AgriBench: A Hierarchical Agriculture Benchmark for Multimodal Large Language Models

Maes, J., Teller, A., Erhard, M., Liquete, C., Braat, L., Berry, P., Egoh, B., Puydarrieux, P., Fiorina, C., Santos, F., et al.: Mapping and assessment of ecosystems and their services. An analytical framework for ecosystem assessments under action 5

2013

Earlier work this paper cites.

Panagos, P., Meusburger, K., Ballabio, C., Borrelli, P., Alewell, C.: Soil erodibility in europe: A high-resolution dataset based on lucas. Science of the total environment 479

2014

Earlier work this paper cites.

Lucas 2018 (land use / cover area frame survey) technical reference document c3 classification (land cover & land use). Tech. rep., European Commission (2018), https://ec.europa.eu/eurostat/documents/205002/8072634\/LUCAS2018-C3-Classification.pdf

2018

Earlier work this paper cites.

d’Andrimont, R., Yordanov, M., Martinez-Sanchez, L., Eiselt, B., Palmieri, A., Dominici, P., Gallego, J., Reuter, H.I., Joebges, C., Lemoine, G., et al.: Harmonised lucas in-situ land cover and use database for field surveys from 2006 to 2018 in the european union. Scientific data 7

2020

Earlier work this paper cites.

Herrmann, I., Bdolach, E., Montekyo, Y., Rachmilevitch, S., Townsend, P.A., Karnieli, A.: Assessment of maize yield and phenology by drone-mounted superspectral camera. Precision Agriculture 21

2020

Earlier work this paper cites.

Laso Bayas, J.C., See, L., Bartl, H., Sturn, T., Karner, M., Fraisl, D., Moorthy, I., Busch, M., Van Der Velde, M., Fritz, S.: Crowdsourcing lucas: Citizens generating reference land cover and land use data with a mobile app. Land 9

2020

Earlier work this paper cites.

Li, Z., Guo, R., Li, M., Chen, Y., Li, G.: A review of computer vision technologies for plant phenotyping. Computers and Electronics in Agriculture 176

2020

Earlier work this paper cites.

Tang, Y., Chen, M., Wang, C., Luo, L., Li, J., Lian, G., Zou, X.: Recognition and localization methods for vision-based fruit picking robots: A review. Frontiers in Plant Science 11

2020

Earlier work this paper cites.

Han, J., Shi, L., Yang, Q., Huang, K., Zha, Y., Yu, J.: Real-time detection of rice phenology through convolutional neural network using handheld camera images. Precision Agriculture 22

2021

Earlier work this paper cites.

Kong, J., Wang, H., Wang, X., Jin, X., Fang, X., Lin, S.: Multi-stream hybrid architecture based on cross-level fusion strategy for fine-grained crop species recognition in precision agriculture. Computers and Electronics in Agriculture 185

2021

Earlier work this paper cites.

Tziolas, N., Tsakiridis, N., Chabrillat, S., Demattê, J.A., Ben-Dor, E., Gholizadeh, A., Zalidis, G., Van Wesemael, B.: Earth observation data-driven cropland soil monitoring: A review. Remote Sensing 13

2021

Earlier work this paper cites.

Alayrac, J.B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al.: Flamingo: a visual language model for few-shot learning. Advances in neural information processing systems 35

2022

Earlier work this paper cites.

Seol, J., Kim, J., Son, H.I.: Field evaluations of a deep learning-based intelligent spraying robot with flow control for pear orchards. Precision Agriculture 23

2022

Earlier work this paper cites.

Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al.: Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)

Original

2023

Earlier work this paper cites.

Anil, R., Dai, A.M., Firat, O., Johnson, M., Lepikhin, D., Passos, A., Shakeri, S., Taropa, E., Bailey, P., Chen, Z., et al.: Palm 2 technical report. arXiv preprint arXiv:2305.10403 (2023)

Original

2023

Earlier work this paper cites.

Bai, J., Bai, S., Chu, Y., Cui, Z., Dang, K., Deng, X., Fan, Y., Ge, W., Han, Y., Huang, F., et al.: Qwen technical report. arXiv preprint arXiv:2309.16609 (2023)

Original

2023

Earlier work this paper cites.

Bai, J., Bai, S., Yang, S., Wang, S., Tan, S., Wang, P., Lin, J., Zhou, C., Zhou, J.: Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond. arXiv preprint arXiv:2308.12966 (2023)

Original

2023

Earlier work this paper cites.

Chen, Z., Wu, J., Wang, W., Su, W., Chen, G., Xing, S., Zhong, M., Zhang, Q., Zhu, X., Lu, L., Li, B., Luo, P., Lu, T., Qiao, Y., Dai, J.: Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks. arXiv preprint arXiv:2312.14238 (2023)

Original

2023

Earlier work this paper cites.

Hafeez, A., Husain, M.A., Singh, S., Chauhan, A., Khan, M.T., Kumar, N., Chauhan, A., Soni, S.: Implementation of drone technology for farm monitoring & pesticide spraying: A review. Information processing in Agriculture 10

2023

Earlier work this paper cites.

Kotwal, J., Kashyap, R., Pathan, S.: Agricultural plant diseases identification: From traditional approach to deep learning. Materials Today: Proceedings 80

2023

Cited alongside, same era.

Li, J., Chen, D., Qi, X., Li, Z., Huang, Y., Morris, D., Tan, X.: Label-efficient learning in agriculture: A comprehensive review. Computers and Electronics in Agriculture 215

2023

Cited alongside, same era.

Liu, Y., Duan, H., Zhang, Y., Li, B., Zhang, S., Zhao, W., Yuan, Y., Wang, J., He, C., Liu, Z., et al.: Mmbench: Is your multi-modal model an all-around player? arXiv preprint arXiv:2307.06281 (2023)

Original

2023

Cited alongside, same era.

Nayak, A., Chakraborty, S., Swain, D.K.: Application of smartphone-image processing and transfer learning for rice disease and nutrient deficiency detection. Smart Agricultural Technology 4

2023

Cited alongside, same era.

Hello gpt-4o (2024), https://openai.com/index/hello-gpt-4o/ , accessed: 2024-07-01

2024

Closest in time.

He, M., Liu, Y., Wu, B., Yuan, J., Wang, Y., Huang, T., Zhao, B.: Efficient multimodal learning from data-centric perspective. arXiv preprint arXiv:2402.11530 (2024)

Original

2024

Closest in time.

Hu, Z., Yang, P., Jiang, Y., Bai, Z.: Prompting large language model with context and pre-answer for knowledge-based vqa. Pattern Recognition 151

2024

Closest in time.

Kok, E., Chen, C.: Occluded apples orientation estimator based on deep learning model for robotic harvesting. Computers and Electronics in Agriculture 219

2024

Closest in time.

Ktena, I., Wiles, O., Albuquerque, I., Rebuffi, S.A., Tanno, R., Roy, A.G., Azizi, S., Belgrave, D., Kohli, P., Cemgil, T., et al.: Generative models improve fairness of medical classifiers under distribution shifts. Nature Medicine pp. 1–8 (2024)