Wheat is one of the major crops throughout the Balkan peninsula of Europe. Specific harvest and binning dates can vary depending on the specific geographic region. Grain aeration, wherein ambient air is used at low-volume airflow rates to cool a grain mass to levels that will suppress insect population development, is an under-utilized component of pest management plans for stored wheat. The successful use of aeration can potentially reduce fumigation of stored wheat, which will contribute to the amelioration of increasingly prevalent phosphine resistance. Historical weather data were used from 19 sites in the Balkan region to predict how quickly grains could be cooled through the use of aeration, using a web-based aeration model, and three different starting dates, including 1, 15, and 30 July. The model was used to predict population growth and development of Sitophilus oryzae, the rice weevil, with and without the use of aeration. Results show that, in the northern regions of the Balkans, aeration implemented at the start of binning reduced insect populations far below pest levels in unaerated wheat, and may potentially eliminate the need for fumigations. In more southerly regions, additional chemical inputs, such as fumigation or grain protectants, may be necessary in conjunction with aeration. Results provide guidelines for the increased potential of using aeration for the management of wheat produced and stored in the Balkan peninsula.

The sugarcane aphid, Melanaphis sacchari (Zehntner), has emerged as a serious pest of sorghum in the United States. Field trials were conducted in Louisiana and South Carolina in 2016–2018 to investigate its population characteristics and distribution patterns in relation to four sample unit sizes (three circular and one leaf based). Sugarcane aphid populations usually progressed through a phase of rapid rise followed by a phase of rapid decline within a span of 5–6 wk, with peak density determined by sorghum cultivars and climatic conditions. Peak population densities for susceptible cultivars were 1.9–14.9× that for resistant cultivars on a per plant basis. Melanaphis sacchari tended to concentrate on the lower green leaf nodes early in the infestation, with the distribution shifting toward higher green leaf nodes as the infestation progressed. Aphid densities per cm2 at the basal and middle sections were about twice as high as at the distal section of leaves. The proportions of infested sample units were fitted to the Wilson–Room binomial model that incorporates the effect of density on clumping pattern. For a specific sample unit size, clumping patterns were similar across cultivars, years, and leaf positions, but varied across infestation stages. For a fixed aphid density per sample unit, the proportion of infested sample units decreased with increasing sample unit size. For a fixed aphid density per cm2, proportion infested increased with increasing sample unit size, indicating less clumping with a larger sample unit size. Field sampling time and efficiency between samplers were quantified.

The quantity and quality of light captured by a plant’s canopy control many of its growth and development processes. However, light quality-related processes are not very well represented in most traditional and functional–structural crop models, which has been a major barrier to furthering crop model improvement and to better capturing the genetic control and environment modification of plant growth and development. A main challenge is the difficulty in obtaining dynamic data on plant canopy architectural characteristics. Current approaches on the measurement of 3D traits often relies on technologies that are either costly, excessively complicated, or impractical for field use. This study presents a methodology to estimate plant 3D traits using smart mobile app and data modeling. Leaf architecture data on 16 genotypes of rice were collected during two crop seasons using the smart-app PocketPlant3D. Quadratic Bézier curves were fitted to leaf lamina for estimation of insertion angle, elevation angle, and curve height. Leaf azimuth angle distribution, leaf phyllotaxis, canopy leaf angle distribution, and light extinction coefficients were also analyzed. The results could be used for breeding line selection or for parameterizing or evaluating rice 3D architectural models. The methodology opens new opportunities for strengthening the integration of plant 3D architectural traits in crop modeling, better capturing the genetic control and environment modification of plant growth and development, and for improving ideotype-based plant breeding.

Use of UAV images to assess narrow brown leaf spot severity in rice

Rice ( Oryza sativa L.) tiller angle is an important trait that influences plant architecture, canopy light interception, and yield potential. In this study, we proposed a deep learning‐based pipeline for automated measurement of rice tiller angle and plant base width using unmanned aerial vehicle (UAV) imagery. Our method leverages keypoint detection models to estimate tiller angles efficiently and accurately. We collected and annotated a dataset of UAV‐captured rice plant images for tiller angle estimation. We demonstrated that our approach provides scalable and precise measurement for keypoints under real‐world field conditions, achieving a mean average precision (mAP)@50 of 0.982 and a mAP@50:95 of 0.859 on the test set. Predicted tiller angle distribution aligns well with human annotations, with a mean absolute error of 5.3° across a range of 10.7°–27.8° and a Pearson's correlation coefficient of 0.64, offering acceptable accuracy for tiller angle measurement in real‐world agricultural settings. Additionally, the predicted plant base width ranges from 2.8 to 5.5 cm, with a mean absolute error of 1.02 cm compared to human annotations, highlighting the model's capability for precise spatial analysis. Significant differences in tiller angle and plant base width were detected among 27 rice genotypes. These results validate the proposed pipeline's potential for accurate and efficient differentiation of plant architecture traits. This research is the first to measure the rice tiller angle directly from UAV images. It lays a foundation for automated phenotyping of plant architecture traits and has the potential for integration into plant phenotyping frameworks to further promote artificial intelligence‐driven rice research and production.

Energycane and biomass sorghum are two of the most promising cellulosic energy crops in the southeastern US. This study aims to determine the biomass loss and composition change of energycane and biomass sorghum in storage under different environments. Aerobic and anaerobic storage trials were conducted in seven locations across the southeastern US for energycane and six locations for biomass sorghum for 3, 6 and 9 months. Results revealed that crop type accounted for less than 3% of the variability in moisture content and biomass loss, suggesting similar storage characteristics between energycane and biomass sorghum. Average moisture content decreased from 60.7 to 50.4% after 9 months in covered aerobic storage piles but increased from 62.9 to 67.2% for anaerobic storage in anaerobic silage bags. Dry matter loss averaged 49.9% after 9 months of aerobic storage and 40.3% for anaerobic storage. Dry matter loss was described as a non-linear function of storage duration and average storage moisture, with greater loss for longer duration and higher storage moisture. Average changes in biomass composition for cellulose, hemicellulose, lignin, and ash were all less than 3% during the 9-month storage regardless of storage types. Results from this study provide valuable insights on changes in biomass quantity and quality during storage and fill a critical knowledge gap in addressing the challenge of year-round biomass supply to biorefineries.