Advanced biosensing technologies for monitoring of agriculture pests and diseases: A review

He, Jun; Chen, Ke; Zhai, Junfeng; Lin, Xiangmei

doi:10.1088/1674-4926/44/2/023104

Cited by 15 publications

(6 citation statements)

References 77 publications

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“…In general, the model ensures that proper monitoring is induced rather than required. It is evident from the literature that the terms of financial institutions always require efficient and independent monitoring for the success of any financing programme (He et al, 2023). Against this background, the researcher looked at the possibility of involving stakeholder's interest in the model in such a way that coordination, cooperation, close and proper monitoring will be a self-induced or instinctive response because failure to do so will turn the stakeholders to the losing side.…”

Section: Risk Management Of the Modelmentioning

confidence: 99%

A proposed Bay-Salam with Takaful and value chain model for financing agriculture in Kano State, Nigeria

Atah,

Mohammed,

Adeyemi

et al. 2024

IES

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PurposeThe purpose of this paper is to propose a model that will demonstrate how the integration of Salam (exclusive agricultural commodity trade) with Takaful (micro-Takaful – a subdivision of Islamic insurance) and value chain can address major challenges facing the agricultural sector in Kano State, Nigeria.Design/methodology/approachThe study conducted a thorough and critical analysis of relevant literature and existing models of financing agriculture in Nigeria to come up with the proposed model.FindingsThe findings indicate that measures undertaken to address the major challenges fail. In view of this, this study proposed Bay-Salam with Takaful and value chain model to solve a number of challenges such as poor access to financing, poor marketing and pricing, delay, collateral requirement and risk issues in order to avail farmers with easy access to finance and provide effective security to financial institutions.Research limitations/implicationsThe paper is limited to using secondary data. Therefore, empirical investigation can be carried out to strengthen the validation of the model.Practical implicationsThe study outcome seeks to improve the productivity of the farmers through enhancing their access to finance. This will increase their level of production and provide more employment opportunities. In addition, it will boost financial inclusion, income generation, poverty alleviation, standard of living, food security and overall economic growth and development.Originality/valueThe novelty of this study lies in the integration of classical Bay-Salam with Takaful and value chain and create a unique model structure which the researchers do not come across in any research that presented it in Nigeria.

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Section: Risk Management Of the Modelmentioning

confidence: 99%

A proposed Bay-Salam with Takaful and value chain model for financing agriculture in Kano State, Nigeria

Atah,

Mohammed,

Adeyemi

et al. 2024

IES

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“…It is important to mention the strategies used to mitigate the effects produced by plagues and diseases around the world, such as the selection of resistant varieties, crop rotation, the use of natural enemies of plagues, and the rational use of chemical products, among others. More efforts need to be made to implement mechanisms and innovative strategies to reduce loss in food crops and sustainably contribute to food security [9,11,12].…”

Section: Introductionmentioning

confidence: 99%

A Performance Comparison of CNN Models for Bean Phenology Classification Using Transfer Learning Techniques

Ibarra-Pérez,

Jaramillo-Martínez,

Correa-Aguado

et al. 2024

AgriEngineering

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The early and precise identification of the different phenological stages of the bean (Phaseolus vulgaris L.) allows for the determination of critical and timely moments for the implementation of certain agricultural activities that contribute in a significant manner to the output and quality of the harvest, as well as the necessary actions to prevent and control possible damage caused by plagues and diseases. Overall, the standard procedure for phenological identification is conducted by the farmer. This can lead to the possibility of overlooking important findings during the phenological development of the plant, which could result in the appearance of plagues and diseases. In recent years, deep learning (DL) methods have been used to analyze crop behavior and minimize risk in agricultural decision making. One of the most used DL methods in image processing is the convolutional neural network (CNN) due to its high capacity for learning relevant features and recognizing objects in images. In this article, a transfer learning approach and a data augmentation method were applied. A station equipped with RGB cameras was used to gather data from images during the complete phenological cycle of the bean. The information gathered was used to create a set of data to evaluate the performance of each of the four proposed network models: AlexNet, VGG19, SqueezeNet, and GoogleNet. The metrics used were accuracy, precision, sensitivity, specificity, and F1-Score. The results of the best architecture obtained in the validation were those of GoogleNet, which obtained 96.71% accuracy, 96.81% precision, 95.77% sensitivity, 98.73% specificity, and 96.25% F1-Score.

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“…These sensors provide valuable data for precise fertilizer application, ensuring that crops receive the right amount of nutrients for optimal growth and yield. On the other hand, crop health sensors detect indicators of plant stress, diseases, and pests [30]. They can measure parameters such as leaf temperature, chlorophyll content, and vegetation indices to assess crop health and detect early signs of disease or pest infestation.…”

Section: Introductionmentioning

confidence: 99%

An extensive survey of smart agriculture technologies: Current security posture

Otieno¹

2023

World J. Adv. Res. Rev.

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Smart agriculture, enabled by advanced technologies such as internet of things, artificial intelligence and data analytics, offers immense potential for optimizing farming practices and increasing agricultural productivity. However, as the adoption of smart agriculture systems continues to grow, it brings forth various security issues that need to be addressed to protect farming operations, data integrity, and privacy. This paper provides an overview of the security issues in smart agriculture, including vulnerabilities in internet of things devices, lack of standardized security protocols, limited security awareness among farmers, and challenges in securing data and communication networks. In addition, it highlights the need for robust security measures to mitigate risks such as unauthorized access, data breaches, and disruption of operations. Moreover, emphasis should be laid on the importance of collaborative efforts between technology providers, agricultural stakeholders, researchers, and policymakers to develop effective security solutions and standards that ensure the trustworthiness and resilience of smart agriculture systems.

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Advanced biosensing technologies for monitoring of agriculture pests and diseases: A review

Cited by 15 publications

References 77 publications

A proposed Bay-Salam with Takaful and value chain model for financing agriculture in Kano State, Nigeria

A proposed Bay-Salam with Takaful and value chain model for financing agriculture in Kano State, Nigeria

A Performance Comparison of CNN Models for Bean Phenology Classification Using Transfer Learning Techniques

An extensive survey of smart agriculture technologies: Current security posture

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