Application of expert systems for agricultural crop disease diagnoses — A review

Deepthi, M B; Sreekantha, D. K.

doi:10.1109/icicct.2017.7975192

Cited by 11 publications

(4 citation statements)

References 12 publications

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“…In the era of globalization, 'transdisciplinary' is also a keyword used in agriculture and forestry, considering the economic, environmental, and social sustainability of the agricultural and forest systems. Expert systems [such as the decision support systems (DSSs)] are a clear exemplification of this transdisciplinarity (Yelapure and Kulkarni 2012;Deepthi and Sreekantha 2017). DSSs, which are coined in numerous ways for many insect pests, diseases and weeds (Damos 2015), can be practically applied with a concrete benefit for the growers on a given productive area, overlaying several topics and simultaneously addressing numerous issues, including trap-based monitoring information (Jones et al 2010).…”

Section: Monitoring Insect Pests With New Technologiesmentioning

confidence: 99%

Insect pest monitoring with camera-equipped traps: strengths and limitations

2020

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Integrated pest management relies on insect pest monitoring to support the decision of counteracting a given level of infestation and to select the adequate control method. The classic monitoring approach of insect pests is based on placing in single infested areas a series of traps that are checked by human operators on a temporal basis. This strategy requires high labor cost and provides poor spatial and temporal resolution achievable by single operators. The adoption of image sensors to monitor insect pests can result in several practical advantages. The purpose of this review is to summarize the progress made on automatic traps with a particular focus on camera-equipped traps. The use of software and image recognition algorithms can support automatic trap usage to identify and/or count insect species from pictures. Considering the high image resolution achievable and the opportunity to exploit data transfer systems through wireless technology, it is possible to have remote control of insect captures, limiting field visits. The availability of real-time and on-line pest monitoring systems from a distant location opens the opportunity for measuring insect population dynamics constantly and simultaneously in a large number of traps with a limited human labor requirement. The actual limitations are the high cost, the low power autonomy and the low picture quality of some prototypes together with the need for further improvements in fully automated pest detection. Limits and benefits resulting from several case studies are examined with a perspective for the future development of technology-driven insect pest monitoring and management.

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Section: Monitoring Insect Pests With New Technologiesmentioning

confidence: 99%

Insect pest monitoring with camera-equipped traps: strengths and limitations

2020

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“…Several reviews have been conducted to investigate the computational approaches based on image processing to identifying crop diseases. A review in [10] offers an analysis and discussion on using expert systems in agricultural crop diagnostics. The emphasis of this review is on an expert system that can analyze the signs of a variety of crop diseases, such as those that affect tomato, wheat and rice and provide recommendations for potential treatments to the farmers.…”

Section: Introductionmentioning

confidence: 99%

Computational Approaches Based On Image Processingfor Automated Disease IdentificationOn Chili Leaf Images: A Review

Aminuddin¹,

Joret²,

Zulkifli³

et al. 2023

EmAIT

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Chili, an important crop whosefruit is used as a spice, is significantly hampered by the existence of chili diseases.While these diseases pose a significant concern to farmers since they impair the supply of spices to the market, they can be managed and monitored to lessen their impact. Therefore, identifying chili diseases using a pertinent approach is of enormous importance. Over the years, the growth of computational approaches based on image processing has found its application in automated disease identification, leading to the availability of a reliable monitoring tool that produces promising findings for the chili. Numerous research papers on identifying chili diseases using the approaches have been published. Still, to the best knowledge of the author, there has not been a proper attempt to analyze these papers to describe the many steps of diagnosis, including pre-processing, segmentation, extraction of features, as well as identification techniques. Thus, a total of 50 researchpaperpublications on theidentificationof chilidiseases, with publication dates spanning from 2013 to 2021, arereviewed in this paper.Through the findings in this paper, it becomes feasible to comprehend the development trend for the application of computational approaches based on image processing in the identification of chili diseases, as well as the challenges and future directions that require attention from the present research community.

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“…It uses sensors to monitor crop growth and to identify pests and diseases. Tailored recommendations can thus be generated by combining individual‐ and macro‐level data to support farmers' decision‐making processes (Deepthi & Sreekantha, 2017; Johannes et al, 2017; Lucas, 2011; Panda, 2020; Wang et al, 2013). Despite government efforts to promote digital agriculture innovations (e.g., DPDM), smallholder farmers in developing countries continue to struggle with recognizing the value of digital technology and adoption (Lowenberg‐DeBoer & Erickson, 2019).…”

Section: Introductionmentioning

confidence: 99%