Improved crop row detection with deep neural network for early-season maize stand count in UAV imagery

Pang, Yan; Shi, Yeyin; Gao, S.; Jiang, Feng; Veeranampalayam-Sivakumar, Arun Narenthiran; Thompson, Laura J.; Luck, Joe D.; Liu, Chao

doi:10.1016/j.compag.2020.105766

Cited by 68 publications

(35 citation statements)

References 19 publications

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“…We assume that it makes sense to use both of these methods depending on the conditions. The results of these methods (tables 3 and 4) significantly exceed the results presented in works on computer vision in recent years [49][50][51]. This is merit not only of modern methods of computer vision but also of successful shooting conditions.…”

Section: B Image Processing Techniquesmentioning

confidence: 66%

“…Wei Yang et al [49] proposed an integrated CNN model based on hyperspectral and RGB images taken at 5 stages of corn growth. Yan Pang et al [50] based on drone images using the combined convolutional neural network MaxArea Mask Scoring RCNN, areas of poor germination of corn were identified. The authors noted the high processing speed, however, does not allow the use of this system for real-time applications.…”

Section: B Artificial Intelligence In Irrigated Agriculturementioning

confidence: 99%

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Artificial Neural Networks and Computer Vision’s-Based Phytoindication Systems for Variable Rate Irrigation Improving

et al. 2022

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The article proposes a methodology for optimizing the process of irrigation of crops using a phytoindication system based on computer vision methods. We have proposed an algorithm and developed a system for obtaining a map of irrigation for maize in low latency mode. The system can be installed on a center pivot irrigation and consists of 8 IP cameras connected to a DVR connected to a laptop. The algorithm consists of three stages. Image preprocessing stage -applying an integrated excess green and excess red difference (ExGR) index. The classification stage is the application of the method that we choose depending on the operating conditions of the system. At the final stage, a neural network trained using the Resilient Propagation method is used, which determines the rate of watering of plants in the current sector of the location of the sprinkler. The selected methods of pretreatment and classification made it possible to achieve an accuracy of plant identification up to 93%, growth stages -up to 92% (with unconsolidated maize sowing and good lighting). System performance up to 100 plants in one second, which exceeds the performance of similar systems. The neural network showed an accuracy of 92% on the training set and 87% on the test set. Dynamic analysis of spatial and temporal variability leads to an increase in productivity and efficiency of water use. In addition, given the ubiquitous distribution of agribusiness management systems, this approach is quite simple to implement in the farm's conditions.

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Section: B Image Processing Techniquesmentioning

confidence: 66%

Section: B Artificial Intelligence In Irrigated Agriculturementioning

confidence: 99%

Artificial Neural Networks and Computer Vision’s-Based Phytoindication Systems for Variable Rate Irrigation Improving

et al. 2022

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“…Waheed et al [131] and Atila [132] gave general advice, such as using transfer learning when all layers of the models were trained, to improve the accuracy of CNN, and used corn leaf diseases as an example. In addition, these recommendations were implemented by Pang et al [133], who determined early-season corn stands using geometric descriptor information and deep neural networks.…”

Section: Future Directionsmentioning

confidence: 99%

Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review

2021

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Automation, including machine learning technologies, are becoming increasingly crucial in agriculture to increase productivity. Machine vision is one of the most popular parts of machine learning and has been widely used where advanced automation and control have been required. The trend has shifted from classical image processing and machine learning techniques to modern artificial intelligence (AI) and deep learning (DL) methods. Based on large training datasets and pre-trained models, DL-based methods have proven to be more accurate than previous traditional techniques. Machine vision has wide applications in agriculture, including the detection of weeds and pests in crops. Variation in lighting conditions, failures to transfer learning, and object occlusion constitute key challenges in this domain. Recently, DL has gained much attention due to its advantages in object detection, classification, and feature extraction. DL algorithms can automatically extract information from large amounts of data used to model complex problems and is, therefore, suitable for detecting and classifying weeds and crops. We present a systematic review of AI-based systems to detect weeds, emphasizing recent trends in DL. Various DL methods are discussed to clarify their overall potential, usefulness, and performance. This study indicates that several limitations obstruct the widespread adoption of AI/DL in commercial applications. Recommendations for overcoming these challenges are summarized.

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“…2021, 13, 3526 2 of 16 and then facilitate real-time tractor or robot navigation [6][7][8]. However, to create sitespecific management practice maps for a large field considering the location of crop rows, row detection by field images is not efficient due to its limited observation scope [9]. In addition, satellite or manned airborne imaging for row detection is limited by the spatial resolution, high operational costs, or long delivery of products [10].…”

Section: Introductionmentioning

confidence: 99%

A New Method for Crop Row Detection Using Unmanned Aerial Vehicle Images

Chen

Wang

et al. 2021

Remote Sensing

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Crop row detection using unmanned aerial vehicle (UAV) images is very helpful for precision agriculture, enabling one to delineate site-specific management zones and to perform precision weeding. For crop row detection in UAV images, the commonly used Hough transform-based method is not sufficiently accurate. Thus, the purpose of this study is to design a new method for crop row detection in orthomosaic UAV images. For this purpose, nitrogen field experiments involving cotton and nitrogen and water field experiments involving wheat were conducted to create different scenarios for crop rows. During the peak square growth stage of cotton and the jointing growth stage of wheat, multispectral UAV images were acquired. Based on these data, a new crop detection method based on least squares fitting was proposed and compared with a Hough transform-based method that uses the same strategy to preprocess images. The crop row detection accuracy (CRDA) was used to evaluate the performance of the different methods. The results showed that the newly proposed method had CRDA values between 0.99 and 1.00 for different nitrogen levels of cotton and CRDA values between 0.66 and 0.82 for different nitrogen and water levels of wheat. In contrast, the Hough transform method had CRDA values between 0.93 and 0.98 for different nitrogen levels of cotton and CRDA values between 0.31 and 0.53 for different nitrogen and water levels of wheat. Thus, the newly proposed method outperforms the Hough transform method. An effective tool for crop row detection using orthomosaic UAV images is proposed herein.

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Improved crop row detection with deep neural network for early-season maize stand count in UAV imagery

Cited by 68 publications

References 19 publications

Artificial Neural Networks and Computer Vision’s-Based Phytoindication Systems for Variable Rate Irrigation Improving

Artificial Neural Networks and Computer Vision’s-Based Phytoindication Systems for Variable Rate Irrigation Improving

Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review

A New Method for Crop Row Detection Using Unmanned Aerial Vehicle Images

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