Upgrading Solid Digestate from Anaerobic Digestion of Agricultural Waste as Performance Enhancer for Starch-Based Mulching Biofilm.

Developing a green and sustainable method to upgrade biogas wastes into high value-added products is attracting more and more public attention. The application of solid residues as a performance enhancer in the manufacture of biofilms is a prospective way to replace conventional plastic based on fossil fuel. In this work, solid digestates from the anaerobic digestion of agricultural wastes, such as straw, cattle and chicken manures, were pretreated by an ultrasonic thermo-alkaline treatment to remove the nonfunctional compositions and then incorporated in plasticized starch paste to prepare mulching biofilms by the solution casting method. The results indicated that solid digestate particles dispersed homogenously in the starch matrix and gradually aggregated under the action of a hydrogen bond, leading to a transformation of the composites to a high crystalline structure. Consequently, the composite biofilm showed a higher tensile strength, elastic modulus, glass transition temperature and degradation temperature compared to the pure starch-based film. The light, water and GHG (greenhouse gas) barrier properties of the biofilm were also reinforced by the addition of solid digestates, performing well in sustaining the soil quality and minimizing N2O or CH4 emissions. As such, recycling solid digestates into a biodegradable plastic substitute not only creates a new business opportunity by producing high-performance biofilms but also reduces the environmental risk caused by biogas waste and plastics pollution.

Qualification of Soybean Responses to Flooding Stress Using UAV-Based Imagery and Deep Learning.

Soybean is sensitive to flooding stress that may result in poor seed quality and significant yield reduction. Soybean production under flooding could be sustained by developing flood-tolerant cultivars through breeding programs. Conventionally, soybean tolerance to flooding in field conditions is evaluated by visually rating the shoot injury/damage due to flooding stress, which is labor-intensive and subjective to human error. Recent developments of field high-throughput phenotyping technology have shown great potential in measuring crop traits and detecting crop responses to abiotic and biotic stresses. The goal of this study was to investigate the potential in estimating flood-induced soybean injuries using UAV-based image features collected at different flight heights. The flooding injury score (FIS) of 724 soybean breeding plots was taken visually by breeders when soybean showed obvious injury symptoms. Aerial images were taken on the same day using a five-band multispectral and an infrared (IR) thermal camera at 20, 50, and 80 m above ground. Five image features, i.e., canopy temperature, normalized difference vegetation index, canopy area, width, and length, were extracted from the images at three flight heights. A deep learning model was used to classify the soybean breeding plots to five FIS ratings based on the extracted image features. Results show that the image features were significantly different at three flight heights. The best classification performance was obtained by the model developed using image features at 20 m with 0.9 for the five-level FIS. The results indicate that the proposed method is very promising in estimating FIS for soybean breeding.