Disease sniffing robots to apps fixing plant diseases: applications of artificial intelligence in plant pathology—a mini review

Prabha, K. Suriya

doi:10.1007/s42360-020-00290-3

Cited by 10 publications

(4 citation statements)

References 42 publications

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“…Swarm intelligence (SI) is one of the most popular bio-inspired computational optimization methods for dealing the real-world applications more effectively [49]. The primary reason for its popularity is that this algorithm is best suited for solving various nonlinear problems in terms of versatility and flexibility.…”

Section: Bio-inspired Approachmentioning

confidence: 99%

AI-Based UAV Swarms for Monitoring and Disease Identification of Brassica Plants Using Machine Learning: A Review

Ali¹,

Deng²,

Shaikh³

et al. 2024

CSSE

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Technological advances in unmanned aerial vehicles (UAVs) pursued by artificial intelligence (AI) are improving remote sensing applications in smart agriculture. These are valuable tools for monitoring and disease identification of plants as they can collect data with no damage and effects on plants. However, their limited carrying and battery capacities restrict their performance in larger areas. Therefore, using multiple UAVs, especially in the form of a swarm is more significant for monitoring larger areas such as crop fields and forests. The diversity of research studies necessitates a literature review for more progress and contribution in the agricultural field. In this review, the comparative analysis of existing literature surveys is explored. This paper aims to provide an overview of AIbased UAV swarms, different cameras and sensors, image processing, and machine learning (ML) algorithms for image analysis having the purpose of monitoring and disease identification. Brassica plants are focused as they are grown on wider scales globally. Brassica species, the commonly infected diseases, and different types of disease detection methods are discussed. Investigations show the significance of using UAV swarms for growth monitoring growth for yield estimation, health monitoring, water status monitoring and irrigation management, nutrition disorders monitoring, pest and disease detection, and pesticide and fertilizer spraying in Brassica plants. Finally, some challenges of swarm-based applications are also addressed that require future consideration. The significance of this paper is that it suggests its readers embrace swarm-based technologies in the pursuit of more efficient production with relevant economic benefits. KEYWORDSArtificial intelligence (AI); Brassica; disease identification; image analysis; image processing; machine learning (ML); monitoring; swarm; remote sensors; unmanned aerial vehicles (UAVs) UAV Swarm Brassica Plants ML Approaches Research FocusFigure 1: Review framework with the research focusWe searched articles in different databases, such as Science Direct, Google Scholar, SpringerLink, SAGE Journals, IEEE Xplore, and Wiley Online Library Journal. In the advanced search section of these database repositories, the period from 2018 to 2023 was selected. While plotting the framework we ensured to include all the important studies. After selecting the first batch of research papers, their related articles were also searched. To extract relevant articles, we read the abstract of all the retrieved articles. The screening criteria include deleting non-English and duplicate papers. We included the papers which were focused on optical sensors and machine learning approaches. We excluded the papers which were focused on other remote sensing tools and sensors. This review is proposed for farmers and researchers who are inclined to study Brassica plants and UAV swarms. The major contributions of the paper include:

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Section: Bio-inspired Approachmentioning

confidence: 99%

AI-Based UAV Swarms for Monitoring and Disease Identification of Brassica Plants Using Machine Learning: A Review

Ali¹,

Deng²,

Shaikh³

et al. 2024

CSSE

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“…There are already more than 800 cancer-fighting medicines and vaccines in clinical trials, according to the Pharmaceutical Research and Manufacturers of America (PhRMA). To provide a commercially available battery of emotional tests for use in clinical settings, the Oxford Pivital® Predicting Response to Depression Treatment (PReDicT) project has set out to develop one 72 .…”

Section: Six-axis Robots Suit Class 1 Clean Roommentioning

confidence: 99%

Untitled

2023

IJPSR

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The rapid development of artificial intelligence (AI) has far-reaching consequences for the global economy. Computers are helping with human input, decisionmaking, and data provision in medicine more than ever before. AI, algorithms, robotics, and big data are all used in today's healthcare sector and the medical profession to derive inferences for monitoring large-scale medical trends and detecting and measuring individual risks and chances based on data-driven estimations. Healthcare is a knowledge-intensive industry that relies heavily on data and analytics to advance treatments and procedures. There has been a significant expansion in the types of medical data gathered in recent years. This includes clinical, genetic, behavioural and environmental information. Large volumes of data from various sources are generated daily by healthcare providers, biomedical researchers, and patients. Technology advancements, such as AI, allow for the analysis of human thought processes. The pharmaceutical business saw significant transformation as a result of the application of machine learning, AI, and automation processes. Various types of analysis have contributed to a general decline in death rates. The use of AI techniques aids in diagnosing the most serious illnesses. To properly diagnose and treat a patient, it is essential to first identify any potential genetic disputes. Technological advancements have made it easier for pharmaceutical companies to detect dosage mistakes. Machine learning relies on monetary analysis, regular spectral analysis and the feature attraction process. The creation of an intelligent computer program is crucial for modelling mechanical systems. In this review, we have glanced at how technology plays a part in the pharmaceutical industry and at the potential outcomes that robotics and AI could have. INTRODUCTION:Robotic systems with artificial intelligence (AI) can learn, make decisions, and solve problems in a manner comparable to that of a person, but they are not yet QUICK RESPONSE CODE

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“…Moreover, the packaging technology has progressed so that the apples will be safer to transport. The packaging materials that have cushioning and are able to keep the humidity level are the ones that can help preserve the quality of apples [9][10][11]. Since these factors are very complex and important, we can say that getting data-driven methods to measure and predict apple quality is a must.…”

Section: Introductionmentioning

confidence: 99%

Apple Perfection: Assessing Apple Quality with Waterwheel Plant Algorithm for Feature Selection and Logistic Regression for Classification

Ibrahim,

Khodadadi,

Khodadadi

et al. 2024

Journal of Artificial Intelligence in Engineering Practice

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This study concentrates on the evaluation of apple quality, which is a vital part of the agricultural industry. The quality of apples is examined through several factors, such as the cultivation techniques, the harvesting methods, and the post-harvest procedures. The dataset, titled "Apple Perfection," contains important characteristics such as the size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and overall quality of the apple. To make the apple quality prediction more accurate, we used different feature selection algorithms, mainly the binary Waterwheel Plant Algorithm (bWWPA), which, in fact, had the lowest average error of 0.52153, and several of the types of classification models, especially Logistic Regression, which had the highest accuracy of 0.88625. The attribute selection process found the most important attributes, which cut down the dimensionality, and hence, the model performance became better. The results of the study show that the combination of bWWPA for feature selection and logistic regression for classification can predict apple quality with high accuracy. This way of dealing with the problem gives us information that is useful for the improvement of the cultivation techniques and the post-harvest handling to the extent that we will be able to have the best quality apples. The findings of this research have a great impact on the farming industry, meaning a strong way to evaluate the quality of apples.

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Disease sniffing robots to apps fixing plant diseases: applications of artificial intelligence in plant pathology—a mini review

Cited by 10 publications

References 42 publications

AI-Based UAV Swarms for Monitoring and Disease Identification of Brassica Plants Using Machine Learning: A Review

AI-Based UAV Swarms for Monitoring and Disease Identification of Brassica Plants Using Machine Learning: A Review

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Apple Perfection: Assessing Apple Quality with Waterwheel Plant Algorithm for Feature Selection and Logistic Regression for Classification

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