Image Segmentation Model of Pear Leaf Diseases Based on Mask R-CNN

Mu, Wenqian; Jia, Zhida; Liu, Yuetian; Xu, Wenshu; Liu, Yongjie

doi:10.1109/icipmc55686.2022.00016

Cited by 5 publications

(2 citation statements)

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“…Several researches have focused on combining deep learning algorithms with machine learning algorithms to address and improve the accuracy of the classification problem, for example, MobileNetv2 and NASNetMobile that were used to extract features from leaves and those features were combined with classification networks such as random forest, support vector machines and multinomial logistic regression [51]. Other works have applied algorithms such as YOLOv3 [45], Faster R-CNN [52,53] and Mask R-CNN [54,55] to detect disease states in plants.…”

Section: Related Workmentioning

confidence: 99%

Monitoring Tomato Leaf Disease through Convolutional Neural Networks

Ibáñez

Reyes-Muñoz

2023

Electronics

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Agriculture plays an essential role in Mexico’s economy. The agricultural sector has a 2.5% share of Mexico’s gross domestic product. Specifically, tomatoes have become the country’s most exported agricultural product. That is why there is an increasing need to improve crop yields. One of the elements that can considerably affect crop productivity is diseases caused by agents such as bacteria, fungi, and viruses. However, the process of disease identification can be costly and, in many cases, time-consuming. Deep learning techniques have begun to be applied in the process of plant disease identification with promising results. In this paper, we propose a model based on convolutional neural networks to identify and classify tomato leaf diseases using a public dataset and complementing it with other photographs taken in the fields of the country. To avoid overfitting, generative adversarial networks were used to generate samples with the same characteristics as the training data. The results show that the proposed model achieves a high performance in the process of detection and classification of diseases in tomato leaves: the accuracy achieved is greater than 99% in both the training dataset and the test dataset.

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Section: Related Workmentioning

confidence: 99%

Monitoring Tomato Leaf Disease through Convolutional Neural Networks

Ibáñez

Reyes-Muñoz

2023

Electronics

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“…These frameworks are extremely flexible, well‐supported, and surprisingly approachable. As a result, many recent projects have also coalesced around these two frameworks with great success, including efforts to segment leaves (Younis et al, 2020; Triki et al, 2020, 2021; Guo et al, 2021; Hussein et al, 2021b; Gu et al, 2022; Ott and Lautenschlager, 2022), segment plant tissue (Love et al, 2021; Goëau et al, 2022; Milleville et al, 2023), isolate plant organs (Davis et al, 2020; Pearson et al, 2020; Triki et al, 2020; Ott and Lautenschlager, 2022), extract specimen label data (Milleville et al, 2023), isolate diseased or damaged leaf tissue (Kaur et al, 2022; Mu et al, 2022; Kavitha Lakshmi and Savarimuthu, 2023), measure bird skeletons (Weeks et al, 2023), isolate preserved snakes (Curlis et al, 2022), segment fossils (Panigrahi et al, 2022), or remotely monitor phenology (Mann et al, 2022). However, rather than relying on a single machine learning architecture to extract trait and archival data from specimens, we developed a modular framework of seven different machine learning algorithms that work in tandem to comprehensively process each image (Table 2, Figure 1).…”

Section: Term Definitionmentioning

confidence: 99%

From leaves to labels: Building modular machine learning networks for rapid herbarium specimen analysis with LeafMachine2

Weaver,

Smith

2023

Appl Plant Sci

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PremiseQuantitative plant traits play a crucial role in biological research. However, traditional methods for measuring plant morphology are time consuming and have limited scalability. We present LeafMachine2, a suite of modular machine learning and computer vision tools that can automatically extract a base set of leaf traits from digital plant data sets.MethodsLeafMachine2 was trained on 494,766 manually prepared annotations from 5648 herbarium images obtained from 288 institutions and representing 2663 species; it employs a set of plant component detection and segmentation algorithms to isolate individual leaves, petioles, fruits, flowers, wood samples, buds, and roots. Our landmarking network automatically identifies and measures nine pseudo‐landmarks that occur on most broadleaf taxa. Text labels and barcodes are automatically identified by an archival component detector and are prepared for optical character recognition methods or natural language processing algorithms.ResultsLeafMachine2 can extract trait data from at least 245 angiosperm families and calculate pixel‐to‐metric conversion factors for 26 commonly used ruler types.DiscussionLeafMachine2 is a highly efficient tool for generating large quantities of plant trait data, even from occluded or overlapping leaves, field images, and non‐archival data sets. Our project, along with similar initiatives, has made significant progress in removing the bottleneck in plant trait data acquisition from herbarium specimens and shifted the focus toward the crucial task of data revision and quality control.

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