Real-Time Selective Spraying for Viola Rope Control in Soybean and Cotton Crops Using Deep Learning

Mion, Renildo Luiz; Silveira, Adriano de O.; Mamiya, Arthur A.

doi:10.1590/1809-4430-eng.agric.v42nepe20210163/2022

Cited by 6 publications

(6 citation statements)

References 23 publications

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“…Detection errors can be caused by light transitions that can severely affect the imaging quality with shading between fruit and weed branches and leaves. According to Sabóia et al (2022), the low proportion of objects detected in the image may be because of the constant search for focus with movement, as the camera equipment used performs auto-zoom and fails to improve the focus. According to Zheng et al (2017), for natural RGB images, detection becomes difficult due to changes in lighting and weather, with difficulty in distinguishing colors in shadow areas.…”

Section: Comparison Experiments Eca-retinanet Detection Resultsmentioning

confidence: 99%

Assessing Pineapple Maturity in Complex Scenarios Using an Improved Retinanet Algorithm

2023

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In China, low levels of accuracy in predicting when pineapple crops will reach maturity can result from environmental variation such as light changes, fruit overlap, and shading. Therefore, this study proposed the use of an improved RetinaNet algorithm (ECA-Retinanet) based on the ECA attention mechanism. The ECA attention mechanism was embedded into the classification subnet of RetinaNet to improve accuracy in detecting different levels of maturity in pineapples. A new pineapple dataset was collected comprising four different growth stages under mild and severe complex scenarios. The experimental results have shown that the mAP (Mean Average Precision) and F1 score (Balanced Score) of the ECA-Retinanet model were 97.69%, 94.75%, 93.2%, and 90% for identification in mild and severe complex scenarios. These values are 0.42%, 2%, 1.78%, and 1.5% higher than the original RetinaNet model which exceeds those of the six existing state-of-the-art detection models. The results have indicated that the proposed algorithm could be used for accurate identification of pineapple fruit and can detect fruit maturity using ground color images in the natural environment. The study findings provide a technical reference for automatic picking robots and early yield estimation.

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Section: Comparison Experiments Eca-retinanet Detection Resultsmentioning

confidence: 99%

Assessing Pineapple Maturity in Complex Scenarios Using an Improved Retinanet Algorithm

2023

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“…In contrast, the Logitech c920 camera was not suitable for weed detection, demonstrating that SLR-type cameras are preferred for the development of mobile platforms, field carts, or robots due to their image quality and different adjustment options. In the work of [65], a semi-professional Nikon P250 camera (Nikon Corporation, Tokyo, Japan) was used to develop a prototype autonomous sprayer; similarly, in [36], a field robot was designed to detect weeds in high-density crops using a 20-Mpixel JAI camera (JAI A/S, Copenhagen, Denmark), which is a high-speed industrial camera.…”

Section: Discussionmentioning

confidence: 99%

Application of Convolutional Neural Networks in Weed Detection and Identification: A Systematic Review

García-Navarrete,

Correa-Guimaraes,

Navas-Gracia

2024

Agriculture

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Weeds are unwanted and invasive plants that proliferate and compete for resources such as space, water, nutrients, and sunlight, affecting the quality and productivity of the desired crops. Weed detection is crucial for the application of precision agriculture methods and for this purpose machine learning techniques can be used, specifically convolutional neural networks (CNN). This study focuses on the search for CNN architectures used to detect and identify weeds in different crops; 61 articles applying CNN architectures were analyzed during the last five years (2019–2023). The results show the used of different devices to acquire the images for training, such as digital cameras, smartphones, and drone cameras. Additionally, the YOLO family and algorithms are the most widely adopted architectures, followed by VGG, ResNet, Faster R-CNN, AlexNet, and MobileNet, respectively. This study provides an update on CNNs that will serve as a starting point for researchers wishing to implement these weed detection and identification techniques.

show abstract

“…In contrast, the Logitech c920 camera was not suitable for weed detection, demonstrating that SLRtype cameras are preferred for the development of mobile platforms, field carts, or robots due to their image quality and different adjustment options. In the work of [65], a semi-professional Nikon P250 camera was used to develop a prototype autonomous sprayer; similarly, in [36], a field robot was designed to detect weeds in high-density crops using a 20-Mpixel JAI camera, which is a high-speed industrial camera.…”

Section: Discussionmentioning

confidence: 99%

Application of Convolutional Neural Networks in Weed Detection and Identification: A Systematic Review.

García-Navarrete,

Correa-Guimaraes,

Navas-Gracia

2024

Preprint

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Weeds are unwanted and invasive plants that proliferate and compete for resources such as space, water, nutrients, and sunlight, affecting the quality and productivity of the desired crops. Weed detection is crucial for the application of precision agriculture methods and for this purpose machine learning techniques can be used, specifically convolutional neural networks (CNN). This study focuses on the search for CNN architectures and technology used to detect and identify weeds in different crops; 61 articles applying CNN architectures and technology were analyzed in the last five years (2019-2023). The results show the used of different devices to acquire the image for training, such as digital cameras, smartphones, and drone cameras. Additionally, the YOLO family and algorithms are the most widely adopted architectures, followed by VGG, ResNet, Faster R-CNN, AlexNet, and MobileNet, respectively. This study provides an update on CNNs that will serve as a starting point for researchers wishing to implement these weed detection and identification techniques.

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Real-Time Selective Spraying for Viola Rope Control in Soybean and Cotton Crops Using Deep Learning

Cited by 6 publications

References 23 publications

Assessing Pineapple Maturity in Complex Scenarios Using an Improved Retinanet Algorithm

Assessing Pineapple Maturity in Complex Scenarios Using an Improved Retinanet Algorithm

Application of Convolutional Neural Networks in Weed Detection and Identification: A Systematic Review

Application of Convolutional Neural Networks in Weed Detection and Identification: A Systematic Review.

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