Laser-Stabilized Real-Time Plant Discrimination Sensor for Precision Agriculture

Askraba, Sreten; Paap, Arie; Alameh, Kamal; Rowe, J. E.; Miller, Carl C.

doi:10.1109/jsen.2016.2582908

Cited by 13 publications

(4 citation statements)

References 6 publications

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“…The monitoring of the action of biological agents in the control of phytopathogenic diseases is mostly carried out by direct and integral human work at the place of cultivation. However, the automation of the monitoring of the biocontrol action in agricultural crops can easily be performed by optical sensor in real time, because in the studies of Askraba et al (2016) found that plant differentiation sensors based on the images capitated with the proximal spectral reflectance of the leaves allows to differentiate the cultivated plant from the weed with an accuracy of 90%.…”

Section: Chapter 282mentioning

confidence: 99%

Bacillus amyloliquefaciens, Microbacterium resistens and Stenotrophomonas maltophilia in Biocontrol of Corynespora cassiicola in Tomato Culture

Monteiro¹,

Albuquerque²,

Silveira³

et al. 2023

Development and Its Applications in Scientific Knowledge

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This article presents an approach on the use of biological control applying the action of bacteria against Corynespora cassiicola (Bert & Curt) Wei, as this is one of the main phytopathogen of aerial propagation that limits the productivity of solanaceous plants. The use of bacteria in the control of solanaceous diseases is a widespread methodology, since the biocontrol of phytopathogen carried out by bacteria can occur through different mechanisms such as inhibition by antibiotics, parasitism, competition for physical space or induction of plant resistance to pathogen attack. This research aimed to select, identify and evaluate in vitro and in plant antagonist bacteria with potential for the development of a bioproduct that contributes to the sustainable agricultural development of tomatoes. In plant collections and tests were carried out at the Dr. Alejo von Der Pahlen, KM 14 on road AM 010 (S: 02° 59' 45.7" and W: 60° 01' 22.3"). The in vitro tests were carried out at Embrapa Western Amazon. The results indicate that Bacillus amyloliquefaciens showed 75.73% of in vitro inhibition of C. cassiicola and 56% of target spot control when compared to Microbacterium resistens and Stenotrophomonas maltophilia in tests on tomato.

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Section: Chapter 282mentioning

confidence: 99%

Bacillus amyloliquefaciens, Microbacterium resistens and Stenotrophomonas maltophilia in Biocontrol of Corynespora cassiicola in Tomato Culture

Monteiro¹,

Albuquerque²,

Silveira³

et al. 2023

Development and Its Applications in Scientific Knowledge

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show abstract

“…After many tests, the K value ranges typically from 2 to 5. The K-means strategy solves the problem of "Maximization of Expectations" to apply the assignment mechanism to the necessary information points for the closest cluster, as shown in equation (2). We can also observe from equation (4), that when the data points belong to a cluster of xi, they belong to other clusters of k. Likewise, µ j term denotes explicitly cluster centre xi.…”

Section: K Adaptive K-means Clustering Algorithm (Ak-means)mentioning

confidence: 99%

“…This will help-early disease detection, which in agriculture is a great challenge. IoT sensor nodes play a significant role in collecting real-time information play a significant role in collecting real-time information [1], [2]. These nodes can make the system highly practical by gathering data from crop fields in real-time to specify the agriculture system precisely.…”

Section: Introductionmentioning

confidence: 99%

Crop Field Monitoring and Disease Detection of Plants in Smart Agriculture using Internet of Things

Balram¹,

Kumar²

2022

IJACSA

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The Internet of Things can be defined as the network of physical objects that have sensors, software, and other technologies built into them in order to communicate and exchange data with other systems and devices over the internet. In intelligent agricultural advancements to increase the quality of agriculture, the Internet of Things (IoT) can be used. The manual monitoring of plant diseases is quite challenging. It demands enormous effort, expertise in the diseases of plants and the considerable time required for processing. The idea of automation in Smart Agriculture is implemented using the Internet of Things (IoT). They help monitor the plant leaf conditions, control water irrigation, gather images using installed IoT system which includes NodeMCU, cameras, soil moisture, temperature sensors and detect diseases in plants on the datasets collected from leaves. To detect plant diseases, image processing is applied. The detection of diseases comprises the acquisition of images, image pre-processing, segmenting an image, extracting and classifying features. In addition, the performance of two machine-learning techniques, such as a linear and polynomial kernel multi hidden extreme machine (MELM) and a support vector machine (SVM), has been studied. This paper discussed how plant diseases could be detected via images of their leaves. This analysis seeks to validate a proposed system for an appropriate solution to the IoT-based environmental surveillance, water irrigation system management and an efficient approach for leaf disease detection on plants. The proposed multi hidden layers extreme machine classification delivers good performance of 99.12% in the classification of leaf diseases in comparison to the Support Vector Machine classification, which gives 98%.

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“…The system included four digital cameras. In [4] a proximal spectral-reflectancebased plant discrimination sensor used a new stabilized three-wave length laser diode module for plant illumination was developed. The novel sensor can sequentially emit identically polarized laser light beams through a common aperture, along one optical path.…”

Section: The Effects Of Light On Green Vegetation Identificationmentioning

confidence: 99%

A Review: The Survey of the Effects of Light on Weed Recognition

Zheng-qiang

Zhao-min

2018

MATEC Web Conf.

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Weeding is one of the important tasks in agricultural field management. With the development of society and information technology, automatic weeding has become a developing trend. The automatic recognition of weeds based visual is the important step. In this paper, the effects of lighting on green identification and weed identification algorithms are summarized. In order to improve the accuracy and stability of the identification of crops and weeds, some further worth problems in the study are also put forward. This will help further research on automatic weeding.

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Laser-Stabilized Real-Time Plant Discrimination Sensor for Precision Agriculture

Cited by 13 publications

References 6 publications

Bacillus amyloliquefaciens, Microbacterium resistens and Stenotrophomonas maltophilia in Biocontrol of Corynespora cassiicola in Tomato Culture

Bacillus amyloliquefaciens, Microbacterium resistens and Stenotrophomonas maltophilia in Biocontrol of Corynespora cassiicola in Tomato Culture

Crop Field Monitoring and Disease Detection of Plants in Smart Agriculture using Internet of Things

A Review: The Survey of the Effects of Light on Weed Recognition

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