Artificial intelligence, blockchain, and extended reality: emerging digital technologies to turn the tide on illegal logging and illegal wood trade

Illegal logging which often results in forest degradation and sometimes in deforestation remains ubiquitous in many places around the globe. Managing illegal logging and illegal wood trade constitutes a global priority over the next few decades. Scientific, technological, and research communities are committed to respond rapidly, evaluating the opportunities to capitalize on emerging digital technologies for treating this formidable challenge. The innovative potentials of these emerging digital technologies at tackling illegal logging-related challenges are here investigated. We propose a novel system, WoodchAInX, combining explainable artificial intelligence (X-AI), next-generation blockchain, and extended reality (XR). Our findings on the most effective means of leveraging each technology’s potential and the convergence of the three technologies infer a vast promise for digital technology in this field. Yet, we argue that, overall, digital transformations will not deliver fundamental, responsible, and sustainable benefits without revolutionary realignment

UAV-based LiDAR for high-throughput determination of plant height and above‐ground biomass of the bioenergy grass arundo donax

Replacing fossil fuels with cellulosic biofuels is a valuable component of reducing the drivers of climate change. This leads to a requirement to develop more productive bioenergy crops, such as Arundo donax with the aim of increasing above-ground biomass (AGB). However, direct measurement of AGB is time consuming, destructive, and labor-intensive. Phenotyping of plant height and biomass production is a bottleneck in genomics- and phenomics-assisted breeding. Here, an unmanned aerial vehicle (UAV) for remote sensing equipped with light detection and ranging (LiDAR) was tested for remote plant height and biomass determination in A. donax. Experiments were conducted on three A. donax ecotypes grown in well-watered and moderate drought stress conditions. A novel UAV-LiDAR data collection and processing workflow produced a dense three-dimensional (3D) point cloud for crop height estimation through a normalized digital surface model (DSM) that acts as a crop height model (CHM). Manual measurements of crop height and biomass were taken in parallel and compared to LiDAR CHM estimates. Stepwise multiple regression was used to estimate biomass. Analysis of variance (ANOVA) tests and pairwise comparisons were used to determine differences between ecotypes and drought stress treatments. We found a significant relationship between the sensor readings and manually measured crop height and biomass, with determination coefficients of 0.73 and 0.71 for height and biomass, respectively. Differences in crop heights were detected more precisely from LiDAR estimates than from manual measurement. Crop biomass differences were also more evident in LiDAR estimates, suggesting differences in ecotypes’ productivity and tolerance to drought. Based on these results, application of the presented UAV-LiDAR workflow will provide new opportunities in assessing bioenergy crop morpho-physiological traits and in delivering improved genotypes for biorefining.</jats:p

A primer on artificial intelligence in plant digital phenomics: embarking on the data to insights journey

Artificial intelligence (AI) has emerged as a fundamental component of global agricultural research that is poised to impact on many aspects of plant science. In digital phenomics, AI is capable of learning intricate structure and patterns in large datasets. We provide a perspective and primer on AI applications to phenome research. We propose a novel human-centric explainable AI (X-AI) system architecture consisting of data architecture, technology infrastructure, and AI architecture design. We clarify the difference between post hoc models and 'interpretable by design' models. We include guidance for effectively using an interpretable by design model in phenomic analysis. We also provide directions to sources of tools and resources for making data analytics increasingly accessible. This primer is accompanied by an interactive online tutorial