Trends in vision-based machine learning techniques for plant disease identification: A systematic review

Thakur, Poornima Singh; Khanna, Pritee; Sheorey, Tanuja; Ojha, Aparajita

doi:10.1016/j.eswa.2022.118117

Cited by 87 publications

(46 citation statements)

References 157 publications

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“…Similarly, overcrowded trees and leaves hinder symptoms and when these symptoms become noticeable it becomes difficult to take any precautions or to treat Scab. These reasons pose challenges in Scab identification and treatment; therefore, early Scab diagnosis is essential through precise and automatic disease identification [32]. The agriculture industry is adopting sensors, advanced AI tools, and information perception systems for achieving images and information to make precise decisions for the prevention, identification, and classification of diseases.…”

Section: Early Detection Methodsmentioning

confidence: 99%

A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

Ali

Yang

Israr

et al. 2023

Drones

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Disease detection in plants is essential for food security and economic stability. Unmanned aerial vehicle (UAV) imagery and artificial intelligence (AI) are valuable tools for it. The purpose of this review is to gather several methods used by our peers recently, hoping to provide some knowledge and assistance for researchers and farmers so that they can employ these technologies more advantageously. The studies reviewed in this paper focused on Scab detection in Rosaceae family fruits. Feature extraction, segmentation, and classification methods for processing the UAV-obtained images and detecting the diseases are discussed briefly. The advantages and limitations of diverse kinds of UAVs and imaging sensors are also explained. The widely applied methods for image analysis are machine learning (ML)-based models, and the extensively used UAV platforms are rotary-wing UAVs. Recent technologies that cope with challenges related to disease detection using UAV imagery are also detailed in this paper. Some challenging issues such as higher costs, limited batteries and flying time, huge and complex data, low resolution, and noisy images, etc., still require future consideration. The prime significance of this paper is to promote automation and user-friendly technologies in Scab detection.

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Section: Early Detection Methodsmentioning

confidence: 99%

A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

Ali

Yang

Israr

et al. 2023

Drones

View full text Add to dashboard Cite

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“…In agricultural production, various crop diseases and pests occur frequently and show the characteristics of heavy disasters and great harm, which brings huge losses to agricultural production and poses a serious boost to food security. , The use of pesticides is the most economical and effective means to improve grain yield . If pesticides are not used, the world’s average annual losses caused by diseases, insects, and grass pests account for about 37% of crop yields, and the annual economic losses are as high as 126 billion dollars .…”

Section: Introductionmentioning

confidence: 99%

Novel Cyclopenta[c]pyridine Derivatives Based on Natural Cerbinal as Potential Agrochemical Anti-TMV Agents and Insecticides

Li,

Xu,

Zou

et al. 2024

J. Agric. Food Chem.

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Based on natural cerbinal, a series of novel 4-bit modified cyclopenta[c]pyridine derivatives containing a substituted amide or ester moiety were designed and synthesized for the first time. Their structures were systematically characterized by NMR and high-resolution mass spectra (HRMS). The anti-TMV activities, such as protection, inactivation, and curative effects in vivo, were evaluated methodically. The lethal activities of the target compounds against the agriculturally common pests Plutella xylostella larvae and Aphis laburni kaltenbach were evaluated by the immersion method. The bioassay results indicated that most of the target compounds exhibited good to excellent anti-TMV activity levels, good lethal activity against P. xylostella larvae at 600 μg/mL, and greater insecticidal activities against A. laburni Kaltenbach compared to the plant-derived insecticide rotenone. The binding mode of cerbinal and cyclopenta[c]pyridine derivatives 4b, 4p, and 4v with the TMV protein was studied with a molecular docking method, which indicated that the functional group of the 2-and 4-positions is vital for anti-TMV activity. The systematic research provides strong evidence that these novel 4-bit modified cyclopenta[c]pyridine derivatives could become potential agrochemical insecticides and anti-TMV agents.

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“…Most previous research has concentrated on studying pests on plant leaves, with relatively fewer investigations in broader pest recognition domains. The continuous advancement of precision agriculture and smart farming has brought attention to the promising integration of deep learning techniques in agriculture 11 . However, the agricultural landscape is diverse, giving rise to various types of data sources 12 .…”

Section: Introductionmentioning

confidence: 99%

ITFNet-API: Image and Text Based Multi-Scale Cross-Modal Feature Fusion Network for Agricultural Pest Identification

Yu,

Xu,

Zhang

et al. 2023

Preprint

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In agricultural production, pest problems are inevitable. In recent years, China has suffered annual losses of up to 40 million tons of grain due to various pests and diseases. There are over one million known insect species in the natural world, exhibiting complex and diverse morphologies, making manual identification costly. With the advancement of deep learning technologies, methods relying solely on image data for crop pest identification have achieved some success. However, these methods heavily depend on numerous high-quality annotated images. Many existing approaches overlook the role and value of other modalities beyond images and rely solely on low-level image features for recognition. They fail to fully exploit semantic correlations between multimodal data, leading to poor reliability and interpretability of the recognition results. Moreover, in real-world scenarios, pest habitats are complex and diverse, with varying scales, imposing high demands on the practical application of models. To address these challenges, this paper proposes a Multi-Scale Cross-Modal Feature Fusion Model (ITFNet-API) utilizing multi-scale visual features and textual features for cross-modal attention learning. Super-resolution reconstruction techniques are introduced to restore high-frequency information in low-quality images. To avoid altering low-frequency information during the reconstruction process, the reconstructed high-quality images are combined with the original images and fed into the model for feature learning to enrich pest features. The proposed Image and Text Fusion Module (ITFM) performs multi-scale cross-modal information fusion and an Inverse Transpose Convolution (ITC) network module is introduced to restore multi-scale visual features. The obtained visual features are input into the neck and head networks of the YOLOv4 (You Only Look Once version 4) model to obtain pest category and location information in each image. Finally, to verify the model’s practical application capability, this paper introduces an Arbitrary Combination Image Enhancement (ACIE) data augmentation method which generates a batch of complex multi-target, multi-scale pest images in challenging scenarios for data augmentation and model training. We conducted extensive comparative experiments based on the IP102 dataset to validate the effectiveness of the proposed method. The experimental results indicate that our method achieved an accuracy rate of 82.15% in pest recognition, outperforming other advanced methods significantly with an AP50 of 92.18%. Moreover, in multi-target recognition,the accuracy rate reached 45.24% with an AP50 of 43.04%.

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Trends in vision-based machine learning techniques for plant disease identification: A systematic review

Cited by 87 publications

References 157 publications

A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

Novel Cyclopenta[c]pyridine Derivatives Based on Natural Cerbinal as Potential Agrochemical Anti-TMV Agents and Insecticides

ITFNet-API: Image and Text Based Multi-Scale Cross-Modal Feature Fusion Network for Agricultural Pest Identification

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