Smart Agriculture: Machine Learning for Disease Detection and Soil Monitoring in Cucumis Sativus

Shekhar Kumar; Austin Dhomane; Tejaswini M Thonge

doi:https://doi.org/10.47001/ICJES/2025.406001

Authors

Shekhar Kumar

School of Computer Applications, Lovely Professional University, Punjab, India

Austin Dhomane

School of Computer Applications, Lovely Professional University, Punjab, India

Tejaswini M Thonge

School of Computer Applications, Lovely Professional University, Punjab, India

Abstract

Cucumber (Cucumis sativus) is an economically important crop worldwide, but its main challenges are various diseases affecting its leaves and fruits. This paper presents a comprehensive review of cucumber leaf diseases, cucumber fruit diseases, impact on marketing strategies and Land recommendation using soil fertility and weather prediction, common diseases like downy mildew, powdery mildew, belly rot and furthermore, using machine learning algorithms to develop soil prediction models to monitor soil health indicators and identify disease causative agents will be coming. The findings reveal significant relationships between soil type, environmental factors, and incidence of cucumber diseases. This information enables stakeholders to implement targeted interventions such as soil amendments, crop rotations and disease-resistant varieties to reduce disease pressure and improve crop yields and quality. Furthermore, a comprehensive analysis of cucumber marketing strategies and consumer preferences provides insights into effective market segmentation and promotional strategies.

Keywords

cucumber diseases VGG16 YOLOv5 weather prediction prediction method AI Image processing

Citation of this Article

Shekhar Kumar, Austin Dhomane, & Tejaswini M Thonge. (2025). Smart Agriculture: Machine Learning for Disease Detection and Soil Monitoring in Cucumis Sativus. International Current Journal of Engineering and Science - ICJES, 4(6), 1-7. Article DOI: https://doi.org/10.47001/ICJES/2025.406001

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[1] F. Bassanezi RB, Gas exchange and emission of chlorophyll fluorescence during the monocycle of rust, angular leaf spot and anthracnose on bean leaves as a function of their trophic characteristics, 2002.

[2] Sriboonchitta M.S., H. T. Nguyen, A. Wiboonpongse, J.Liu, "Modeling volatility and dependency of agricultural priceand production indices of Thailand: static versus time- varyingcopulas," International Journal of Approximate Reasoning.

[3] B. Oancea, C. Ciucu, "Time series forecasting using neuralnetworks," 2013.

[4] K. Alok Kumar, " “Plant Disease Detection using VGG16,” International Journal of Creative Research Thoughts, Volume 11, 1 January 2023.," [Online]. Available: https://ijcrt.org/papers/IJCRT2301347.pdf.

[5] Z. C. e. al, "Plant disease recognition model based on improved , vol. 12, p. 365, 2022.," [Online].

[6] B. T, Regulation of the production of stress ethylene and its significance.

[7] Caeneghem WV, Messens E, Lamber H, van Montagu M, van der Straeten D, "Presymptomatic visualization of plant–virus interactions by thermography," Nature Biotechnology.

[8] Boever F, van Montagu M, van der Straeten D, "Thermographic visualization of cell death in tobacco and Arabidopsis," Plant, Cell and Environment, 2001.

[9] Hagenbeek D, De Bruyne E, Valcke R, van der Straeten D, "Thermal and chlorophyll-fluorescence imaging distinguish plant–pathogen interactions at an early stage," Plant Cell Physiology, 2004.

[10] HORDI, "HORDI," [Online]. Available: https://doa.gov.lk/hordi-crop-cucumber/.

[11] S. R. Shampa Chakraborty, Health Beneficial Effects of Cucumber,” Studies on Cucumber (Cucumis sativus L., 2012.

[12] T. C. Michael S, Resistance to Belly Rot in Cucumber Identified through Field and Detached-fruit Evaluations.

[13] Z. Chen, Plant disease recognition model based on improved, 2022.

[14] M. Yang, Fruit target detection based on BCo-YOLOv5 model.

[15] Akshatkary H, Benhamou N, Brodeur J, "Ultrastructural and cytochemical investigations of the antagonistic effect of Verticillium lecanii on cucumber powdery mildew".