An Algorithm For Detection of Nutritional Deficiencies from Digital Images of Coffee Leaves Based on Descriptors and Neural Networks

Sosa, José Daniel Hernández; Ramirez, Janidet; Vives, Luis; Kemper, Guillermo

doi:10.1109/stsiva.2019.8730286

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…Asimismo, se identificaron artículos sobre aplicación de IA en detección de enfermedades en la hoja y grano de café (Kumar et al, 2020;Monsalve et al, 2015;Montalbo & Hernandez, 2020;Sosa et al, 2019;Torres Caballero & Reyes Duke, 2020), así como en el reconocimiento de calidad y grado de tostado (Hakim et al, 2020;Leme et al, 2019). Estas investigaciones fueron revisadas por ser pertinente al objeto de estudio, pero no se incorporaron al corpus de artículos seleccionados.…”

Section: Resultados De La Revisiónunclassified

Eficacia de sistemas expertos en la selección de granos de café (Coffea arabica)

Monsalve-Vásquez

2022

Rev. Amaz. Digit.

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La selección de granos de café es importante para su productividad; sin embargo, productores de baja o mediana escala realizan este proceso de forma manual, ocasionado limitaciones que afectan su producción o comercialización. El artículo tuvo como objetivo analizar la eficacia de los sistemas expertos en la selección de granos de café. Para ello, el estudio consistió en una revisión sistemática de la literatura en las bases de datos de Xplore IEEE, SciencieDirect y SpringerLink, de artículos publicados en revistas indizadas a Scopus, WoS o Scielo; entre los años 2015 y 2021. El resultado de la revisión fue una matriz de información según autor, título del artículo, técnicas o modelos y eficacia de los sistemas expertos. Entre ellos, destacan el uso de los parámetros de procesamiento de imágenes RGB convertidos en HSV, como HSL; y la efectividad de los sistemas, en todos los casos fueron superiores al 80%. La revisión concluye que los sistemas expertos son eficaces para la selección de granos de café, debido a que optimizan tiempo y mejoran la calidad en la selección de granos.

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Section: Resultados De La Revisiónunclassified

Eficacia de sistemas expertos en la selección de granos de café (Coffea arabica)

Monsalve-Vásquez

2022

Rev. Amaz. Digit.

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“…It is necessary to detect their presence as soon as possible to prevent pests from destroying all existing plants. Almost all of the filtered articles discussed the detection of diseases or the health level of coffee plants through their leaves [9,10,35,41,[45][46][47][48]50,53,54]. The rest detected insect pests based on sound [32] and detected tissue characteristics using ultrasonic waves [11].…”

Section: Iot Smart Farming Technology Solution For Coffee Farmingmentioning

confidence: 99%

The Potential of Smart Farming IoT Implementation for Coffee farming in Indonesia: A Systematic Review

Mulyono

Apnitami

Wangi

et al. 2022

Green Intell. Syst. Appl.

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As one of Indonesia’s main export agricultural commodities, coffee farming faces many obstacles, ranging from plant pest organisms to climate and environmental problems. These problems can be solved using smart farming technology. However, smart farming technology has not been applied intensively in Indonesia. This paper aims to analyze the potential for implementing smart farming for coffee in Indonesia. This article presents a systematic review of the information about the potential application of IoT smart farming for coffee farming in Indonesia by applying the PSALSAR (Protocol, Search, Appraisal, Synthesis, Analysis, Report) review method. This study concludes the list of smart farming technologies for coffee that have the potential to be applied in Indonesia. Those technologies are classified based on their application scope: quality control (including subtopics like coffee quality control), climate monitoring, the anticipation of pest organisms, and coffee processing), coffee production planning, and coffee waste utilization. Regarding infrastructure readiness and the need for smart farming technology for coffee, the island of Java has the most potential for implementing smart farming for coffee as soon as possible. The high potential for application in Java is because the telecommunications technology infrastructure is ready, and the land area and coffee production are large.

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“…As a result, ML produces the highest accuracy, reaching 97% [8], [24], [17] study about the classification of macronutrient deficiency and MLP give the best performance, with 83.33% of accuracy. RFT, Naive Bayes, and SVM have been compared in other studies and SVM deliver the best performance [28]. In addition, research [20] also compares the use of deep learning methods with and without a combination of SVM.…”

Section: Related Workmentioning

confidence: 99%

Classification of Macronutrient Deficiency in Chili Leaves using Support Vector Machine

Rahadiyan¹,

Hartati²,

Wahyono³

et al. 2022

Advances in Biological Sciences Research

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Chili is a horticultural crop that has high economic value in Indonesia. The productivity level of chili in the country is not proportional to the level of consumption, one of the causes is malnutrition. Each plant requires different amounts of macronutrients and micronutrients to support plant growth and development. Chili plants that lack or excess macronutrients show different visual symptoms. Digital Image Processing is a non-destructive method that is useful for determining plant health conditions based on visual symptoms of chili leaves. The combination of digital image processing and learning methods such as the Support Vector Machine (SVM) helps classify the types of macronutrient deficiencies in order to obtain a nutrient solution. In this study, there are several stages in determining macronutrient deficiencies in chili plants, namely image acquisition, pre-processing, feature extraction, to classification using SVM with several kernels. Based on the experimental results in this study, the SVM method can help modern farmers to determine the health condition of plants non-destructively with 97.76% accuracy using a Polynomial kernel. Applying this system to an intelligent farming system is expected to support the realization of precision agriculture in Indonesia.

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An Algorithm For Detection of Nutritional Deficiencies from Digital Images of Coffee Leaves Based on Descriptors and Neural Networks

Cited by 6 publications

References 5 publications

Eficacia de sistemas expertos en la selección de granos de café (Coffea arabica)

Eficacia de sistemas expertos en la selección de granos de café (Coffea arabica)

The Potential of Smart Farming IoT Implementation for Coffee farming in Indonesia: A Systematic Review

Classification of Macronutrient Deficiency in Chili Leaves using Support Vector Machine

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