Plant leaf disease detection and control: A survey

Joshi, Barkha M.; Bhavsar, Hetal

doi:10.1080/02522667.2020.1734295

Cited by 23 publications

(13 citation statements)

References 14 publications

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“…The early fusion gives marginally better performance. There is also a 2.2% improvement in precision for early and late fusion techniques than conventional convolutional neural network models and some existing literature models in Barbedo (2016), Barbedo (2017), Barkha and Bhavsar (2020), Chouhan et al (2020), Chuanlei et al (2017), Kaur et al (2019), Liu et al (2017),…”

Section: F I G U R E 5 Precision Early Fusion Scores T a B L E 3 Earl...mentioning

confidence: 96%

Hybrid intelligent technology for plant health using the fusion of evolutionary optimization and deep neural networks

Al-bayati

Üstündağ

2021

Expert Systems

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In the last decade, plant leaf disease identification has been an efficient research subject. In connection with this interest, deep learning architectures show a remarkable era in various fields of image processing and computer vision, including image classification, function detection, and image pattern recognition. In this study, we examine many aspects of convolutional neural networks for image pattern recognition. We examine the early and late fusion of multiple pattern recognition classifiers using various plant leaves. Commonly, it considers disease discovery with the diagnostic technologies available. In standard cases, planters usually do not discover the disease.Therefore, plant leaf disease detection is a significant research problem, and one of their goals is to uncover an effective way to identify leaf image disease. The article has made a potential effort to find a process that should be able to expose plant leaf disease using early and late fusion of two classifiers: modified Optimized Deep Neural Network (ODNN) with different parameters of evolutionary optimization of Grasshopper algorithm (GOA), Speeded Up Robust Features (SURF) and Convolutional Neural Network (CNN) that could support the system to achieve excellent performance. Classification quality parameters are determined, and research to explain the validation of the model has been carried out.

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Section: F I G U R E 5 Precision Early Fusion Scores T a B L E 3 Earl...mentioning

confidence: 96%

Hybrid intelligent technology for plant health using the fusion of evolutionary optimization and deep neural networks

Al-bayati

Üstündağ

2021

Expert Systems

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“…Spotted‐wilt insects that feed on different crops by puncturing their leaves and sucking the contents of the plant can spread this infection. Any of the symptoms include bronze‐colored or black‐spotted leaves, dark streaking in the plant's terminal branches, small growth, and possible die‐back of the plant's growing tips 6 . In curly‐top disease young leaves and shoots on the infected plant are stunted.…”

Section: Plant Leaf Disease and Symptomsmentioning

confidence: 99%

“…Any of the symptoms include bronze-colored or black-spotted leaves, dark streaking in the plant's terminal branches, small growth, and possible die-back of the plant's growing tips. 6 In curly-top disease young leaves and shoots on the infected plant are stunted. It takes a long time to mature, finally becoming bushy and dwarfed.…”

Section: Viral Diseasementioning

confidence: 99%

Recent advances in agricultural disease image recognition technologies: A review

Bondre

Patil

2023

Concurrency and Computation

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In the world of intelligent agriculture, agricultural disease picture detection plays a critical role. Plant disease identification must be efficient if agricultural production is to be increased sustainably. Anomalies in plants affected by viruses, insects, nutritional deficiencies, or poor weather conditions have traditionally been diagnosed by human experts. Deep learning (DL) and transfer learning, two sophisticated machine learning techniques synonymous with the advancement of artificial intelligence technologies in recent years, have started to be used for the identification of agricultural diseases.However, a variety of significant obstacles exist in the way of widespread use of these approaches. This paper looks at DL and transfer learning in particular, and discusses recent developments in using these advanced technologies to recognize agricultural disease images. Many DL architectures have recently been adopted, along with visualization tools, which are critical for identifying signs and classifying plant diseases. The analysis and evaluation of these two approaches show that transfer learning is the better choice given existing agricultural disease data tools. The paper then looks at the key problems that need to be addressed for research in this area to advance, such as image dataset creation, the collection of big data auxiliary domains, and the optimization of the transfer learning process. The construction of technically feasible agricultural disease image recognition systems requires the creation of image datasets collected under real cultivation conditions. This survey aims to learn more about DL capabilities in plant disease detection to increase device efficiency and accuracy in future research.

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“…Deep learning has the ability to capture a deep representation of images that is useful for recognition and detection. Several researchers have used deep learning methods, typically convolutional neural networks (CNNs), to detect plant leaf disease (Joshi & Bhavsar, 2020;Wallelign, 2020;Yu & Son, 2020;Yu et al, 2021;Zeng et al, 2022). C. R. et al ( 2022) combined a CNN based on the residual dense block and multilevel feature detection to select the appropriate feature for cassava leaf diseases.…”

Section: Introductionmentioning

confidence: 99%

DFNet: Dense fusion convolution neural network for plant leaf disease classification

et al. 2023

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The early identification of plant diseases is crucial for preventing the loss of crop production. Recently, the advancement of deep learning has significantly improved the identification of plant leaf diseases. However, most approaches depend on a single convolutional neural network (CNN) to extract the leaf features, ignoring the opportunity to take full advantage of the feature richness available in the images. This paper explores a novel CNN model with multiple automated feature extractors, namely, dense fusion CNN (DFNet), for classifying plant leaf diseases. DFNet aims to increase the diversity of extracted features in order to improve discrimination. Instead of using a single‐CNN model, DFNet relies on a double‐pretrained CNN model, MobileNetV2 and NASNetMobile, as the feature extractor. The features extracted from each CNN are fused in the fusion layer using a fully connected network. The proposed method was evaluated using corn (Zea mays L.) and coffee (Coffea canephora) leaf disease datasets and compared to the existing models. The experiment showed that DFNet is superior and consistent to other CNN methods by achieving an accuracy of 97.53% for corn leaf diseases and 94.65% for coffee leaf diseases.

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Plant leaf disease detection and control: A survey

Cited by 23 publications

References 14 publications

Hybrid intelligent technology for plant health using the fusion of evolutionary optimization and deep neural networks

Hybrid intelligent technology for plant health using the fusion of evolutionary optimization and deep neural networks

Recent advances in agricultural disease image recognition technologies: A review

DFNet: Dense fusion convolution neural network for plant leaf disease classification

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