TY - GEN A1 - Mengsuwan, Konlavach A1 - Rivera-Palacio, Juan C. A1 - Ryo, Masahiro T1 - ChatGPT and general-purpose AI count fruits in pictures surprisingly well without programming or training T2 - Smart Agricultural Technology N2 - General-purpose artificial intelligence (AI) can facilitate agricultural digitalization as many tools do not require coding. Yet, it remains unclear how well the emerging general-purpose AI technologies can perform object counting, which is a fundamental task in agricultural digitalization, in comparison to the current standard practice. We show that ChatGPT (GPT4 V) demonstrated moderate performance in counting coffee cherries from images, while the T-Rex, foundation model for object counting, performed with high accuracy. Testing with a hundred images, we examined that ChatGPT can count cherries, and the performance improves with human feedback (R2 = 0.36 and 0.46, respectively). The T-Rex foundation model required only a few samples for training but outperformed YOLOv8, the conventional best practice model (R2 = 0.92 and 0.90, respectively). Obtaining the results with these models was 100x shorter than the conventional best practice. These results bring two surprises for deep learning users in applied domains: a foundation model can drastically save effort and achieve higher accuracy than a conventional approach, and ChatGPT can reveal a relatively good performance especially with guidance by providing some examples and feedback. No requirement for coding skills can impact education, outreach, and real-world implementation of generative AI for supporting farmers. KW - Foundation model KW - General purpose ai KW - Chatgpt KW - Large language model KW - Large vision language model KW - Agriculture Y1 - 2024 U6 - https://doi.org/10.1016/j.atech.2024.100688 SN - 2772-3755 VL - 9 PB - Elsevier BV ER - TY - GEN A1 - Ryo, Masahiro A1 - Schiller, Josepha A1 - Stiller, Stefan A1 - Palacio, Juan Camilo Rivera A1 - Mengsuwan, Konlavach A1 - Safonova, Anastasiia A1 - Wei, Yuqi T1 - Deep learning for sustainable agriculture needs ecology and human involvement T2 - Journal of Sustainable Agriculture and Environment N2 - Deep learning is an emerging data analytic tool that can improve predictability, efficiency and sustainability in agriculture. With a bibliometric analysis of 156 articles, we show how deep learning methods have been applied in the context of sustainable agriculture. As a general publication trend, China and India are leading countries for publication, international collaboration is still minor. Deep learning has been popularly applied in the context of smart agriculture across scales for individual plant monitoring, field monitoring, field operation and robotics, predicting soil, water and climate conditions and landscape‐level monitoring of land use and crop types. We identified that the potential of deep learning had been investigated mainly for predicting soil (abiotic), water, climate and vegetation dynamics, but ecological characteristics are critically understudied. We also highlight key themes that can be better addressed with deep learning for fostering sustainable agriculture: (i) including above‐ and belowground ecological dynamics such as ecosystem functioning and ecotone, (ii) evaluating agricultural impacts on other ecosystems and (iii) incorporating the knowledge and opinions of domain experts and stakeholders into artificial intelligence. We propose that deep learning needs to go beyond automatic data analysis by integrating ecological and human knowledge to foster sustainable agriculture. KW - biodiversity KW - deep learning KW - ecology KW - smart agriculture KW - sustainable agriculture Y1 - 2022 U6 - https://doi.org/10.1002/sae2.12036 SN - 2767-035X VL - 2 IS - 1 SP - 40 EP - 44 PB - Wiley ER - TY - GEN A1 - Synodinos, Alexis D. A1 - Karnatak, Rajat A1 - Aguilar‐Trigueros, Carlos A. A1 - Gras, Pierre A1 - Heger, Tina A1 - Ionescu, Danny A1 - Maaß, Stefanie A1 - Musseau, Camille L. A1 - Onandia, Gabriela A1 - Planillo, Aimara A1 - Weiss, Lina A1 - Wollrab, Sabine A1 - Ryo, Masahiro T1 - The rate of environmental change as an important driver across scales in ecology T2 - Oikos N2 - Global change has been predominantly studied from the prism of ‘how much' rather than ‘how fast' change occurs. Associated to this, there has been a focus on environmental drivers crossing a critical value and causing so‐called regime shifts. This presupposes that the rate at which environmental conditions change is slow enough to allow the ecological entity to remain close to a stable attractor (e.g. an equilibrium). However, environmental change is occurring at unprecedented rates. Equivalently to the classical regime shifts, theory shows that a critical threshold in rates of change can exist, which can cause rate‐induced tipping (R‐tipping). However, the potential implications of R‐tipping in ecology remain understudied. We aim to facilitate the application of R‐tipping theory in ecology with the objective of identifying which properties (e.g. level of organisation) increase susceptibility to rates of change. First, we clarify the fundamental difference between tipping caused by the magnitude as opposed to the rate of change crossing a threshold. Then we present examples of R‐tipping from the ecological literature and seek the ecological properties related to higher sensitivity to rates of change. Specifically, we consider the role of the level of ecological organisation, spatial processes, eco‐evolutionary dynamics and pair–wise interactions in mediating or buffering rate‐induced transitions. Finally, we discuss how targeted experiments can investigate the mechanisms associated to increasing rates of change. Ultimately, we seek to highlight the need to better understand how rates of environmental change may induce ecological responses and to facilitate the systematic study of rates of environmental change in the context of current global change. KW - climate change KW - ecological communities KW - eco-evo feedbacks KW - transitions KW - global change KW - R-tipping KW - temporal ecology Y1 - 2023 U6 - https://doi.org/10.1111/oik.09616 SN - 0030-1299 VL - 2023 IS - 4 PB - Wiley ER - TY - GEN A1 - Edlinger, Anna A1 - Garland, Gina A1 - Banerjee, Samiran A1 - Degrune, Florine A1 - García‐Palacios, Pablo A1 - Herzog, Chantal A1 - Pescador, David Sánchez A1 - Romdhane, Sana A1 - Ryo, Masahiro A1 - Saghaï, Aurélien A1 - Hallin, Sara A1 - Maestre, Fernando T. A1 - Philippot, Laurent A1 - Rillig, Matthias C. A1 - Heijden, Marcel G. A. van der T1 - The impact of agricultural management on soil aggregation and carbon storage is regulated by climatic thresholds across a 3000 km European gradient T2 - Global Change Biology N2 - Organic carbon and aggregate stability are key features of soil quality and are important to consider when evaluating the potential of agricultural soils as carbon sinks. However, we lack a comprehensive understanding of how soil organic carbon (SOC) and aggregate stability respond to agricultural management across wide environmental gradients. Here, we assessed the impact of climatic factors, soil properties and agricultural management (including land use, crop cover, crop diversity, organic fertilization, and management intensity) on SOC and the mean weight diameter of soil aggregates, commonly used as an indicator for soil aggregate stability, across a 3000 km European gradient. Soil aggregate stability (−56%) and SOC stocks (−35%) in the topsoil (20 cm) were lower in croplands compared with neighboring grassland sites (uncropped sites with perennial vegetation and little or no external inputs). Land use and aridity were strong drivers of soil aggregation explaining 33% and 20% of the variation, respectively. SOC stocks were best explained by calcium content (20% of explained variation) followed by aridity (15%) and mean annual temperature (10%). We also found a threshold‐like pattern for SOC stocks and aggregate stability in response to aridity, with lower values at sites with higher aridity. The impact of crop management on aggregate stability and SOC stocks appeared to be regulated by these thresholds, with more pronounced positive effects of crop diversity and more severe negative effects of crop management intensity in nondryland compared with dryland regions. We link the higher sensitivity of SOC stocks and aggregate stability in nondryland regions to a higher climatic potential for aggregate‐mediated SOC stabilization. The presented findings are relevant for improving predictions of management effects on soil structure and C storage and highlight the need for site‐specific agri‐environmental policies to improve soil quality and C sequestration. KW - aggregate stability KW - agro-ecosystems KW - aridity KW - climatic threshold KW - environmental gradient KW - intensive agriculture KW - soil organic carbon Y1 - 2023 U6 - https://doi.org/10.1111/gcb.16677 SN - 1354-1013 VL - 29 IS - 11 SP - 3177 EP - 3192 PB - Wiley ER - TY - GEN A1 - Riva, Federico A1 - Graco-Roza, Caio A1 - Daskalova, Gergana N. A1 - Hudgins, Emma J. A1 - Lewthwaite, Jayme M. M. A1 - Newman, Erica A. A1 - Ryo, Masahiro A1 - Mammola, Stefano T1 - Toward a cohesive understanding of ecological complexity T2 - Science Advances N2 - Ecological systems are quintessentially complex systems. Understanding and being able to predict phenomena typical of complex systems is, therefore, critical to progress in ecology and conservation amidst escalating global environmental change. However, myriad definitions of complexity and excessive reliance on conventional scientific approaches hamper conceptual advances and synthesis. Ecological complexity may be better understood by following the solid theoretical basis of complex system science (CSS). We review features of ecological systems described within CSS and conduct bibliometric and text mining analyses to characterize articles that refer to ecological complexity. Our analyses demonstrate that the study of complexity in ecology is a highly heterogeneous, global endeavor that is only weakly related to CSS. Current research trends are typically organized around basic theory, scaling, and macroecology. We leverage our review and the generalities identified in our analyses to suggest a more coherent and cohesive way forward in the study of complexity in ecology. Y1 - 2023 U6 - https://doi.org/10.1126/sciadv.abq4207 SN - 2375-2548 VL - 9 IS - 25 PB - American Association for the Advancement of Science (AAAS) ER - TY - GEN A1 - Wei, Yuqi A1 - Wei, Bin A1 - Ryo, Masahiro A1 - Bi, Yixian A1 - Sun, Xiangyun A1 - Zhang, Yingjun A1 - Liu, Nan T1 - Grazing facilitates litter-derived soil organic carbon formation in grasslands by fostering microbial involvement through microenvironment modification T2 - CATENA N2 - Grasslands store 10–30 % of the global soil organic carbon (SOC) and have the potential to mitigate the increase in atmospheric CO2 concentrations. Grazing plays a crucial role in regulating SOC storage in grassland ecosystems. However, the mechanistic understanding of how grazing influences the SOC dynamic still needs to be improved. We investigated how grazing-induced microenvironment changes influence the microbial assimilation of plant litter C and SOC formation from decomposed litter C in a multi-year field experiment, where grazing was simulated with mowing, dung and urine return, and trampling. We incubated 13C labeled litter in PVC collars to trace the microbial assimilation of litter C and the fate of litter C in the SOC after decomposition. While the grazing treatments changed soil properties marginally, mowing decreased above-ground plant biomass, litter mass, plant height, and plant cover (−12 % to −79 %). Accordingly, mowing treatment increased the exposure of litter to UV radiation (+38 %) and therefore facilitated the microbial assimilation of litter C (+20 %) and the SOC formation (+15 %). Trampling treatment promoted the transformation of litter C to SOC pools by mixing litter and soil (+34 %). Dung and urea return treatment did not affect SOC formation due to a marginal change in available nitrogen. Collectively, our results suggest that grazing facilitates litter-derived SOC formation by regulating microbial involvement through changes in the microenvironment. Our study indicates that grazing promotes SOC formation from plant litter, which maintains SOC storage in grasslands. Accurate quantification of the contribution of plant C input to SOC pools in different grasslands under various utilization is the next step to better predict SOC dynamics. KW - 13C-stable isotope KW - Grazing KW - Microenvironment KW - PLFA-SIP KW - Soil microbial assimilation KW - Soil organic carbon (SOC) formation Y1 - 2023 U6 - https://doi.org/10.1016/j.catena.2023.107389 SN - 0341-8162 VL - 232 PB - Elsevier BV ER - TY - GEN A1 - Safonova, Anastasiia A1 - Ghazaryan, Gohar A1 - Stiller, Stefan A1 - Main-Knorn, Magdalena A1 - Nendel, Claas A1 - Ryo, Masahiro T1 - Ten deep learning techniques to address small data problems with remote sensing T2 - International Journal of Applied Earth Observation and Geoinformation N2 - Researchers and engineers have increasingly used Deep Learning (DL) for a variety of Remote Sensing (RS) tasks. However, data from local observations or via ground truth is often quite limited for training DL models, especially when these models represent key socio-environmental problems, such as the monitoring of extreme, destructive climate events, biodiversity, and sudden changes in ecosystem states. Such cases, also known as small data problems, pose significant methodological challenges. This review summarises these challenges in the RS domain and the possibility of using emerging DL techniques to overcome them. We show that the small data problem is a common challenge across disciplines and scales that results in poor model generalisability and transferability. We then introduce an overview of ten promising DL techniques: transfer learning, self-supervised learning, semi-supervised learning, few-shot learning, zero-shot learning, active learning, weakly supervised learning, multitask learning, process-aware learning, and ensemble learning; we also include a validation technique known as spatial k-fold cross validation. Our particular contribution was to develop a flowchart that helps DL users select which technique to use given by answering a few questions. We hope that our review article facilitate DL applications to tackle societally important environmental problems with limited reference data. KW - Small data problems KW - Remote sensing KW - Deep learning KW - Transfer learning KW - Few-shot learning KW - Zero-shot learning KW - Self-supervised learning Y1 - 2023 U6 - https://doi.org/10.1016/j.jag.2023.103569 SN - 1569-8432 VL - 125 PB - Elsevier BV ER - TY - GEN A1 - Stiller, Stefan A1 - Dueñas, Juan F A1 - Hempel, Stefan A1 - Rillig, Matthias C. A1 - Ryo, Masahiro T1 - Deep learning image analysis for filamentous fungi taxonomic classification: dealing with small datasets with class imbalance and hierarchical grouping T2 - Biology Methods and Protocols N2 - Deep learning applications in taxonomic classification for animals and plants from images have become popular, while those for microorganisms are still lagging behind. Our study investigated the potential of deep learning for the taxonomic classification of hundreds of filamentous fungi from colony images, which is typically a task that requires specialized knowledge. We isolated soil fungi, annotated their taxonomy using standard molecular barcode techniques, and took images of the fungal colonies grown in petri dishes (n = 606). We applied a convolutional neural network with multiple training approaches and model architectures to deal with some common issues in ecological datasets: small amounts of data, class imbalance, and hierarchically structured grouping. Model performance was overall low, mainly due to the relatively small dataset, class imbalance, and the high morphological plasticity exhibited by fungal colonies. However, our approach indicates that morphological features like color, patchiness, and colony extension rate could be used for the recognition of fungal colonies at higher taxonomic ranks (i.e. phylum, class, and order). Model explanation implies that image recognition characters appear at different positions within the colony (e.g. outer or inner hyphae) depending on the taxonomic resolution. Our study suggests the potential of deep learning applications for a better understanding of the taxonomy and ecology of filamentous fungi amenable to axenic culturing. Meanwhile, our study also highlights some technical challenges in deep learning image analysis in ecology, highlighting that the domain of applicability of these methods needs to be carefully considered. KW - Metagenomics and Environmental Biology KW - Computational Methods KW - Imaging technologies Y1 - 2024 U6 - https://doi.org/10.1093/biomethods/bpae063 SN - 2396-8923 VL - 9 IS - 1 PB - Oxford University Press (OUP) ER - TY - GEN A1 - Ryo, Masahiro T1 - Ecology with artificial intelligence and machine learning in Asia: a historical perspective and emerging trends T2 - Ecological Research N2 - The use of artificial intelligence (AI) and machine learning (ML) has significantly enhanced ecological research in Asia by improving data processing, analysis, and pattern extraction. Analyzing 1550 articles, I show an overview of the use of AI and ML for Asian ecological research. Following the last 20 year trend, I found that the topics in Asian ecological research have transitioned from technical perspectives to more applied issues, focusing on biodiversity conservation, climate change, land use change, and societal impacts. Non‐Asian countries, on the other hand, have focused more on theoretical understanding and ecological processes. The difference between Asian and non‐Asian regions may have emerged due to the ecological challenges faced by Asian countries, such as rapid economic growth, land development, and climate change impacts. In both regions, deep learning related technology has been emerging (e.g., big data collection including image and movement). Within Asia, China has been the Asia‐leading country for AI/ML applications followed by Korea, Japan, India, and Iran. The number of computer science education programs in China has been increasing 3.5× times faster than that in the United States, indicating that a nationwide strategy for computer science development is key for ecological science with AI. Overall, the adoption of AI and ML technologies in ecological studies in Asia has propelled the field forward and opened new avenues for innovative research and conservation practices. KW - artificial intelligence (AI) KW - biodiversity conservation KW - China KW - deep learning KW - machine learning (ML) Y1 - 2023 U6 - https://doi.org/10.1111/1440-1703.12425 SN - 0912-3814 VL - 39 IS - 1 SP - 5 EP - 14 PB - Wiley ER - TY - GEN A1 - Stiller, Stefan A1 - Grahmann, Kathrin A1 - Ghazaryan, Gohar A1 - Ryo, Masahiro T1 - Improving spatial transferability of deep learning models for small-field crop yield prediction T2 - ISPRS Open Journal of Photogrammetry and Remote Sensing N2 - Predicting crop yield using deep learning (DL) and remote sensing is a promising technique in agriculture. In smallholder agriculture (<2 ha), where 84% of the farms operate globally, it is crucial to build a model that can be useful across several fields (high spatial transferability). However, enhancing spatial model transferability in a small-scale setting faces significant challenges, including spatial autocorrelation, heterogeneity and scale dependence of spatial dynamics, as well as the need to address limited data points. This study aimed to test the hypothesis that spatial cross validation (SCV) is a more suitable model validation practice than random cross validation (RCV) to enhance model transferability for spatial prediction in a small-scale farming setting. We compared the performances of DL models that predict crop yield for several settings including three crop types and two DL architectures based on RCV with and without overlapping samples and SCV. Notably, we conducted model performance tests on external, equally sized fields instead of the field used for training. We used high resolution RGB imagery taken with a drone as input. Our results show that the models using SCV outperformed those using RCV when the models were tested on external fields (on average r = 0.37 for SCV, r = 0.18 for RCV with overlap and r = 0.07 without), even though the models using SCV showed a substantially lower performance for cross validation (CV) than those using RCV (r with SCV and RCV w/o overlap = 0.73 and 0.98/0.73, respectively). The results suggest that RCV leads to over-optimism by overfitting the spatial structure and remembering image-specific information (so called memorization). Our study offers the first empirical evidence in agriculture that SCV is preferable to RCV in small field settings for making DL models more transferable. KW - Smallholder farming KW - UAV KW - Crop yield KW - Convolutional neural network KW - Spatial cross validation KW - External validation Y1 - 2024 U6 - https://doi.org/10.1016/j.ophoto.2024.100064 SN - 2667-3932 VL - 12 PB - Elsevier BV ER - TY - GEN A1 - Palacio, Juan Camilo Rivera A1 - Bunn, Christian A1 - Rahn, Eric A1 - Little-Savage, Daisy A1 - Schmidt, Paul Günter A1 - Ryo, Masahiro T1 - Geographic-scale coffee cherry counting with smartphones and deep learning T2 - Plant Phenomics N2 - Deep learning and computer vision, using remote sensing and drones, are 2 promising nondestructive methods for plant monitoring and phenotyping. However, their applications are infeasible for many crop systems under tree canopies, such as coffee crops, making it challenging to perform plant monitoring and phenotyping at a large spatial scale at a low cost. This study aims to develop a geographic-scale monitoring method for coffee cherry counting, supported by an artificial intelligence (AI)-powered citizen science approach. The approach uses basic smartphones to take a few pictures of coffee trees; 2,968 trees were investigated with 8,904 pictures in Junín and Piura (Peru), Cauca, and Quindío (Colombia) in 2022, with the help of nearly 1,000 smallholder coffee farmers. Then, we trained and validated YOLO (You Only Look Once) v8 for detecting cherries in the dataset in Peru. An average number of cherries per picture was multiplied by the number of branches to estimate the total number of cherries per tree. The model's performance in Peru showed an R2 of 0.59. When the model was tested in Colombia, where different varieties are grown in different biogeoclimatic conditions, the model showed an R2 of 0.71. The overall performance in both countries reached an R2 of 0.72. The results suggest that the method can be applied to much broader scales and is transferable to other varieties, countries, and regions. To our knowledge, this is the first AI-powered method for counting coffee cherries and has the potential for a geographic-scale, multiyear, photo-based phenotypic monitoring for coffee crops in low-income countries worldwide. Y1 - 2024 U6 - https://doi.org/10.34133/plantphenomics.0165 SN - 2643-6515 VL - 6 PB - American Association for the Advancement of Science (AAAS) ER -