Detection of Key Organs in Tomato Based on Deep Migration Learning in Complex Background

Sun, Jun; He, Xiaofei; Ge, Xiao Lan; Wu, Xiaohong; Shen, Jifeng; Song, Yingying

doi:10.20944/preprints201810.0524.v1

Cited by 32 publications

(9 citation statements)

References 17 publications

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“…There are different metrics to measure the accuracy and effectiveness in object detection models. In this study, we used mAP which is one of the widely used metrics in the literature [33,34], especially for detection. Additionally, for each best model, a confusion matrix was generated.…”

Section: Performance Metrics and Validation Of Developed Modelsmentioning

confidence: 99%

AI-powered banana diseases and pest detection

et al. 2019

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Background: Banana (Musa spp.) is the most popular marketable fruit crop grown all over the world, and a dominant staple food in many developing countries. Worldwide, banana production is affected by numerous diseases and pests. Novel and rapid methods for the timely detection of pests and diseases will allow to surveil and develop control measures with greater efficiency. As deep convolutional neural networks (DCNN) and transfer learning has been successfully applied in various fields, it has freshly moved in the domain of just-in-time crop disease detection. The aim of this research is to develop an AI-based banana disease and pest detection system using a DCNN to support banana farmers. Results: Large datasets of expert pre-screened banana disease and pest symptom/damage images were collected from various hotspots in Africa and Southern India. To build a detection model, we retrained three different convolutional neural network (CNN) architectures using a transfer learning approach. A total of six different models were developed from 18 different classes (disease by plant parts) using images collected from different parts of the banana plant. Our studies revealed ResNet50 and InceptionV2 based models performed better compared to MobileNetV1. These architectures represent the state-of-the-art results of banana diseases and pest detection with an accuracy of more than 90% in most of the models tested. These experimental results were comparable with other state-of-the-art models found in the literature. With a future view to run these detection capabilities on a mobile device, we evaluated the performance of SSD (single shot detector) MobileNetV1. Performance and validation metrics were also computed to measure the accuracy of different models in automated disease detection methods. Conclusion: Our results showed that the DCNN was a robust and easily deployable strategy for digital banana disease and pest detection. Using a pre-trained disease recognition model, we were able to perform deep transfer learning (DTL) to produce a network that can make accurate predictions. This significant high success rate makes the model a useful early disease and pest detection tool, and this research could be further extended to develop a fully automated mobile app to help millions of banana farmers in developing countries.

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Section: Performance Metrics and Validation Of Developed Modelsmentioning

confidence: 99%

AI-powered banana diseases and pest detection

et al. 2019

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“…Focusing on the detection of key organs of tomatoes using CNN architectures, the work of (Sun et al, 2018) introduces a dataset of over 5,000 images with annotated objects of tomato flowers, immature green and mature red tomatoes. The images are mostly close-up shots and were collected with high definition camera on greenhouse in different times and light conditions.…”

Section: Regarding Classification and Detection Tasks For Precisionmentioning

confidence: 99%

Tomatod: Evaluation of Object Detection Algorithms on a New Real-World Tomato Dataset

Tsironis¹,

Bourou²,

Stentoumis³

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The integration of modern technologies in farming poses a challenging task to the research community. In this work, the task of selective cropping and treating is considered, whereas learning algorithms can provide essential assistance on crop growth and disease prediction, species recognition and fruit detection. In this paper, we introduce a highly specialized object detection (OD) and classification dataset of tomato fruits that contains class information for the ripening stage of each tomato fruit apart from its corresponding bounding box. With this dataset we aim to encourage the development of task-specific production ready object detection algorithms, as well as to evaluate and provide a baseline result of common state-of-the-art generic OD algorithms. In detail, a thorough presentation of the most common OD datasets takes place, where we discuss both generic OD and some highly specialized datasets. Our dataset contains more than 250 images and 2400 annotations in total. The dataset contains class information for three ripening stages of a tomato fruit provided by expert agriculturists, while providing views consistent with the targeted real-world use case scenario. Compared to other OD datasets our proposition differs in core areas such as the quality of the annotations, the object size distribution and the public availability. Evaluating the performance in our dataset for six object detection models we draw conclusions about the strength and weaknesses of each one’s performance. Finally, we present a future roadmap of revisions and discuss some future research topics that could improve the performance of OD algorithms in our dataset.

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“…Faster R-CNN networks have shown state of the art performance in various object detection applications and competitions [43]. Therefore many researchers have explored the use of Faster R-CNN for detecting various plant organs like flowers, fruits and seedlings [28,30,9,20,31,1,13]. To our knowledge this is the first time object detection has been used for detecting multiple types of plant organs on herbarium scans.…”

Section: Introductionmentioning

confidence: 99%

Detection and Annotation of Plant Organs from Digitized Herbarium Scans using Deep Learning

Younis,

Schmidt,

Weiland

et al. 2020

Preprint

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As herbarium specimens are increasingly becoming digitized and accessible in online repositories, advanced computer vision techniques are being used to extract information from them. The presence of certain plant organs on herbarium sheets is useful information in various scientific contexts and automatic recognition of these organs will help mobilize such information. In our study we use deep learning to detect plant organs on digitized herbarium specimens with Faster R-CNN. For our experiment we manually annotated hundreds of herbarium scans with thousands of bounding boxes for six types of plant organs and used them for training and evaluating the plant organ detection model. The model worked particularly well on leaves and stems, while flowers were also present in large numbers in the sheets, but not equally well recognized.

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Detection of Key Organs in Tomato Based on Deep Migration Learning in Complex Background

Cited by 32 publications

References 17 publications

AI-powered banana diseases and pest detection

AI-powered banana diseases and pest detection

Tomatod: Evaluation of Object Detection Algorithms on a New Real-World Tomato Dataset

Detection and Annotation of Plant Organs from Digitized Herbarium Scans using Deep Learning

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