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1. DS-DETR: A Model for Tomato Leaf Disease Segmentation and Damage Evaluation

Author:
Jianshuang Wu; Changji Wen; Hongrui Chen; Zhenyu Ma; Tian Zhang; Hengqiang Su; Ce Yang
Source:
Agronomy 2022 v.12 no.9 pp. -
ISSN:
2073-4395
Subject:
foliar diseases,   etc   ; Phytophthora infestans; agronomy; blight; data collection; models; tomatoes;     Show all 7 Subjects
Abstract:
... Early blight and late blight are important factors restricting tomato yield. However, it is still a challenge to accurately and objectively detect and segment crop diseases in order to evaluate disease damage. In this paper, the Disease Segmentation Detection Transformer (DS-DETR) is proposed to segment leaf disease spots efficiently based on several improvements to DETR. Additionally, a damage as ...
DOI:
10.3390/agronomy12092023

2. Lightweight dense-scale network (LDSNet) for corn leaf disease identification

Author:
Weihui Zeng; Haidong Li; Gensheng Hu; Dong Liang
Source:
Computers and electronics in agriculture 2022 v.197 pp. 106943
ISSN:
0168-1699
Subject:
foliar diseases,   etc   ; agriculture; corn; data collection; electronics; leaves; mobile telephones; models;     Show all 8 Subjects
Abstract:
... The identification of corn leaf diseases in real scenarios faces important challenges, such as complex background interference, intra- and inter-class scale changes, and lightweight model deployment. To overcome these challenges, we propose a lightweight dense-scale network (LDSNet) for real-world corn leaf disease image identification. The main component of LDSNet is the improved dense dilated co ...
DOI:
10.1016/j.compag.2022.106943

3. Self-Supervised Clustering for Leaf Disease Identification

Author:
Muhammad Mostafa Monowar; Md. Abdul Hamid; Faris A. Kateb; Abu Quwsar Ohi; M. F. Mridha
Source:
Agriculture (Basel) 2022 v.12 no.6 pp. -
ISSN:
2077-0472
Subject:
foliar diseases,   etc   ; agriculture; algorithms; computer vision; fruits; humans; leaves; neural networks;     Show all 8 Subjects
Abstract:
... Plant diseases have been one of the most threatening scenarios to farmers. Although most plant diseases can be identified by observing leaves, it often requires human expertise. The recent improvements in computer vision have led to introduce disease classification systems through observing leaf images. Nevertheless, most disease classification systems are specific to diseases and plants, limiting ...
DOI:
10.3390/agriculture12060814

4. Practical cucumber leaf disease recognition using improved Swin Transformer and small sample size

Author:
Fengyi Wang; Yuan Rao; Qing Luo; Xiu Jin; Zhaohui Jiang; Wu Zhang; Shaowen Li
Source:
Computers and electronics in agriculture 2022 v.199 pp. 107163
ISSN:
0168-1699
Subject:
foliar diseases,   etc   ; agriculture; cucumbers; data collection; electronics; leaves; neural networks; sample size;     Show all 8 Subjects
Abstract:
... The deep learning methods based on convolutional neural network (CNN) have been widely explored in dataset augmentation and recognition of plant leaf diseases. The recently developed transformer-based models such as Swin Transformer (SwinT) show competitive and even better performance on various visual benchmarks compared with CNN due to their inherent attention mechanism and ability to learn long ...
DOI:
10.1016/j.compag.2022.107163

5. Registration of two peanut recombinant inbred lines (TifGP‐5 and TifGP‐6) resistant to late leaf spot disease

Author:
Ye Chu; Josh P. Clevenger; C. Corley Holbrook; Thomas G. Isleib; Peggy Ozias‐Akins
Source:
Journal of plant registrations 2022 v.16 no.3 pp. 635-640
ISSN:
1936-5209
Subject:
foliar diseases,   etc   ; disease resistance; leaf spot; oleic acid; oxidative stability; peanuts; supply balance;     Show all 7 Subjects
Abstract:
... Late leaf spot (LLS) disease is an omnipresent peanut (Arachis hypogaea L.) foliar disease that causes significant yield loss. Integrating host resistance to reduce yield loss and management costs from this disease is highly desirable. In addition to disease resistance, market demand for high‐oleic peanut is on the rise due to its improved oxidative stability and health benefits. Previously, a rec ...
Handle:
10113/7907691
DOI:
10.1002/plr2.20242

6. Characterisation of phytoplasma associated with sugarcane white leaf disease in Uttar Pradesh, India

Author:
Nishtha Mishra; Surya Prakash Dwivedi; Ajay Kumar Tiwari; Malyaj R. Prajapati; Jitender Singh
Source:
Archiv für Phytopathologie und Pflanzenschutz 2022 v.55 no.1 pp. 19-27
ISSN:
1477-2906
Subject:
foliar diseases,   etc   ; Phytoplasma; farmers; leaves; phylogeny; phytoplasmal diseases; sequence analysis; sugarcane; surveys; India;     Show all 10 Subjects
Abstract:
... During the survey of six districts of Uttar Pradesh in 2013–2017, white leaf, grassy appearance, green grassy with no cane formation, leaf yellows, stunted growth were observed on sugarcane grown on farmer’s field. Two symptomatic from each surveyed location along with one non symptomatic canes were analysed through nested 16S rRNA primers (P1/P7 and R16RF2n/R16R2). All the symptomatic leaves were ...
DOI:
10.1080/03235408.2021.1987115

7. AFD-Net: Apple Foliar Disease multi classification using deep learning on plant pathology dataset

Author:
Anju Yadav; Udit Thakur; Rahul Saxena; Vipin Pal; Vikrant Bhateja; Jerry Chun-Wei Lin
Source:
Plant and soil 2022 v.477 no.1-2 pp. 595-611
ISSN:
0032-079X
Subject:
foliar diseases,   etc   ; apples; computer vision; data collection; disease control; leaves; models; plant pathology; soil;     Show all 9 Subjects
Abstract:
... BACKGROUND: Plant diseases significantly affect the crop, so their identification is very important. Correct identification of these diseases is crucial for establishing a good disease control strategy to avoid time and financial losses. In general, machines can greatly reduce the possibility of human error. In particular, computer vision techniques developed through deep learning have paved a way ...
DOI:
10.1007/s11104-022-05407-3

8. Comparative study on the performance of deep learning implementation in the edge computing: Case study on the plant leaf disease identification

Author:
Soo Jun Wei; Dimas Firmanda Al Riza; Hermawan Nugroho
Source:
Journal of agriculture and food research 2022 v.10 pp. 100389
ISSN:
2666-1543
Subject:
foliar diseases,   etc   ; agriculture; case studies; comparative study; computer software; data collection; food research; models;     Show all 8 Subjects
Abstract:
... This research examines and explores four different pre-trained CNN deep learning models (AlexNet, VGG16, ResNet50, and DenseNet121) to be adopted as edge solution. The model is developed and evaluated using the PlantVillage dataset. Image transformation techniques and down sampling were carried out to mitigate the unbalanced class distribution problem. From the preliminary works, DenseNet121 was s ...
DOI:
10.1016/j.jafr.2022.100389

9. Evaluation of Fungicides and Application Strategies for the Management of the Red Leaf Blotch Disease of Almond

Author:
Laura Torguet; Lourdes Zazurca; Guillem Martínez; Gemma Pons-Solé; Jordi Luque; Xavier Miarnau
Source:
Horticulturae 2022 v.8 no.6 pp. -
ISSN:
2311-7524
Subject:
foliar diseases,   etc   ; Polystigma; almonds; boscalid; fluopyram; leaf blotch; pyraclostrobin; temperature; trees; trifloxystrobin; Mediterranean region; Spain;     Show all 12 Subjects
Abstract:
... Red leaf blotch (RLB) of almond, caused by Polystigma amygdalinum, is an important foliar disease of this nut tree in the Mediterranean basin and especially in Spain. In recent years, the control of this disease has become a key factor in the management of Spanish almond orchards. The management of RLB is not easy due to intrinsic factors of the disease (e.g., long infection and latency periods) a ...
DOI:
10.3390/horticulturae8060501

10. Enhanced radial basis function neural network for tomato plant disease leaf image segmentation

Author:
Manoj A. Patil; Manohar M
Source:
Ecological informatics 2022 v.70 pp. 101752
ISSN:
1574-9541
Subject:
foliar diseases,   etc   ; Helianthus annuus; algorithms; color; computer vision; farmers; image analysis; leaves; models; prediction; tomatoes;     Show all 11 Subjects
Abstract:
... Primary crop losses in agriculture are due to leaf diseases, which farmers cannot identify early. If the diseases are not detected early and correctly, then the farmer will have to undergo huge losses. Therefore, in the field of agriculture, the detection of leaf diseases in tomato crops plays a vital role. Recent advances in computer vision and deep learning techniques have made disease predictio ...
DOI:
10.1016/j.ecoinf.2022.101752

11. Identification of ‘Candidatus Phytoplasma aurantifolia’ in pigeon pea with little leaf disease in Karnataka, India

Author:
Gangadhara B. Naik; Suresh Patil; S. Bhavana; Manjunath Krishnappa Naik
Source:
Phytopathogenic Mollicutes 2022 v.12 no.1 pp. 38-41
ISSN:
2249-4677
Subject:
foliar diseases,   etc   ; Phytoplasma aurantifolia; brooms; databases; hemp; leaves; nucleic acids; pigeon peas; polymerase chain reaction; India;     Show all 10 Subjects
Abstract:
... Pigeon pea is an important pulse crop grown in tropical regions and mainly cultivated in semi-arid regions of India. Phytoplasma little leaf symptoms were observed on BGR-1 variety of pigeon pea in an experimental plot in Karnataka. The affected leaf samples were subjected to nucleic acid extraction for PCR amplification of phytoplasma 16S rDNA by nested PCR. The obtained product was sequenced and ...
DOI:
10.5958/2249-4677.2022.00007.X

12. Infection Characteristics and Physical Prevention Strategy of Panax notoginseng Round Spot Disease Caused by Mycocentrospora acerina

Author:
K. Yang; H. L. Wang; C. Ye; Z. H. Wang; K. H. Ye; S. Zhang; H. P. Huang; Z. X. Wei; S. S. Zhu; Y. Y. Zhu; L. W. Guo; X. H. He
Source:
Plant disease 2022 v.106 no.10 pp. 2607-2617
ISSN:
0191-2917
Subject:
foliar diseases,   etc   ; Mycocentrospora acerina; Panax notoginseng; conidia; control methods; germination; leaf wetness; leaves; rain; temperature; transfection;     Show all 11 Subjects
Abstract:
... Panax notoginseng round spot disease (PRSD), caused by Mycocentrospora acerina, is the main leaf disease occurring in cultured P. notoginseng. Aiming to find a safe and efficient control method for PRSD, we studied the disease characteristics of PRSD and the optimal growth conditions of M. acerina and evaluated the efficacy of rain-shelter cultivation in PRSD control. Moreover, we described M. ace ...
DOI:
10.1094/PDIS-01-22-0087-RE

13. Registration of TifGP‐3 and TifGP‐4 peanut germplasm lines

Author:
C. Corley Holbrook; Peggy Ozias‐Akins; Ye Chu; Samuele Lamon; David J. Bertioli; Soraya C. M. Leal‐Bertioli; Albert K. Culbreath; Ignacio Godoy
Source:
Journal of plant registrations 2022 v.16 no.1 pp. 120-123
ISSN:
1936-5209
Subject:
foliar diseases,   etc   ; Arachis hypogaea; chromosomes; cultivars; diploidy; genotyping; germplasm; introgression; leaf spot; marker-assisted selection; peanuts;     Show all 11 Subjects
Abstract:
... Late leaf spot (LLS) disease caused by Nothopassalora personata (Berk. & M.A. Curtis) U. Braun, C. Nakash, Videira & Crous is a foliar disease that plagues peanut (Arachis hypogaea L.) production worldwide. One effective solution to control this disease would be the development of resistant cultivars. IAC 322 is a breeding line resistant to LLS due to alien introgressions from A. cardenasii Krapov ...
DOI:
10.1002/plr2.20179

14. Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model

Author:
Ghazanfar Latif; Sherif E. Abdelhamid; Roxane Elias Mallouhy; Jaafar Alghazo; Zafar Abbas Kazimi
Source:
Plants 2022 v.11 no.17 pp. -
ISSN:
2223-7747
Subject:
foliar diseases,   etc   ; Internet; crop losses; data collection; leaf blight; leaves; neural networks; prices; rice; scald diseases;     Show all 10 Subjects
Abstract:
... Rice is considered one the most important plants globally because it is a source of food for over half the world’s population. Like other plants, rice is susceptible to diseases that may affect the quantity and quality of produce. It sometimes results in anywhere between 20–40% crop loss production. Early detection of these diseases can positively affect the harvest, and thus farmers would have to ...
DOI:
10.3390/plants11172230

15. A Conserved Hypothetical Gene Is Required but Not Sufficient for Ptr ToxC Production in Pyrenophora tritici-repentis

Author:
Gongjun Shi; Gayan Kariyawasam; Sanzhen Liu; Yueqiang Leng; Shaobin Zhong; Shaukat Ali; Paula Moolhuijzen; Caroline S. Moffat; Jack B. Rasmussen; Timothy L. Friesen; Justin D. Faris; Zhaohui Liu
Source:
Molecular plant-microbe interactions 2022 v.35 no.4 pp. 336-348
ISSN:
1943-7706
Subject:
foliar diseases,   etc   ; Pyrenophora tritici-repentis; biochemical pathways; chlorosis; fungi; genes; haplotypes; loci; necrosis; pathogens; vacuoles; wheat;     Show all 12 Subjects
Abstract:
... The fungus Pyrenophora tritici-repentis causes tan spot, an important foliar disease of wheat worldwide. The fungal pathogen produces three necrotrophic effectors, namely Ptr ToxA, Ptr ToxB, and Ptr ToxC to induce necrosis or chlorosis in wheat. Both Ptr ToxA and Ptr ToxB are proteins, and their encoding genes have been cloned. Ptr ToxC was characterized as a low–molecular weight molecule 20 years ...
DOI:
10.1094/MPMI-12-21-0299-R

16. A Lightweight Attention-Based Convolutional Neural Networks for Tomato Leaf Disease Classification

Author:
Anil Bhujel; Na-Eun Kim; Elanchezhian Arulmozhi; Jayanta Kumar Basak; Hyeon-Tae Kim
Source:
Agriculture (Basel) 2022 v.12 no.2 pp. -
ISSN:
2077-0472
Subject:
foliar diseases,   etc   ; agriculture; computer vision; data collection; disease detection; food production; neural networks; plant pathology; tomatoes;     Show all 9 Subjects
Abstract:
... Plant diseases pose a significant challenge for food production and safety. Therefore, it is indispensable to correctly identify plant diseases for timely intervention to protect crops from massive losses. The application of computer vision technology in phytopathology has increased exponentially due to automatic and accurate disease detection capability. However, a deep convolutional neural netwo ...
DOI:
10.3390/agriculture12020228

17. A Pilot Study of Transplanting Methods for Wilding American Beech (Fagus grandifolia)

Author:
Andrea L. Myers; Yvette L. Dickinson; Andrew J. Storer; Tara L. Bal
Source:
Horticulturae 2022 v.8 no.7 pp. -
ISSN:
2311-7524
Subject:
foliar diseases,   etc   ; Curculionidae; Fagus grandifolia; beech bark disease; climate; disease resistance; habitats; Hiawatha National Forest; Michigan;     Show all 9 Subjects
Abstract:
... American beech is facing pressure from a number of emergent health issues including beech bark disease, beech leaf disease, beech leaf mining weevil, and climate and habitat change. Interest has increased in the propagation of American beech in response to the demand for more disease-resistant American beech for use in restoration. This study describes the first steps towards publishing methods fo ...
DOI:
10.3390/horticulturae8070565

18. Deep diagnosis: A real-time apple leaf disease detection system based on deep learning

Author:
Asif Iqbal Khan; S.M.K. Quadri; Saba Banday; Junaid Latief Shah
Source:
Computers and electronics in agriculture 2022 v.198 pp. 107093
ISSN:
0168-1699
Subject:
foliar diseases,   etc   ; agriculture; apples; color; data collection; disease detection; early diagnosis; electronics; financial economics; industry; leaves; models;     Show all 12 Subjects
Abstract:
... Diseases and pests are one of the major reasons for low productivity of apples which in turn results in huge economic loss to the apple industry every year. Early detection of apple diseases can help in controlling the spread of infections and ensure better productivity. However, early diagnosis and identification of diseases is challenging due to many factors like, presence of multiple symptoms o ...
DOI:
10.1016/j.compag.2022.107093

19. Performance of Machine Learning and Image Processing in Plant Leaf Disease Detection

Author:
Abu Sarwar Zamani; L. Anand; Kantilal Pitambar Rane; P. Prabhu; Ahmed Mateen Buttar; Harikumar Pallathadka; Abhishek Raghuvanshi; Betty Nokobi Dugbakie
Source:
Journal of food quality 2022 v.2022 pp. -
ISSN:
1745-4557
Subject:
foliar diseases,   etc   ; automation; disease detection; farmers; food quality; image analysis; leaves; photography; precision agriculture; principal component analysis;     Show all 10 Subjects
Abstract:
... The aim of this study is to evaluate infected leaf disease images. Precision agriculture's automatic leaf disease detection system employs image acquisition, image processing, image segmentation, feature extraction, and machine learning techniques. An automated disease detection system offers the farmer with a fast and accurate diagnosis of the plant disease. Automation of plant leaf disease detec ...
DOI:
10.1155/2022/1598796

20. Attention-optimized DeepLab V3 + for automatic estimation of cucumber disease severity

Author:
Kaiyu Li; Lingxian Zhang; Bo Li; Shufei Li; Juncheng Ma
Source:
Plant methods 2022 v.18 no.1 pp. 109
ISSN:
1746-4811
Subject:
foliar diseases,   etc   ; artificial intelligence; cucumbers; disease control; disease severity; downy mildew; leaves; models; powdery mildew; prediction; solar radiation;     Show all 11 Subjects
Abstract:
... BACKGROUND: Automatic and accurate estimation of disease severity is critical for disease management and yield loss prediction. Conventional disease severity estimation is performed using images with simple backgrounds, which is limited in practical applications. Thus, there is an urgent need to develop a method for estimating the disease severity of plants based on leaf images captured in field c ...
DOI:
10.1186/s13007-022-00941-8