Digital Transformation in Smart Farm and Forest Operations Needs Human-Centered AI: Challenges and Future Directions

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

Excellence in Excrements: Upcycling of Herbivore Manure into Nanocellulose and Biogas

Funding Information: The authors acknowledge Tiergarten Schönbrunn for providing the elephant manure, Johannes Theiner from the Microanalytical Laboratory (Faculty of Chemistry) at the University of Vienna for performing elemental analysis and IR-spectroscopy, and the following students for their help with various aspects of the work: Alexander Blocher, Lisa Panzenböck, Hanna Hirn, Nina Troppmaier, Manuel Holzman (all University of Vienna), and Elodie Schaffner (Institut National Polytechnique de Toulouse). The authors also thank Stephan Puchegger from the Faculty Center for Nano Structure Research for his help with the SEM and Antje Potthast (Institute of Chemistry of Renewable Resources, University of Natural Resources and Life Sciences, Vienna) for measuring the molecular weight of the cellulose. This work was supported by OeAD (WTZ ZA 03/2017) enabling the collaboration with CSIR, Port Elisabeth, South Africa. K.W. is grateful for the financial support provided by the Institute of Materials Chemistry of University of Vienna (371300). E.K. acknowledges the support by FinnCERES Materials Bioeconomy Ecosystem. Publisher Copyright: © 2021 The Authors. Published by American Chemical Society.The demand for animal products has significantly increased over the past decades as a result of the growing population and the heightened standards of living. Increased livestock farming does not only yield desired products but also significant quantities of wastes, particularly manure whose storage and application are being monitored with a tightening network of regulations. The problem is that manure is considered merely as a substrate for biogas production or as a fertilizer, whereas the substantial portion of fibers residing in herbivore manure has remained underutilized. Here, we propose a manure management system, in which not only biogas and fertilizer precursors but also high-value materials in the form of (nano)cellulose are produced. We show that high biogas yields can be achieved for elephant manure and the remaining substrate enables effortless isolation of cellulose nanofibers, leading to a significant reduction of the environmental impact compared with traditional systems based on wood.Peer reviewe

Environmental Life Cycle Assessment in Organic and Conventional Rice Farming Systems: Using a Cradle to Farm Gate Approach

The rising demand for agricultural products and expanding public awareness of environmental friendliness have led to the adoption of the organic farming system rather than the conventional one. The life cycle assessment (LCA) concept is a frequently used method to examine the environmental impacts of any activity across its entire life cycle. This research is the first use of LCA for the impacts of vermicompost and cattle manure as organic fertilizers in rice farming. The main goal of this study was to compare the environmental impacts of conventional and organic rice farming. This paper uses midpoint attributional LCA to analyze environmental damages during rice production. The four primary harm categories used in this strategy to categorize the environmental effects were: (1) climate change, (2) human health, (3) ecosystem quality, and (4) resources. The inventory data for the agricultural stage were obtained through farmer interviews. The system boundaries were set to cradle to farm gate, and 1 ton of final product (dry matter) was used as the functional unit. The results show that in all main damage categories, except for particulate matter formation, stratospheric ozone depletion, mineral resource scarcity, and freshwater eutrophication, conventional rice production has higher environmental impacts than organic rice production. Overall, organic rice production is more effective in diminishing the negative environmental effects of farming compared to conventional rice production

Environmental Life Cycle Assessment in Organic and Conventional Rice Farming Systems: Using a Cradle to Farm Gate Approach

The rising demand for agricultural products and expanding public awareness of environmental friendliness have led to the adoption of the organic farming system rather than the conventional one. The life cycle assessment (LCA) concept is a frequently used method to examine the environmental impacts of any activity across its entire life cycle. This research is the first use of LCA for the impacts of vermicompost and cattle manure as organic fertilizers in rice farming. The main goal of this study was to compare the environmental impacts of conventional and organic rice farming. This paper uses midpoint attributional LCA to analyze environmental damages during rice production. The four primary harm categories used in this strategy to categorize the environmental effects were: (1) climate change, (2) human health, (3) ecosystem quality, and (4) resources. The inventory data for the agricultural stage were obtained through farmer interviews. The system boundaries were set to cradle to farm gate, and 1 ton of final product (dry matter) was used as the functional unit. The results show that in all main damage categories, except for particulate matter formation, stratospheric ozone depletion, mineral resource scarcity, and freshwater eutrophication, conventional rice production has higher environmental impacts than organic rice production. Overall, organic rice production is more effective in diminishing the negative environmental effects of farming compared to conventional rice production

Digital Transformation in Smart Farm and Forest Operations Needs Human-Centered AI: Challenges and Future Directions

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