| Detecting greenhouse strawberries (mature and immature), using deep convolutional neural network H Habaragamuwa, Y Ogawa, T Suzuki, T Shiigi, M Ono, N Kondo Engineering in Agriculture, Environment and Food 11 (3), 127-138, 2018 | 87 | 2018 |
| Potato quality grading based on machine vision and 3D shape analysis Q Su, N Kondo, M Li, H Sun, DF Al Riza, H Habaragamuwa Computers and electronics in agriculture 152, 261-268, 2018 | 77 | 2018 |
| Automated abnormal potato plant detection system using deep learning models and portable video cameras Y Oishi, H Habaragamuwa, Y Zhang, R Sugiura, K Asano, K Akai, ... International Journal of Applied Earth Observation and Geoinformation 104 …, 2021 | 27 | 2021 |
| Monitoring harvested paddy during combine harvesting using a machine vision-Double lighting system J Mahirah, K Yamamoto, M Miyamoto, N Kondo, Y Ogawa, T Suzuki, ... Engineering in agriculture, Environment and food 10 (2), 140-149, 2017 | 22 | 2017 |
| Potato quality grading based on depth imaging and convolutional neural network Q Su, N Kondo, DF Al Riza, H Habaragamuwa Journal of Food Quality 2020, 1-9, 2020 | 20 | 2020 |
| Double lighting machine vision system to monitor harvested paddy grain quality during head-feeding combine harvester operation M Jahari, K Yamamoto, M Miyamoto, N Kondo, Y Ogawa, T Suzuki, ... Machines 3 (4), 352-363, 2015 | 19 | 2015 |
| Temperature compensation method using base-station for spread spectrum sound-based positioning system in green house T Shiigi, N Kondo, Y Ogawa, T Suzuki, H Harshana Engineering in agriculture, environment and food 10 (3), 233-242, 2017 | 7 | 2017 |
| Is spread spectrum sound a robust local positioning system for a quadcopter operating in a greenhouse? Z Huang, M Ono, T Shiigi, T Suzuki, H Habaragamuwa, H Nakanishi, ... Chemical Engineering Transactions 58, 829-834, 2017 | 5 | 2017 |
| Greenhouse Based Orientation Measurement System using Spread Spectrum Sound Z Huang, H Fukuda, TLW Jacky, X Zhao, H Habaragamuwa, T Shiigi, ... IFAC-PapersOnLine 51 (17), 108-111, 2018 | 4 | 2018 |
| Achieving explainability for plant disease classification with disentangled Variational Autoencoders H Habaragamuwa, Y Oishi, K Tanaka Engineering Applications of Artificial Intelligence 133, 107982, 2024 | 3 | 2024 |
| Stem water potential estimation from images using a field noise-robust deep regression-based approach in peach trees T Yamane, H Habaragamuwa, R Sugiura, T Takahashi, H Hayama, ... Scientific Reports 13 (1), 22359, 2023 | | 2023 |
| Achieving Explainability for Deep Learning-Based Image Classification Applications in Agriculture : Methods and Approaches Habragamuwa Harshana, 大石 優, 竹谷 勝田中 健一 Journal of the Japanese Society of Agricultural Machinery and Food Engineers …, 2020 | | 2020 |
| Research Article Potato Quality Grading Based on Depth Imaging and Convolutional Neural Network Q Su, N Kondo, DF Al Riza, H Habaragamuwa | | 2020 |
| 農業におけるディープラーニングに基づく画像分類アプリケーションの説明可能性の実現: 方法とアプローチ 大石優， 竹谷勝， 田中健一 農業食料工学会 82 (3), 214-223, 2020 | | 2020 |
| AI を活用した農作物の病害虫診断等で活用 画像診断根拠を可視化できる AI 大石優 機械化農業= Farming mechanization, 20-23, 2020 | | 2020 |
| Plant Disease Identification using Explainable Features with Deep Convolutional Neural Network Harshana Habaragamuwa , Yu Oishi, Masaru Takeya, Kenichi Tanaka International Join Conference on JSAM and SASJ, and CIGR VI Technical …, 2019 | | 2019 |
| Deep Convolutional Neural Network's Applicability and Interpretability for Agricultural Machine Vision Systems H Harshana Kyoto University, 2018 | | 2018 |
| Potato quality grading based on machine vision and 3D shape analysis. SQH Su QingHua, N Kondo, LMZ Li MinZan, SH Sun Hong, DF Al-Riza, ... | | 2018 |
| Deep convolutional neural network's applicability and interpretability for agricultural machine vision systems H Habaragamuwa 京都大学, 2018 | | 2018 |
