Background: The objective of this study is twofold. First, ascertain the important variables that predict tomato yields from plant height (PH) and vegetation index (VI) maps. The maps were derived from images taken by unmanned aerial vehicles (UAVs). Second, examine the accuracy of predictions of tomato fresh shoot masses (SM), fruit weights (FW), and the number of fruits (FN) from multiple machine learning algorithms using selected variable sets. To realize our objective, ultra-high-resolution RGB and multispectral images were collected by a UAV on ten days in 2020’s tomato growing season. From these images, 756 total variables, including first- (e.g., average, standard deviation, skewness, range, and maximum) and second-order (e.g., gray-level co-occurrence matrix features and growth rates of PH and VIs) statistics for each plant, were extracted. Several selection algorithms (i.e., Boruta, DALEX, genetic algorithm, least absolute shrinkage and selection operator, and recursive feature elimination) were used to select the variable sets useful for predicting SM, FW, and FN. Random forests, ridge regressions, and support vector machines were used to predict the yield using the top five selected variable sets.Results: First-order statistics of PH and VIs collected during the early to mid-fruit formation periods, about one month prior to harvest, were important variables for predicting SM. Similar to the case for SM, variables collected approximately one month prior to harvest were important for predicting FW and FN. Furthermore, variables related to PH were unimportant for prediction. Compared with predictions obtained using only first-order statistics, those obtained using the second-order statistics of VIs were more accurate for FW and FN. The prediction accuracy of SM, FW, and FN by models constructed from all variables (rRMSE = 8.8–28.1%) was better than that from first-order statistics (rRMSE = 10.0–50.1%).Conclusions: In addition to basic statistics (e.g., average and standard deviation), we derived second-order statistics of PH and VIs at the plant level using the ultra-high resolution UAV images. Our findings indicated that our variable selection method reduced the number variables needed for tomato yield prediction, improving the efficiency of phenotypic data collection and assisting with the selection of high-yield lines within breeding programs.

The monthly variability in the photosynthetic capacities, leaf mass per area (LMA) and leaf nitrogen (N) contents of rice (Oryza sativa L.) plants is important for determining the growth conditions, estimating the biomass and yield, and developing a rice growth model. Moreover, the correlations among LMA, leaf N content, and photosynthetic capacity parameters, such as the maximum rate of carboxylation (Vcmax) and the maximum rate of electron transport (Jmax), have not been fully investigated. The objectives of this study were (1) to analyze the monthly variability in Vcmax, Jmax, LMA, and leaf N content; and (2) to investigate the correlations among Vcmax, Jmax, LMA, leaf N content, and available N content in the soil. We measured the monthly changes in Vcmax and Jmax in 120 leaves of two rice cultivars, Koshihikari and Akitakomachi, subjected to standard-N and non-N treatments using gas-exchange measurements under open-air field conditions. Vcmax and Jmax values adjusted to a 25°C leaf temperature were significantly greater after the standard-N treatment compared with the non-N treatment in both cultivars, and the values of the photosynthetic capacities tended to decrease over time. In particular, these values significantly decreased at the ripening stage compared with the previous growth stages. The correlations among Vcmax and Jmax at a 25°C leaf temperature, leaf N content based on mass and area, and SPAD readings were significant for both cultivars but were not correlated with LMA. As indicators of photosynthetic performance, the monthly variability in Vcmax and Jmax for the fully expanded top or flag leaves were expressed easily as functions of the SPAD readings, rather than the leaf N contents based on mass and area, which are relatively expensive and labor intensive to measure. This research provides useful information for developing a universal rice growth model.

Agricultural land-use changes pose challenges for land managers in terms of ensuring the implementation of local land-use plans. This paper aims to build a land-use change model named MEKOLUC (Mekong Delta land-use change) for simulating land-use changes under the impacts of socioeconomic factors (profitability of land-use types, societal impacts on neighborhoods) and environmental factors (soil, salinity, persistence of salinity). The salinity diffusion map was generated using GAMA software and employed Markov cellular automata to depict the spread of salinity under the influence of dike and sluice gate system operations. The land-use decision-making process was based on multi-criteria selection of the main factors, which were land suitability, land convertibility, density of land use in the neighborhood and profitability of land-use patterns. The input data for the case study were historical land-use maps from 2005, 2010 and 2015 of Soc Trang, a coastal province in the Mekong Delta. The model was calibrated using a land-use map from 2010 (with kappa = 0.86) and was verified with land-use maps from 2015 and 2020 with deviations from 0 to 19%. The simulated results showed that shrimp–rice farming areas have been shrinking, even though these are recommended as sustainable farming systems. Inversely, intensive rice crops tended to change to rice–vegetable crops, vegetable crops or perennial fruit trees, which are projected to be well adapted to climate and salinity intrusion by 2030. This case study shows that the developed model is an essential tool for helping land managers and farmers build land-use plans.

The responsiveness of photosynthesis to continuously fluctuating light intensities can provide an estimate of the net assimilation rate (A) and photosynthetic light‐use efficiency (PLUE), which cannot be measured under steady‐state conditions. This study was conducted to investigate whether the leaves of rice (Oryza sativa L.) ‘Koshihikari’ and ‘Akitakomachi’ can efficiently utilize light under constantly fluctuating incident photosynthetic photon flux density (PPFD) and also to determine the effect of leaf N concentration on the acclimation of photosynthesis to changing PPFD. To this end, the instantaneous A and continually fluctuating incident PPFD were measured for 16 youngest fully expanded leaves of individual plants from dawn to dusk at the vegetative and reproductive phases under open‐air field conditions using two portable photosynthesis systems. The results revealed that (a) daily PLUE and calculated as the slope of the linear regression of A for values of PPFD between the light compensation point and 200 μmol m⁻² s⁻¹ can be improved with leaf N; (b) rice leaves do not utilize light efficiently under constantly fluctuating light conditions; and (c) the stomatal conductance per unit PPFD decreases under higher daily incident PPFD and diurnal fluctuation. Overall, our findings will help in refining crop photosynthesis models and would assist with the selection of high‐PLUE lines in breeding programs.