Crop growth stage estimation prior to canopy closure using deep learning algorithms

Rasti, Sanaz; Bleakley, Chris J.; Silvestre, G.C.M.; Holden, Nick; Langton, David; O’Hare, Gregory M. P.

doi:10.1007/s00521-020-05064-6

Cited by 33 publications

(12 citation statements)

References 37 publications

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“…Machine learning-based image processing techniques offer a promising opportunity for continuous monitoring of plant growth stages and nutrient status at the field level during the whole life cycle of a crop [78]. For example, Rasti, S. et al (2020) investigated the performance of three different machine learning approaches (CNN model, VGG19-based transfer learning, and SVM) for estimating wheat and barley growth stages with their customized dataset [79]. Over two consecutive years (2018 and 2019), the authors used a cell phone and a DJI Osmo+ camera to record high-quality videos at a height of 2 m in two postures, vertically downward looking at the field and at 45 • declination from the horizon; 138,000 images of 12 wheat growth stages and 11 barley growth stages were extracted from video recordings with a minimum of 120 ms.…”

Section: Field Scale: Abiotic Stress Assessment Growth Monitoring And...mentioning

confidence: 99%

“…In our review, we briefly introduced the details of collection methods for datasets used in some representative studies. Among these datasets, some were collected under laboratory conditions [41,49,51,65], some were captured in the field [45,52,63,66,70,79], and others were established by combining these two methods [43,50,53]. Figure 2 shows some publicly available datasets for agriculture sensing at the leaf or canopy scale.…”

Section: Datasetsmentioning

confidence: 99%

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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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Section: Field Scale: Abiotic Stress Assessment Growth Monitoring And...mentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

A Review of Deep Learning in Multiscale Agricultural Sensing

et al. 2022

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“…In a similar study, Bai et al (2018) determined the arrival of the rice heading stage by the number of the spike patches detected by a CNN network. Rasti et al (2021) investigated wheat and barley growth stage estimation by classification of proximal images using deep learning algorithm. The classification was carried out using three different machine learning approaches on an image dataset of 12 growth stages of wheat and 11 growth stages of barley.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for Rice Seedling Growth Stages Detection

Tan

Liu

et al. 2022

Front. Plant Sci.

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Recognizing rice seedling growth stages to timely do field operations, such as temperature control, fertilizer, irrigation, cultivation, and disease control, is of great significance of crop management, provision of standard and well-nourished seedlings for mechanical transplanting, and increase of yield. Conventionally, rice seedling growth stage is performed manually by means of visual inspection, which is not only labor-intensive and time-consuming, but also subjective and inefficient on a large-scale field. The application of machine learning algorithms on UAV images offers a high-throughput and non-invasive alternative to manual observations and its applications in agriculture and high-throughput phenotyping are increasing. This paper presented automatic approaches to detect rice seedling of three critical stages, BBCH11, BBCH12, and BBCH13. Both traditional machine learning algorithms and deep learning algorithms were investigated the discriminative ability of the three growth stages. UAV images were captured vertically downward at 3-m height from the field. A dataset consisted of images of three growth stages of rice seedlings for three cultivars, five nursing seedling densities, and different sowing dates. In the traditional machine learning algorithm, histograms of oriented gradients (HOGs) were selected as texture features and combined with the support vector machine (SVM) classifier to recognize and classify three growth stages. The best HOG-SVM model obtained the performance with 84.9, 85.9, 84.9, and 85.4% in accuracy, average precision, average recall, and F1 score, respectively. In the deep learning algorithm, the Efficientnet family and other state-of-art CNN models (VGG16, Resnet50, and Densenet121) were adopted and investigated the performance of three growth stage classifications. EfficientnetB4 achieved the best performance among other CNN models, with 99.47, 99.53, 99.39, and 99.46% in accuracy, average precision, average recall, and F1 score, respectively. Thus, the proposed method could be effective and efficient tool to detect rice seedling growth stages.

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“…Numerous studies are being actively conducted to analyze the growth of horticultural crops, and it involves automated image processing to replace existing manual work 24 – 27 . The monitoring system can perform various tasks automatically through the application of advanced technologies, such as object detection and size recognition of fruits.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, different input features have different resolutions, so a simple but more effective bi-directional FPN structure which takes different degrees of contribution to the output features is adopted, which broke out of the conventional FPN structure and resulting in huge improvement of detection performance. Different from the conventional FPN structure, Rasti et al 27 applied a deep learning-based object detection method, including transfer learning, to locate crops and estimate their growth stages. The constructed deep neural network is a convolutional neural network (CNN), and the weights finely adjusted using ImageNet were applied to the growth stage estimation.…”

Section: Introductionmentioning

confidence: 99%

CNN-based object detection and growth estimation of plum fruit (Prunus mume) using RGB and depth imaging techniques

Kim

Hong

Kim

et al. 2022

Sci Rep

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Modern people who value healthy eating habits have shown increasing interest in plum (Prunus mume) fruits, primarily owing to their nutritiousness and proven efficacy. As consumption increases, it becomes important to monitor work to prevent Prunus mume fruits from falling out. Moreover, determining the growth status of Prunus mume is also crucial and is attracting increasing attention. In this study, convolutional neural network (CNN)-based deep learning object detection was developed using RGBD images collected from Prunus mume farms. These RGBD images consider various environments, including the depth information of objects in the outdoor field. A faster region-based convolutional neural network (R-CNN), EfficientDet, Retinanet, and Single Shot Multibox Detector (SSD) were applied for detection, and the performance of all models was estimated by comparing their respective computing speeds and average precisions (APs). The test results show that the EfficientDet model is the most accurate, and SSD MobileNet is the fastest among the four models. In addition, the algorithm was developed to acquire the growth status of P. mume fruits by applying the coordinates and score values of bounding boxes to the depth map. Compared to the diameters of the artificial Prunus mume fruits used as the experimental group, the calculated diameters were very similar to those of the artificial objects. Collectively, the results demonstrate that the CNN-based deep learning Prunus mume detection and growth estimation method can be applied to real farmlands.

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Crop growth stage estimation prior to canopy closure using deep learning algorithms

Cited by 33 publications

References 37 publications

A Review of Deep Learning in Multiscale Agricultural Sensing

A Review of Deep Learning in Multiscale Agricultural Sensing

Machine Learning Approaches for Rice Seedling Growth Stages Detection

CNN-based object detection and growth estimation of plum fruit (Prunus mume) using RGB and depth imaging techniques

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