Active Learning for Crop-Weed Discrimination by Image Classification from Convolutional Neural Network’s Feature Pyramid Levels

Zahidi, Usman A.; Cielniak, Grzegorz

doi:10.1007/978-3-030-87156-7_20

Cited by 5 publications

(1 citation statement)

References 19 publications

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“…There have also been two studies on active learning for agricultural purposes. Zahidi and Cielniak (2021) researched active learning for image classification of crops and weeds, and it was shown that with active learning only 60% of the images were needed to achieve a performance comparable to that of a CNN trained on the complete dataset. Chandra et al (2020) researched active learning for object detection in cereal crops, and it was found that 50% of the annotation time could be saved by active learning.…”

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confidence: 99%

Perception models for selective harvesting robots in fruit and vegetable production

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Section: Introductionmentioning

confidence: 99%

Perception models for selective harvesting robots in fruit and vegetable production

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Optimising robotic operation speed with edge computing via 5G network: Insights from selective harvesting robots

Zahidi,

Khan,

Zhivkov

et al. 2024

Journal of Field Robotics

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Selective harvesting by autonomous robots will be a critical enabling technology for future farming. Increases in inflation and shortages of skilled labor are driving factors that can help encourage user acceptability of robotic harvesting. For example, robotic strawberry harvesting requires real‐time high‐precision fruit localization, three‐dimensional (3D) mapping, and path planning for 3D cluster manipulation. Whilst industry and academia have developed multiple strawberry harvesting robots, none have yet achieved human–cost parity. Achieving this goal requires increased picking speed (perception, control, and movement), accuracy, and the development of low‐cost robotic system designs. We propose the edge‐server over 5G for Selective Harvesting (E5SH) system, which is an integration of high bandwidth and low latency Fifth‐Generation (5G) mobile network into a crop harvesting robotic platform, which we view as an enabler for future robotic harvesting systems. We also consider processing scale and speed in conjunction with system environmental and energy costs. A system architecture is presented and evaluated with support from quantitative results from a series of experiments that compare the performance of the system in response to different architecture choices, including image segmentation models, network infrastructure (5G vs. Wireless Fidelity), and messaging protocols, such as Message Queuing Telemetry Transport and Transport Control Protocol Robot Operating System. Our results demonstrate that the E5SH system delivers step‐change peak processing performance speedup of above 18‐fold than a standalone embedded computing Nvidia Jetson Xavier NX system.

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Advancing precision agriculture: domain-specific augmentations and robustness testing for convolutional neural networks in precision spraying evaluation

Rogers,

De La Iglesia,

Zebin

et al. 2024

Neural Comput & Applic

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Modern agriculture relies heavily on the precise application of chemicals such as fertilisers, herbicides, and pesticides, which directly affect both crop yield and environmental footprint. Therefore, it is crucial to assess the accuracy of precision sprayers regarding the spatial location of spray deposits. However, there is currently no fully automated evaluation method for this. In this study, we collected a novel dataset from a precision spot spraying system to enable us to classify and detect spray deposits on target weeds and non-target crops. We employed multiple deep convolutional backbones for this task; subsequently, we have proposed a robustness testing methodology for evaluation purposes. We experimented with two novel data augmentation techniques: subtraction and thresholding which enhanced the classification accuracy and robustness of the developed models. On average, across nine different tests and four distinct convolutional neural networks, subtraction improves robustness by 50.83%, and thresholding increases by 42.26% from a baseline. Additionally, we have presented the results from a novel weakly supervised object detection task using our dataset, establishing a baseline Intersection over Union score of 42.78%. Our proposed pipeline includes an explainable artificial intelligence stage and provides insights not only into the spatial location of the spray deposits but also into the specific filtering methods within that spatial location utilised for classification.

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Active Learning for Crop-Weed Discrimination by Image Classification from Convolutional Neural Network’s Feature Pyramid Levels

Cited by 5 publications

References 19 publications

Perception models for selective harvesting robots in fruit and vegetable production

Perception models for selective harvesting robots in fruit and vegetable production

Optimising robotic operation speed with edge computing via 5G network: Insights from selective harvesting robots

Advancing precision agriculture: domain-specific augmentations and robustness testing for convolutional neural networks in precision spraying evaluation

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