacked agricultural and statistical knowledge, the agronomic information quantified by popular equipment provided proper educational materials. The interdisciplinary fieldwork also indicated serious problems in the study area, such as low beneficial crop production and environmental sustainability. To overcome these problems, improving agricultural education is required to foster skillful agricultural professionals, and the quantification of agronomic information is an essential issue.

Maize is a globally important staple crop, and automated monitoring of germination and seedling emergence is essential for precision agriculture, enabling timely reseeding and reducing potential yield loss. To address this need, we propose Seedling-DETR, a transformer-based model for the real-time detection of emerged and missing maize seedlings using multispectral UAV imagery in an end-to-end manner. First, we construct a multispectral UAV dataset and introduce a dedicated annotation strategy in which missing seedlings were labeled individually rather than inferred indirectly. Then, we modify the feature fusion module of RT-DETR and develop a hybrid-scale feature fusion module to obtain richer and more expressive feature representations for missing seedling detection and improve the precision of missing seedling detection. Finally, we propose a channel fusion module to incorporate multispectral images into our model without requiring a dedicated multispectral backbone or additional pretraining, thereby improving model adaptability. The results show that, under a random train–test split (8:2), when using RGB images as input, our Seedling-DETR achieves a mean average precision (mAP) of 83.1% at an IoU threshold of 0.5, outperforming YOLOv11x and RT-DETR by 2.5% and 1.1%, respectively. The proposed method achieves an AP of 69.3% at an IoU threshold of 0.5 for missing seedling detection, which increases to 71.7% when multispectral inputs are incorporated. Similar performance trends are observed on an independent validation set collected on a different date. Although the model introduces moderate computational overhead (282 GFLOPs for RGB input and 418 GFLOPs for multispectral configuration, with 84.0 M and 85.1 M parameters, respectively), it can maintain efficient detection performance suitable for actual agricultural field deployment. The method is further validated at the field scale using orthomosaic-based analysis. Overall, this study provides an effective and scalable framework for the detection of emerged and missing maize seedlings under complex field conditions. The proposed framework supports accurate reseeding decisions, and contributes to automated maize production.

Shifting cultivation remains a primary farming system in Myanmar’s mountainous regions. However, population growth and economic pressures have disrupted its traditional balance. This study aimed to clarify historical land-use patterns and evaluate vegetation recovery in Lailenpi by integrating field surveys with multitemporal Sentinel-2 imagery from 2019 to 2025. We identified cultivation plots using NDVI differences and quantified recovery trajectories with a Bayesian hierarchical nonlinear model. Results confirmed that a systematic eight-year rotational cycle was maintained. However, the total cultivated area expanded from 0.93% to 3.13%, shifting toward steeper terrain. Bayesian modeling showed that canopy greenness recovered within 24 to 36 months. Despite this resilience, the shift to rugged terrain suggested mounting land-use pressure and soil degradation risks. These findings highlight the importance of combining field surveys with high-resolution monitoring to ensure the long-term ecological sustainability of tropical mountain regions.