Construction of Deep Learning-Based Disease Detection Model in Plants

Jung, Minah; Song, Jong Seob; Shin, Ah-Young; Choi, Beomjo; Go, Sangjin; Kwon, Suk‐Yoon; Park, Juhan; Park, Sung Goo; Kim, Yongmin

doi:10.21203/rs.3.rs-1939712/v1

Cited by 4 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CNNs have previously demonstrated utility in plant sciences, with applications including segmentation of overlapping field plants in maize (25), soybean stress (26), and disease detection in bell pepper, potato, and tomato (27), wheat (28), and within the PlantVillage data set, which includes 39 classes of plant leaves with varying diseases (29). Ubbens and Stavness (30) demonstrated an early application of neural networks for leaf counting, classifying mutants, and plant age using primarily the International Plant Phenotyping Network (IPPN) phenotyping data set (31).…”

Section: Deep Learning For Plant Phenotypingmentioning

confidence: 99%

Cotton Chronology: Convolutional Neural Network Enables Single-Plant Senescence Scoring with Temporal Drone Images

DeSalvio,

Arik,

Murray

et al. 2024

Preprint

View full text Add to dashboard Cite

Senescence is a degenerative biological process that affects most organisms. Timing of senescence is critical for annual and perennial crops and is associated with yield and quality. Tracking time-series senescence data has previously required expert annotation and can be laborious for large-scale research. Here, a convolutional neural network (CNN) was trained on unoccupied aerial system (UAS, drone) images of individual plants of cotton (Gossypium hirsutum L.), an early application of single-plant analysis (SPA). Using images from 14 UAS flights capturing most of the senescence window, the CNN achieved 71.4% overall classification accuracy across six senescence categories, with class accuracies ranging between 46.8–89.4% despite large imbalances in numbers of images across classes. For example, the number of images ranged from 109 to 1,129 for the lowest-performing class (80% senesced) to the highest-performing class (fully healthy). The results demonstrate that minimally pre-processed UAS images can enable translatable implementations of high-throughput phenotyping using deep learning methods. This has applications for understanding fundamental plant biology, monitoring orchards and other spaced plantings, plant breeding, and genetic research.

show abstract

Section: Deep Learning For Plant Phenotypingmentioning

confidence: 99%

Cotton Chronology: Convolutional Neural Network Enables Single-Plant Senescence Scoring with Temporal Drone Images

DeSalvio,

Arik,

Murray

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…CNN applications in plant sciences include segmentation of overlapping field plants in maize (Guo et al, 2023), classification of soybean stress (Ghosal et al, 2018), and disease detection in bell pepper, potato, and tomato (Jung et al, 2023), wheat (Nigus et al, 2023), and across the PlantVillage data set, which includes 39 classes of plant leaves with varying diseases (Mohanty et al, 2016). Ubbens and Stavness (2017) demonstrated an early application of neural networks for leaf counting, classifying mutants, and plant age using primarily the International Plant Phenotyping Network Arabidopsis phenotyping data set (Minervini et al, 2014).…”

Section: Deep Learning For Plant Phenotypingmentioning

confidence: 99%

Temporal Image Sandwiches Enable Link between Functional Data Analysis and Deep Learning for Single-Plant Cotton Senescence

DeSalvio,

Adak,

Arik

et al. 2024

Preprint

View full text Add to dashboard Cite

SummarySenescence is a highly ordered degenerative biological process that affects yield and quality in annuals and perennials. Images from 14 unoccupied aerial system (UAS, UAV, drone) flights captured the senescence window across two experiments while functional principal component analysis (FPCA) effectively reduced the dimensionality of temporal visual senescence ratings (VSRs) and two vegetation indices: RCC and TNDGR.Convolutional neural networks (CNNs) trained on temporally concatenated, or “sandwiched,” UAS images of individual cotton plants (Gossypium hirsutumL.), allowed single-plant analysis (SPA). The first functional principal component scores (FPC1) served as the regression target across six CNN models (M1-M6).Model performance was strongest for FPC1 scores from VSR (R2= 0.857 and 0.886 for M1 and M4), strong for TNDGR (R2= 0.743 and 0.745 for M3 and M6), and strong-to- moderate for RCC (R2= 0.619 and 0.435 for M2 and M5), with deep learning attention of each model confirmed by activation of plant pixels within saliency maps.Single-plant UAS image analysis across time enabled translatable implementations of high-throughput phenotyping by linking deep learning with functional data analysis (FDA). This has applications for fundamental plant biology, monitoring orchards or other spaced plantings, plant breeding, and genetic research.

show abstract

“…The authors developed an Android application named Plantscape based on MobileNet for monitoring plant health and reported an accuracy of 95.94%. Jung et al (2023) developed a stepwise disease detection framework using a CNN to automate plant disease detection for better quality and yield. The model achieved a good accuracy of 97.09%.…”

Section: Introductionmentioning

confidence: 99%

Plant disease detection using leaf images and an involutional neural network

Pradhan,

Kumar,

Kumar

et al. 2024

ECJ

View full text Add to dashboard Cite

The human population and domestic animals rely heavily on agriculture for their food and livelihood. Agriculture is an important contributor to the national economy of many countries. Plant diseases lead to a significant reduction in agricultural yield, posing a threat to global food security. It is crucial to detect plant diseases in a timely manner to prevent economic losses. Expert diagnosis and pathogen analysis are widely used for the detection of diseases in plants. However, both expert diagnosis and pathogen analysis rely on the real-time investigation experience of experts, which is prone to errors. In this work, an image analysis-based method is proposed for detecting and classifying plant diseases using an involution neural network and self-attention-based model. This method uses digital images of plant leaves and identifies diseases on the basis of image features. Different diseases affect leaf characteristics in different ways; therefore, their visual patterns are highly useful in disease recognition. For rigorous evaluation of the method, leaf images of different crops, including apple, grape, peach, cherry, corn, pepper, potato, and strawberry, are taken from a publicly available PlantVillage dataset to train the developed model. The experiments are not performed separately for different crops; instead, the model is trained to work for multiple crops. The experimental results demonstrate that the proposed method performed well, with an average classification accuracy of approximately 98.73% (κ = 98.04) for 8 different crops with 23 classes. The results are also compared with those of several existing methods, and it is found that the proposed method outperforms the other methods considered in this work.

show abstract

Construction of Deep Learning-Based Disease Detection Model in Plants

Cited by 4 publications

References 19 publications

Cotton Chronology: Convolutional Neural Network Enables Single-Plant Senescence Scoring with Temporal Drone Images

Cotton Chronology: Convolutional Neural Network Enables Single-Plant Senescence Scoring with Temporal Drone Images

Temporal Image Sandwiches Enable Link between Functional Data Analysis and Deep Learning for Single-Plant Cotton Senescence

Plant disease detection using leaf images and an involutional neural network

Contact Info

Product

Resources

About