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

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.

A novel intelligent mobile application using human-centered AR: A case study in orange inspection

“…Human-Centered AI (HCAI) is defined by systems that are continually being optimized due to human action [Shneiderman 2020]. This method can be important in agriculture because they reach more precise levels, combining intelligent information for decision-making [Holzinger et al 2022]. Given this, we propose presenting two AR objects in the App interface so the user can decide which model best represents the inspected image.…”

Development of Microbiology Plantation-Based Multimodal Segmentation for Smart Garden Using Machine Learning

“…(1) Start (2) Initialize the requirements (3) Check the available requirements details (4) If (requirements� available) (5) en check if (garden parameter� available) (6) en send the details to garden manager (7) Wait for approval (8) Assign the requirements for utilization (9) Else (10) en send the details to the audit manager (11) Waiting for approval from the garden manager (12) Provide the license audit manager (13) End. ALGORITHM 1: Multimodal segmentation technique (MMST).…”

“…At the same time, we note, however, that mineral fertilizer is not a poison, but it is good in reasonable amounts. e plants should be fed but not overfed [7]. Combined planting of vegetables not only makes full use of the available land but also positively affects the growth and yield of both plants.…”

Improving Wheat Yield Prediction Accuracy Using LSTM-RF Framework Based on UAV Thermal Infrared and Multispectral Imagery

“…In addition, AI technologies have the potential to contribute more in the future. To achieve practical success in agriculture, Andreas et al [36] proposed three important AI frontier research areas: (1) intelligent sensor information fusion, (2) robotics and embodied intelligence, and (3) augmentation, explanation, and verification technologies for trusted decision support. Among the three areas, intelligent sensor information fusion is the most important.…”