Detection of nutrition deficiencies in plants using proximal images and machine learning: A review

Barbedo, Jayme Garcia Arnal

doi:10.1016/j.compag.2019.04.035

Cited by 133 publications

(81 citation statements)

References 80 publications

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“…In this sense, several studies are related to the use of A4.0 technologies, such as sensing and actuation drones to identify and control pests in crops [20], sensors and Internet of Things (IoT) for water management [21], agricultural decision support systems [22], proximal images and machine learning (ML) to identify nutritional deficiencies in crops [23], among others. These technologies assist in data collect, information analysis, diagnostics and formulation of strategies for the agricultural sector [24].…”

Section: Introductionmentioning

confidence: 99%

Precision Techniques and Agriculture 4.0 Technologies to Promote Sustainability in the Coffee Sector: State of the Art, Challenges and Future Trends

et al. 2020

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Precision Agriculture (PA) and Agriculture 4.0 (A4.0) have been widely discussed as a medium to address the challenges related to agricultural production. In this research, we present a Systematic Literature Review (SLR) supported by a Bibliometric Performance and Network Analysis (BPNA) of the use of A4.0 technologies and PA techniques in the coffee sector. To perform the SLR, 87 documents published since 2011 were extracted from the Scopus and Web of Science databases and processed through the Preferred Reporting Items for Systematic reviews and Meta-Analyzes (PRISMA) protocol. The BPNA was carried out to identify the strategic themes in the field of study. The results present 23 clusters with different levels of development and maturity. We also discovered and presented the thematic network structure of the most used A4.0 technologies in the coffee sector. Our findings shows that Internet of Things, Machine Learning and geostatistics are the most used technologies in the coffee sector, we also present the main challenges and trends related to technological adoption in coffee systems. We believe that the demonstrated results have the potential to be considered by researchers in future works and decision making related to the field of study.

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Section: Introductionmentioning

confidence: 99%

Precision Techniques and Agriculture 4.0 Technologies to Promote Sustainability in the Coffee Sector: State of the Art, Challenges and Future Trends

et al. 2020

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“…It also accelerates the development of better accuracy and performance image analysis software. [17] Deep Learning [18] Machine learning…”

Section: Image Processing Systemmentioning

confidence: 99%

“…Forward Chaining [1], Fuzzy system [2], K-Means Clustering [3] [4], Decision Tree [5] [6], Bayesian networks and incremental learning [7], Naïve Bayes and Certainty Factor [8], Naïve Bayes [9], Computer vision and artificial intelligence [10], Deep Convolutional Neural Network [11] [12], Fuzzy inference system [13], Convolutional Neural Network (CNN) [14], Fractal Dimension Values and Fuzzy C-Means [15], Deep learning [16] [17], Machine learning [18]. Furthermore, there are some applications for detection system of plant pests and diseases using technologies as follows: expert system [1] [19], mobile system [6] [12], computer vision and artificial intelligence [10] [20], image processing system [15] [16] [18] [20], and Internet of Things (IoT) [21] [22] [23]. However, research in this area is still needed, especially from the computer science perspective.…”

Section: Introductionmentioning

confidence: 99%

Technologies, methods, and approaches on detection system of plant pests and diseases

Anamisa¹,

Yusuf²,

Agustiono³

et al. 2019

EECSI

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This research aims to identify the technology, methods, approaches applied in developing plant pest and disease detection systems. For this purpose, it mainly reviews systematically related research on identification, monitoring, detection, and control techniques of plant pests and diseases using a computer or mobile technology. Evidence from the literature shows previous both academia and practitioners have used various technologies, methods and approaches for developing detection system of plant pests and diseases. Some technologies have been applied for the detection system, such as web-based, mobile-based, and internet of things (IoT). Furthermore, the dominant approaches are expert system and deep learning. While backward chaining, forward chaining, fuzzy model, genetic algorithm (GA), K-means clustering, Bayesian networks and incremental learning, Naïve Bayes and Certainty Factors, Convolutional Neural Network, and Decision Tree are the most frequently methods applied in the previous researches. The review also indicated that no single technology or technique is best for developing accurate pest/disease detection system. Instead, the combination of technologies, methods, and approaches resulted in different performance and accuracies. A possible explanation for this is because the systems are used for detecting, controlling and monitoring various plants, such as corn, onion, wheat, rice, mango, flower, and others that are different. This research contributes by providing a reference for technologies, methods, and approaches to the detection system for plant pests and diseases. Also, it adds a way of literature review. This research has implications for researchers as a reference for researching in the computer system, especially for the detection of plant pest and disease research. Hence, this research also extends the body of knowledge of the intelligence system, deep learning, and computer science. For practice, the method references can be used for developing technology for detecting plant pest and disease.

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“…Thus, proximal images are still prevalent. Multispectral [14,15], hyperspectral [16,17], thermal [18] and X-ray [10] sensors are being explored, but conventional RGB (Red-Green-Blue) sensors still dominate due to their low price, portability and flexibility [19].…”

Section: Introductionmentioning

confidence: 99%

Detecting and Classifying Pests in Crops Using Proximal Images and Machine Learning: A Review

Barbedo

2020

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Pest management is among the most important activities in a farm. Monitoring all different species visually may not be effective, especially in large properties. Accordingly, considerable research effort has been spent towards the development of effective ways to remotely monitor potential infestations. A growing number of solutions combine proximal digital images with machine learning techniques, but since species and conditions associated to each study vary considerably, it is difficult to draw a realistic picture of the actual state of the art on the subject. In this context, the objectives of this article are (1) to briefly describe some of the most relevant investigations on the subject of automatic pest detection using proximal digital images and machine learning; (2) to provide a unified overview of the research carried out so far, with special emphasis to research gaps that still linger; (3) to propose some possible targets for future research.

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Detection of nutrition deficiencies in plants using proximal images and machine learning: A review

Cited by 133 publications

References 80 publications

Precision Techniques and Agriculture 4.0 Technologies to Promote Sustainability in the Coffee Sector: State of the Art, Challenges and Future Trends

Precision Techniques and Agriculture 4.0 Technologies to Promote Sustainability in the Coffee Sector: State of the Art, Challenges and Future Trends

Technologies, methods, and approaches on detection system of plant pests and diseases

Detecting and Classifying Pests in Crops Using Proximal Images and Machine Learning: A Review

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