8. Multi species weed detection with Retinanet one-step network in a maize field

Corrêa, Juliane; Todeschini, Matheus Henrique; Pérez, Diego; Karouta, Jeremy; Bromberg, Facundo; Ribeiro, Ángela; Andújar, Dionisio

doi:10.3920/978-90-8686-916-9_8

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…Every 2 m, a zenithal image was taken from a height of 1.3 m. Five weed species, monocotyledonous (Cyperus rotundus L., Echinochloa crus galli L., Setaria verticillata L.) and dicotyledonous (Portulaca oleracea L., Solanum nigrum L.), were found in low growth stages, combined with crop plants (Solanum lycopersicum L.). The considered species are the most problematic tomato weeds in Spain [31]. The camera used was a Canon PowerShot SX540 HS with a spatial resolution of 5184 pixels × 3886 pixels.…”

Section: Image Acquisitionmentioning

confidence: 99%

Intelligent Weed Management Based on Object Detection Neural Networks in Tomato Crops

et al. 2022

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As the tomato (Solanum lycopersicum L.) is one of the most important crops worldwide, and the conventional approach for weed control compromises its potential productivity. Thus, the automatic detection of the most aggressive weed species is necessary to carry out selective control of them. Precision agriculture associated with computer vision is a powerful tool to deal with this issue. In recent years, advances in digital cameras and neural networks have led to novel approaches and technologies in PA. Convolutional neural networks (CNNs) have significantly improved the precision and accuracy of the process of weed detection. In order to apply on-the-spot herbicide spraying, robotic weeding, or precise mechanical weed control, it is necessary to identify crop plants and weeds. This work evaluates a novel method to automatically detect and classify, in one step, the most problematic weed species of tomato crops. The procedure is based on object detection neural networks called RetinaNet. Moreover, two current mainstream object detection models, namelyYOLOv7 and Faster-RCNN, as a one and two-step NN, respectively, were also assessed in comparison to RetinaNet. CNNs model were trained on RGB images monocotyledonous (Cyperus rotundus L., Echinochloa crus galli L., Setaria verticillata L.) and dicotyledonous (Portulaca oleracea L., Solanum nigrum L.) weeds. The prediction model was validated with images not used during the training under the mean average precision (mAP) metric. RetinaNet performed best with an AP ranging from 0.900 to 0.977, depending on the weed species. Faster-RCNN and YOLOv7 also achieved satisfactory results, in terms of mAP, particularly through data augmentation. In contrast to Faster CNN, YOLOv7 was less precise when discriminating monocot weed species. The results provide a better insight on how weed identification methods based on CNN can be made more broadly applicable for real-time applications.

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Section: Image Acquisitionmentioning

confidence: 99%

Intelligent Weed Management Based on Object Detection Neural Networks in Tomato Crops

et al. 2022

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“…Weeds pose a significant threat to agricultural crops, depriving them of essential resources like water, light, and nutrients. To reduce economic losses and decrease reliance on herbicides in weed management, some one-stage detectors have been utilized for weed control [17][18][19] . In summary, object detection algorithms hold tremendous promise in the realm of plant disease detection and prevention.…”

Section: Introductionmentioning

confidence: 99%

Deep learning approaches for object recognition in plant diseases: a review

Zhou,

Zhang,

et al. 2023

Intelligence & Robotics

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Plant diseases pose a significant threat to the economic viability of agriculture and the normal functioning of trees in forests. Accurate detection and identification of plant diseases are crucial for smart agricultural and forestry management. In recent years, the intersection of agriculture and artificial intelligence has become a popular research topic. Researchers have been experimenting with object recognition algorithms, specifically convolutional neural networks, to identify diseases in plant images. The goal is to reduce labor and improve detection efficiency. This article reviews the application of object detection methods for detecting common plant diseases, such as tomato, citrus, maize, and pine trees. It introduces various object detection models, ranging from basic to modern and sophisticated networks, and compares the innovative aspects and drawbacks of commonly used neural network models. Furthermore, the article discusses current challenges in plant disease detection and object detection methods and suggests promising directions for future work in learning-based plant disease detection systems.

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“…Besides Nguyen et al (2020), in their review paper shows that RetinaNet method demonstrates promising results for small object detection. In agriculture applications especially with imbalanced data sets, such as disease and insect classification, the RetinaNet object detector has been utilized to overcome this common challenge (Sales et al, 2021;Correa et al, 2021;Bao et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based phenotyping for genome wide association studies of sudden death syndrome in soybean

et al. 2022

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Using a reliable and accurate method to phenotype disease incidence and severity is essential to unravel the complex genetic architecture of disease resistance in plants, and to develop disease resistant cultivars. Genome-wide association studies (GWAS) involve phenotyping large numbers of accessions, and have been used for a myriad of traits. In field studies, genetic accessions are phenotyped across multiple environments and replications, which takes a significant amount of labor and resources. Deep Learning (DL) techniques can be effective for analyzing image-based tasks; thus DL methods are becoming more routine for phenotyping traits to save time and effort. This research aims to conduct GWAS on sudden death syndrome (SDS) of soybean [Glycine max L. (Merr.)] using disease severity from both visual field ratings and DL-based (using images) severity ratings collected from 473 accessions. Images were processed through a DL framework that identified soybean leaflets with SDS symptoms, and then quantified the disease severity on those leaflets into a few classes with mean Average Precision of 0.34 on unseen test data. Both visual field ratings and image-based ratings identified significant single nucleotide polymorphism (SNP) markers associated with disease resistance. These significant SNP markers are either in the proximity of previously reported candidate genes for SDS or near potentially novel candidate genes. Four previously reported SDS QTL were identified that contained a significant SNPs, from this study, from both a visual field rating and an image-based rating. The results of this study provide an exciting avenue of using DL to capture complex phenotypic traits from images to get comparable or more insightful results compared to subjective visual field phenotyping of traits for disease symptoms.

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8. Multi species weed detection with Retinanet one-step network in a maize field

Cited by 7 publications

References 17 publications

Intelligent Weed Management Based on Object Detection Neural Networks in Tomato Crops

Intelligent Weed Management Based on Object Detection Neural Networks in Tomato Crops

Deep learning approaches for object recognition in plant diseases: a review

Deep learning-based phenotyping for genome wide association studies of sudden death syndrome in soybean

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