Digital Transformation in Smart Farm and Forest Operations Needs Human-Centered AI: Challenges and Future Directions
| dc.contributor.author | Holzinger, Andreas | |
| dc.contributor.author | Saranti, Anna | |
| dc.contributor.author | Angerschmid, Alessa | |
| dc.contributor.author | Retzlaff, Carl Orge | |
| dc.contributor.author | Gronauer, Andreas | |
| dc.contributor.author | Pejakovic, Vladimir | |
| dc.contributor.author | Medel-Jimenez, Francisco | |
| dc.contributor.author | Krexner, Theresa | |
| dc.contributor.author | Gollob, Christoph | |
| dc.contributor.author | Stampfer, Karl | |
| dc.date.accessioned | 2022-05-10T13:45:38Z | |
| dc.date.available | 2022-05-10T13:45:38Z | |
| dc.date.issued | 2022-04-15 | |
| dc.date.updated | 2022-05-05T14:06:54Z | |
| dc.description.abstract | The main impetus for the global efforts toward the current digital transformation in almost all areas of our daily lives is due to the great successes of artificial intelligence (AI), and in particular, the workhorse of AI, statistical machine learning (ML). The intelligent analysis, modeling, and management of agricultural and forest ecosystems, and of the use and protection of soils, already play important roles in securing our planet for future generations and will become irreplaceable in the future. Technical solutions must encompass the entire agricultural and forestry value chain. The process of digital transformation is supported by cyber-physical systems enabled by advances in ML, the availability of big data and increasing computing power. For certain tasks, algorithms today achieve performances that exceed human levels. The challenge is to use multimodal information fusion, i.e., to integrate data from different sources (sensor data, images, *omics), and explain to an expert why a certain result was achieved. However, ML models often react to even small changes, and disturbances can have dramatic effects on their results. Therefore, the use of AI in areas that matter to human life (agriculture, forestry, climate, health, etc.) has led to an increased need for trustworthy AI with two main components: explainability and robustness. One step toward making AI more robust is to leverage expert knowledge. For example, a farmer/forester in the loop can often bring in experience and conceptual understanding to the AI pipeline—no AI can do this. Consequently, human-centered AI (HCAI) is a combination of “artificial intelligence” and “natural intelligence” to empower, amplify, and augment human performance, rather than replace people. To achieve practical success of HCAI in agriculture and forestry, this article identifies three important frontier research areas: (1) intelligent information fusion; (2) robotics and embodied intelligence; and (3) augmentation, explanation, and verification for trusted decision support. This goal will also require an agile, human-centered design approach for three generations (G). G1: Enabling easily realizable applications through immediate deployment of existing technology. G2: Medium-term modification of existing technology. G3: Advanced adaptation and evolution beyond state-of-the-art. | en |
| dc.identifier.eissn | 1424-8220 | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/16863 | |
| dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-15641 | |
| dc.language.iso | en | en |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject.ddc | 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten | de |
| dc.subject.other | sensors | en |
| dc.subject.other | cyber-physical systems | en |
| dc.subject.other | machine learning | en |
| dc.subject.other | artificial intelligence | en |
| dc.subject.other | human-centered AI | en |
| dc.subject.other | smart farming | en |
| dc.subject.other | smart forestry | en |
| dc.subject.other | precision farming | en |
| dc.subject.other | precision forestry | en |
| dc.subject.other | AI for good | en |
| dc.title | Digital Transformation in Smart Farm and Forest Operations Needs Human-Centered AI: Challenges and Future Directions | en |
| dc.type | Article | en |
| dc.type.version | publishedVersion | en |
| dcterms.bibliographicCitation.articlenumber | 3043 | en |
| dcterms.bibliographicCitation.doi | 10.3390/s22083043 | en |
| dcterms.bibliographicCitation.issue | 8 | en |
| dcterms.bibliographicCitation.journaltitle | Sensors | en |
| dcterms.bibliographicCitation.originalpublishername | MDPI | en |
| dcterms.bibliographicCitation.originalpublisherplace | Basel | en |
| dcterms.bibliographicCitation.volume | 22 | en |
| tub.accessrights.dnb | free | en |
| tub.affiliation | Fak. 1 Geistes- und Bildungswissenschaften::Inst. Sprache und Kommunikation::FG Audiokommunikation | de |
| tub.affiliation.faculty | Fak. 1 Geistes- und Bildungswissenschaften | de |
| tub.affiliation.group | FG Audiokommunikation | de |
| tub.affiliation.institute | Inst. Sprache und Kommunikation | de |
| tub.publisher.universityorinstitution | Technische Universität Berlin | en |