Robust Fruit Counting: Combining Deep Learning, Tracking, and Structure from Motion

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Beyond Trade-Off: An Optimized Binocular Stereo Vision Based Depth Estimation Algorithm for Designing Harvesting Robot in Orchards

Zhang, L., Hao, Q., Mao, Y., Su, J., & Cao, J. (2023) · Agriculture · doi:10.3390/agriculture13061117 · Cited by 9 · see citing works

Tracking and Counting Method for Tomato Fruits Scouting Robot in Greenhouse

Dai, G., Hu, L., Wang, P., & Rong, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-13844-7_6 · Cited by 8 · see citing works

Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization

Brune, M., Meisen, T., & Pomp, A. (2024) · Applied Sciences · doi:10.3390/app14062267 · Cited by 6 · see citing works

Few-Shot Fruit Segmentation via Transfer Learning

James, J. A., Manching, H. C., Hulse‐Kemp, A. M., & Beksi, W. J. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610003 · Cited by 6 · see citing works

Orchard sweet cherry color distribution estimation from wireless sensor networks and video-based fruit detection

Cossio-Montefinale, L., Quiñinao, C., & Verschae, R. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.110334 · Cited by 6 · see citing works

Deep Learning with Data Augmentation for Fruit Counting

Pawara, P., Boshchenko, A., Schomaker, L., & Wiering, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61401-0_20 · Cited by 6 · see citing works

Cross-region building counting in satellite imagery using counting consistency

Zakria, M., Rawal, H., Sultani, W., & Ali, M. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08923-0 · Cited by 6 · see citing works

A Moving Target Tracking System of Quadrotors with Visual-Inertial Localization

Lin, Z., Xu, W., & Wang, W. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161323 · Cited by 6 · see citing works

Noninvasive system for weight estimation in cactus crops: A YOLOv5-decision tree approach based on interval type-2 fuzzy sets

Rodríguez-Álvarez, J. L., García-Alcaráz, J. L., Vidal, R. P. I., Cuevas-Jacquez, R. A., & Díaz-Reza, J. R. (2024) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2024.105064 · Cited by 6 · see citing works

Deep Learning Solutions for Agricultural and Farming Activities

Karegowda, A. G., Devika, G., & Geetha, M. (2020) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-7998-2108-3.ch011 · Cited by 6 · see citing works

Yield Estimation using Deep Learning for Precision Agriculture

Osman, Y., Dennis, R., & Elgazzar, K. (2021) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot51360.2021.9595143 · Cited by 5 · see citing works

Tracking and Counting Apples in Orchards Under Intermittent Occlusions and Low Frame Rates

Matos, G. P., Santiago, C., Costeira, J. P., Saldanha, R. L., & Morgado, E. M. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00550 · Cited by 5 · see citing works

Detection and counting of overlapped apples based on convolutional neural networks

Gao, M., Ma, S., Zhang, Y., & Xue, Y. (2022) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-213072 · Cited by 5 · see citing works

PheMuT: A phenology-informed, multi-modal time-series model for strawberry yield forecasting

Huang, Z., Lee, W. S., Ampatzidis, Y. G., Agehara, S., & Peres, N. A. (2026) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2026.111526 · Cited by 4 · see citing works

CapsiScan: Autonomous Robotic System for Monitoring and Managing Greenhouse-Based Capsicum Farms

Jayakody, E. R. T., Rabel, W. T. T., Estrop, R. D. V., Herath, K. G. L. P., Harshanath, S. M. B., & Abeygunawardhana, P. K. W. (2023) · International Conference on Advancements in Computing (ICAC) · doi:10.1109/icac60630.2023.10417337 · Cited by 4 · see citing works

Contrasting bean analysis system based on YOLOv5 and a neural network model using the interval type-2 fuzzy set approach

Rodríguez-Álvarez, J. L., García-Alcaráz, J. L., Puig, R., & Soto-Cabral, A. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10217-y · Cited by 4 · see citing works

ASPEN study case: Real time in situ apples detection and characterization

Chiang, C., Monney, A., Monney, P., & Christen, D. (2024) · Smart Agricultural Technology · doi:10.1016/j.atech.2024.100506 · Cited by 3 · see citing works

DATR: Diffusion-Based 3D Apple Tree Reconstruction Framework With Sparse-View

Qiu, T., Zoubi, A. Z., Lin, Y., Du, R., Cheng, L., & Jiang, Y. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3655283 · Cited by 3 · see citing works

Comparative analysis of 2D and 3D vineyard yield prediction system using artificial intelligence

Barbole, D. K., & Jadhav, P. M. (2023) · Journal of Agricultural Engineering · doi:10.4081/jae.2023.1545 · Cited by 3 · see citing works

Extended Kalman Filter Based Tracking Method for Accurate Fruit Yield Estimation Preserving SE(3) Equivariance

Pichhika, H. C., Subudhi, P., & Yerra, R. V. P. (2024) · IEEE Transactions on AgriFood Electronics · doi:10.1109/tafe.2024.3513637 · Cited by 3 · see citing works

Detection and Counting of Lemons using Artificial Vision and Tracking Techniques for Real Time Harvest Estimation

Serafino, S., Cicerchia, B., Pérez, G., Adorno, S., & Balmer, A. (2020) · Latin American Computer Conference (CLEI) · doi:10.1109/clei52000.2020.00064 · Cited by 3 · see citing works

Monocular Camera Based Fruit Counting and Mapping With Semantic Data Association

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Yield Estimation using Deep Learning for Precision Agriculture

Osman, Y., Dennis, R., & Elgazzar, K. (2021) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot51360.2021.9595143 · Cited by 5 · see citing works

Counting Apples and Oranges With Deep Learning: A Data-Driven Approach

Chen, S. W., Shivakumar, S. S., Dcunha, S., Das, J., Okon, E., Qu, C., Taylor, C. J., & Kumar, V. (2017) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2017.2651944 · Cited by 406 · see citing works

Cascade-SORT: A robust fruit counting approach using multiple features cascade matching

He, L., Wu, F., Du, X., & Zhang, G. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107223 · Cited by 39 · see citing works

FruitNeRF: A Unified Neural Radiance Field based Fruit Counting Framework

Meyer, L., Gilson, A., Schmid, U., & Stamminger, M. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802065 · Cited by 21 · see citing works