Deep learning-based segmentation of multiple species of weeds and corn crop using synthetic and real image datasets

Picón, Artzai; San-Emeterio, Miguel G.; Bereciartua-Pérez, Arantza; Klukas, Christian; Eggers, Till; Navarra-Mestre, Ramón

doi:10.1016/j.compag.2022.106719

Cited by 46 publications

(19 citation statements)

References 18 publications

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“…Examples of this cross-over are presented in Table 2 , where despite semantic differences, the research fundamentals between plant science and weed recognition are common. Khaki et al (2022a) recognise this opportunity for the generalizability of a stand count approach in maize to contribute to weed detection, which could be combined with works such as Picon et al (2022) for maize extraction within weedy environments. Recognising the potential for research overlap, Weyler et al (2021) present a crop-weed detection system, which incorporates leaf counting for growth stage estimation and in-field phenotyping.…”

Section: Similarities Between Phenotyping and Weed Recognitionmentioning

confidence: 99%

More eyes on the prize: open-source data, software and hardware for advancing plant science through collaboration

Coleman

Salter

2023

AoB PLANTS

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Automating the analysis of plants using image processing would help remove barriers to phenotyping and large-scale precision agricultural technologies, such as site-specific weed control. The combination of accessible hardware and high-performance deep learning (DL) tools for plant analysis is becoming widely recognised as a path forward for both plant science and applied precision agricultural purposes. Yet, a lack of collaboration in image analysis for plant science, despite the open-source origins of much of the technology, is hindering development. Here, we show how tools developed for specific attributes of phenotyping or weed recognition for precision weed control have substantial overlapping data structure, software/hardware requirements and outputs. An open-source approach to these tools facilitates interdisciplinary collaboration, avoiding unnecessary repetition and allowing research groups in both basic and applied sciences to capitalise on advancements and resolve respective bottlenecks. The approach mimics that of machine learning in its nascence. Three areas of collaboration are identified as critical for improving efficiency, (1) standardised, open-source, annotated dataset development with consistent metadata reporting; (2) establishment of accessible and reliable training and testing platforms for DL algorithms; and (3) sharing of all source code used in the research process. The complexity of imaging plants and cost of annotating image datasets means that collaboration from typically distinct fields will be necessary to capitalise on the benefits of DL for both applied and basic science purposes.

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Section: Similarities Between Phenotyping and Weed Recognitionmentioning

confidence: 99%

More eyes on the prize: open-source data, software and hardware for advancing plant science through collaboration

Coleman

Salter

2023

AoB PLANTS

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“…The approach recalled up to 92% of weeds present in the area between crop rows, benefiting from the constrained inter-row environment. As research continues into large-scale, more complex environments, the ability to exploit crop agronomy and cultural practices is likely to be reduced, with reliance predominantly on advanced architectures and training methods (Picon et al 2022).…”

Section: Cropping System Contextmentioning

confidence: 99%

Weed detection to weed recognition: reviewing 50 years of research to identify constraints and opportunities for large-scale cropping systems

Coleman

Bender

et al. 2022

Weed Technol

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The past 50 years of advances in weed recognition technologies have poised site-specific weed control (SSWC) on the cusp of requisite performance for large-scale production systems. The technology offers improved management of diverse weed morphology over highly variable background environments. SSWC enables the use of non-selective weed control options, such as lasers and electrical weeding, as feasible in-crop selective alternatives to herbicides by targeting individual weeds. This review looks at the progress made over this half-century of research and its implications for future weed recognition and control efforts; summarizing advances in computer vision techniques and the most recent deep convolutional neural network (CNN) approaches to weed recognition. The first use of CNNs for plant identification in 2015 began an era of rapid improvement in algorithm performance on larger and more diverse datasets. These performance gains and subsequent research have shown that the variability of large-scale cropping systems is best managed by deep learning for in-crop weed recognition. The benefits of deep learning and improved accessibility to open-source software and hardware tools has been evident in the adoption of these tools by weed researchers and the increased popularity of CNN-based weed recognition research. The field of machine learning holds substantial promise for weed control, especially the implementation of truly integrated weed management strategies. While previous approaches sought to reduce environmental variability or manage it with advanced algorithms, research in deep learning architectures suggests that large-scale, multi-modal approaches are the future for weed recognition.

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“…As described in [ 40 , 42 , 45 , 48 , 50 , 61 ], R-CNN models such as Mask-RCNN and Faster-RCNN, two of the most widely used DL models, are used in crop yield prediction applications, especially for tomato and strawberry. Other custom DL models for detecting crops have been proposed in the studies of [ 35 , 38 , 44 , 54 ].…”

Section: Deep Learning In Ceamentioning

confidence: 99%

Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects

Ojo

Zahid

2022

Sensors

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Controlled environment agriculture (CEA) is an unconventional production system that is resource efficient, uses less space, and produces higher yields. Deep learning (DL) has recently been introduced in CEA for different applications including crop monitoring, detecting biotic and abiotic stresses, irrigation, microclimate prediction, energy efficient controls, and crop growth prediction. However, no review study assess DL’s state of the art to solve diverse problems in CEA. To fill this gap, we systematically reviewed DL methods applied to CEA. The review framework was established by following a series of inclusion and exclusion criteria. After extensive screening, we reviewed a total of 72 studies to extract the useful information. The key contributions of this article are the following: an overview of DL applications in different CEA facilities, including greenhouse, plant factory, and vertical farm, is presented. We found that majority of the studies are focused on DL applications in greenhouses (82%), with the primary application as yield estimation (31%) and growth monitoring (21%). We also analyzed commonly used DL models, evaluation parameters, and optimizers in CEA production. From the analysis, we found that convolutional neural network (CNN) is the most widely used DL model (79%), Adaptive Moment Estimation (Adam) is the widely used optimizer (53%), and accuracy is the widely used evaluation parameter (21%). Interestingly, all studies focused on DL for the microclimate of CEA used RMSE as a model evaluation parameter. In the end, we also discussed the current challenges and future research directions in this domain.

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Deep learning-based segmentation of multiple species of weeds and corn crop using synthetic and real image datasets

Cited by 46 publications

References 18 publications

More eyes on the prize: open-source data, software and hardware for advancing plant science through collaboration

More eyes on the prize: open-source data, software and hardware for advancing plant science through collaboration

Weed detection to weed recognition: reviewing 50 years of research to identify constraints and opportunities for large-scale cropping systems

Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects

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