DeepCorn: A semi-supervised deep learning method for high-throughput image-based corn kernel counting and yield estimation

Khaki, Saeed; Pham, Hieu; Han, Ye; Kuhl, Andy; Kent, Wade; Wang, Lizhi

doi:10.1016/j.knosys.2021.106874

Cited by 53 publications

(25 citation statements)

References 45 publications

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“…State-of-the-art networks include the faster region-based convolutional network (Faster R-CNN) [25], the mask region-based convolutional network (Mask-RCNN) [26], and You Only Look Once (Yolo) [27][28][29]. It is a promising method to develop algorithms for agricultural object detection and counting based on deep convolutional neural networks (CNNs) [13,17,30]. In particular, Faster R-CNN [25] and Yolo [28] are representative two-stage and one-stage detection networks, respectively.…”

Section: Introductionmentioning

confidence: 99%

A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field

Wang

Xiang

Tang

et al. 2021

Sensors

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Accurate corn stand count in the field at early season is of great interest to corn breeders and plant geneticists. However, the commonly used manual counting method is time consuming, laborious, and prone to error. Nowadays, unmanned aerial vehicles (UAV) tend to be a popular base for plant-image-collecting platforms. However, detecting corn stands in the field is a challenging task, primarily because of camera motion, leaf fluttering caused by wind, shadows of plants caused by direct sunlight, and the complex soil background. As for the UAV system, there are mainly two limitations for early seedling detection and counting. First, flying height cannot ensure a high resolution for small objects. It is especially difficult to detect early corn seedlings at around one week after planting, because the plants are small and difficult to differentiate from the background. Second, the battery life and payload of UAV systems cannot support long-duration online counting work. In this research project, we developed an automated, robust, and high-throughput method for corn stand counting based on color images extracted from video clips. A pipeline developed based on the YoloV3 network and Kalman filter was used to count corn seedlings online. The results demonstrate that our method is accurate and reliable for stand counting, achieving an accuracy of over 98% at growth stages V2 and V3 (vegetative stages with two and three visible collars) with an average frame rate of 47 frames per second (FPS). This pipeline can also be mounted easily on manned cart, tractor, or field robotic systems for online corn counting.

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Section: Introductionmentioning

confidence: 99%

A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field

Wang

Xiang

Tang

et al. 2021

Sensors

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“…(iii). Some novel strategies, such as transfer learning [104][105][106][107][108][109], few-shot learning [31,32,114,115], graph convolutional networks (GCN) [66], generative adversarial networks (GAN) [127], and semi-supervised learning [128] have been proposed to reduce the dependency of DL models on the agricultural datasets. However, their performance has not been fully released.…”

Section: Deep Learning Algorithms and Neural Networkmentioning

confidence: 99%

A Review of Deep Learning in Multiscale Agricultural Sensing

et al. 2022

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Population growth, climate change, and the worldwide COVID-19 pandemic are imposing increasing pressure on global agricultural production. The challenge of increasing crop yield while ensuring sustainable development of environmentally friendly agriculture is a common issue throughout the world. Autonomous systems, sensing technologies, and artificial intelligence offer great opportunities to tackle this issue. In precision agriculture (PA), non-destructive and non-invasive remote and proximal sensing methods have been widely used to observe crops in visible and invisible spectra. Nowadays, the integration of high-performance imagery sensors (e.g., RGB, multispectral, hyperspectral, thermal, and SAR) and unmanned mobile platforms (e.g., satellites, UAVs, and terrestrial agricultural robots) are yielding a huge number of high-resolution farmland images, in which rich crop information is compressed. However, this has been accompanied by challenges, i.e., ways to swiftly and efficiently making full use of these images, and then, to perform fine crop management based on information-supported decision making. In the past few years, deep learning (DL) has shown great potential to reshape many industries because of its powerful capabilities of feature learning from massive datasets, and the agriculture industry is no exception. More and more agricultural scientists are paying attention to applications of deep learning in image-based farmland observations, such as land mapping, crop classification, biotic/abiotic stress monitoring, and yield prediction. To provide an update on these studies, we conducted a comprehensive investigation with a special emphasis on deep learning in multiscale agricultural remote and proximal sensing. Specifically, the applications of convolutional neural network-based supervised learning (CNN-SL), transfer learning (TL), and few-shot learning (FSL) in crop sensing at land, field, canopy, and leaf scales are the focus of this review. We hope that this work can act as a reference for the global agricultural community regarding DL in PA and can inspire deeper and broader research to promote the evolution of modern agriculture.

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“…Several methods have been developed to extract ear and grain characteristics from images (Chipindu et al, 2020;Makanza et al, 2018;Severini et al, 2011). They are usually based on manual or non-standardized acquisition involving either isolated grains after shelling (Liang et al, 2016;Makanza et al, 2018;Miller et al, 2017;Ni et al, 2019;Severini et al, 2011) or one side of the ear (Chipindu et al, 2020;Khaki et al, 2020Khaki et al, , 2021Kienbaum et al, 2021;Miller et al, 2017;Wu et al, 2020). Thus, the spatial distributions of grain presence/absence (grain set vs grain abortion) and grain traits along and around the ear is usually not, or only partly, considered.…”

Section: Introductionmentioning

confidence: 99%

Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits

Oury¹,

Leroux²,

Turc

et al. 2021

Preprint

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Background: Characterizing plant genetic resources and their response to the environment through accurate measurement of relevant traits is crucial to genetics and breeding. The spatial organization of the maize ear provides insights into the response of grain yield to environmental conditions. Current automated methods for phenotyping the maize ear do not capture these spatial features. Results: We developed EARBOX, a low-cost, open-source system for automated phenotyping of maize ears. EARBOX integrates open-source technologies for both software and hardware that facilitate its deployment and improvement for specific research questions. The imaging platform consists of a customized box in which ears are repeatedly imaged as they rotate via motorized rollers. With deep learning based on convolutional neural networks, the image analysis algorithm uses a two-step procedure: ear-specific grain masks are first created and subsequently used to extract a range of trait data per ear, including ear shape and dimensions, the number of grains and their spatial organization, and the distribution of grain dimensions along the ear. The reliability of each trait was validated against ground-truth data from manual measurements. Moreover, EARBOX derives novel traits, inaccessible through conventional methods, especially the distribution of grain dimensions along grain cohorts, relevant for ear morphogenesis, and the distribution of abortion frequency along the ear, relevant for plant response to stress, especially soil water deficit. Conclusions: The proposed system provides robust and accurate measurements of maize ear traits including spatial features. Future developments include grain type and colour categorization. This method opens avenues for high-throughput genetic or functional studies in the context of plant adaptation to a changing environment.

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DeepCorn: A semi-supervised deep learning method for high-throughput image-based corn kernel counting and yield estimation

Cited by 53 publications

References 45 publications

A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field

A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field

A Review of Deep Learning in Multiscale Agricultural Sensing

Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits

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