A deep learning approach for anthracnose infected trees classification in walnut orchards

Anagnostis, Athanasios; Tagarakis, Aristotelis C.; Asiminari, Gavriela; Papageorgiou, Elpiniki I.; Kateris, Dimitrios; Moshou, Dimitrios; Bochtis, Dionysis

doi:10.1016/j.compag.2021.105998

Cited by 51 publications

(22 citation statements)

References 34 publications

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“…In reference to large-scale cultivations, such kinds of automated processes can be combined with autonomous vehicles, to timely identify phytopathological problems by implementing regular inspections. Furthermore, maps of the spatial distribution of the plant disease can be created, depicting the zones in the farm where the infection has been spread [ 57 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Agriculture: A Comprehensive Updated Review

Benos

Tagarakis

Dolias

et al. 2021

Sensors

379

146

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The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” along with “crop management”, “water management”, “soil management”, and “livestock management”, and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018–2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.

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Section: Introductionmentioning

confidence: 99%

Machine Learning in Agriculture: A Comprehensive Updated Review

Benos

Tagarakis

Dolias

et al. 2021

Sensors

379

146

View full text Add to dashboard Cite

show abstract

“…However, deep learning methods are gradually being applied to agricultural research because they can automatically learn the deep feature information of images, and their speed and accuracy levels are greater than those of traditional algorithms [27][28][29][30]. Deep learning has also been applied to the detection of plant diseases from visible light images.…”

Section: Introductionmentioning

confidence: 99%

Plant Disease Recognition Model Based on Improved YOLOv5

Wu²,

et al. 2022

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To accurately recognize plant diseases under complex natural conditions, an improved plant disease-recognition model based on the original YOLOv5 network model was established. First, a new InvolutionBottleneck module was used to reduce the numbers of parameters and calculations, and to capture long-distance information in the space. Second, an SE module was added to improve the sensitivity of the model to channel features. Finally, the loss function ‘Generalized Intersection over Union’ was changed to ‘Efficient Intersection over Union’ to address the former’s degeneration into ‘Intersection over Union’. These proposed methods were used to improve the target recognition effect of the network model. In the experimental phase, to verify the effectiveness of the model, sample images were randomly selected from the constructed rubber tree disease database to form training and test sets. The test results showed that the mean average precision of the improved YOLOv5 network reached 70%, which is 5.4% higher than that of the original YOLOv5 network. The precision values of this model for powdery mildew and anthracnose detection were 86.5% and 86.8%, respectively. The overall detection performance of the improved YOLOv5 network was significantly better compared with those of the original YOLOv5 and the YOLOX_nano network models. The improved model accurately identified plant diseases under natural conditions, and it provides a technical reference for the prevention and control of plant diseases.

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“…Finally, by describing each output as a linear combination of the defuzzifized consequent parts, the model takes advantage of the mapping capability offered by the consequent parts to approximate the desired outputs. Recent studies, as found on [20], continued on the idea of creating models based on neuro-fuzzy inference systems with the advantage of obtaining the weights of connecting links to adjust the parameters of fuzzy rules. In other words, for determining the rules and obtaining the weights, in addition to the knowledge and experience of specialists, the model also exploits the existing data to fine-tune the inference system's parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern Classification

Nápoles¹,

Salgueiro²,

Grau³

et al. 2021

Preprint

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Machine learning solutions for pattern classification problems are nowadays widely deployed in society and industry. However, the lack of transparency and accountability of most accurate models often hinders their meaningful and safe use. Thus, there is a clear need for developing explainable artificial intelligence mechanisms. There exist model-agnostic methods that summarize feature contributions, but their interpretability is limited to specific predictions made by black-box models. An open challenge is to develop models that have intrinsic interpretability and produce their own explanations, even for classes of models that are traditionally considered black boxes like (recurrent) neural networks. In this paper, we propose an LTCNbased model for interpretable pattern classification of structured data. Our method brings its own mechanism for providing explanations by quantifying the relevance of each feature in the decision process. For supporting the interpretability without affecting the performance, the model incorporates more flexibility through a quasi-nonlinear reasoning rule that allows controlling nonlinearity. Besides, we propose a recurrence-aware decision model that evades the issues posed by unique fixed points while introducing a deterministic learning method to compute the learnable parameters. The simulations show that our interpretable model obtains competitive performance when compared to the stateof-the-art white and black boxes.

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A deep learning approach for anthracnose infected trees classification in walnut orchards

Cited by 51 publications

References 34 publications

Machine Learning in Agriculture: A Comprehensive Updated Review

Machine Learning in Agriculture: A Comprehensive Updated Review

Plant Disease Recognition Model Based on Improved YOLOv5

Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern Classification

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