A multimodal approach for enhanced disease management in cauliflower crops: integration of spectral sensors, machine learning models and targeted spraying technology

Highlights

	● Sustainable approach to minimize pesticide usage and enhance crop productivity was developed.
	● Disease management in cauliflower achieved by integrating spectral sensor, machine learning, and targeted spraying.
	● Support vector machine outperformed the decision trees model in black rot detection in cauliflower.
	● Targeted spraying cut chemical use by 72.5% and saved 21.0% time in black rot-infested crops.

Abstract

This research explored a novel multimodal approach for disease management in cauliflower crops. With the rising challenges in sustainable agriculture, the research focused on a patch spraying method to control disease and reduce crop losses and environmental impact. For non-destructive disease assessment, a spectral sensor was used to collect spectral information from diseased and healthy cauliflower parts. The spectral data sets were analyzed using decision tree and support vector machine (SVM) algorithms to identify the most accurate model for distinguishing diseased and healthy plants. The chosen model was integrated with a low-volume sprayer (50‒150 L·ha^‒1), equipped with an electronic control unit for targeted spraying based on sensor-detected regions. The decision tree model achieved 89.9% testing accuracy, while the SVM model achieved 96.7% accuracy using hyperparameters: cost of 10.0 and tolerance of 0.001. The research successfully demonstrated the integration of spectral sensors, machine learning, and targeted spraying technology for precise input application. Additionally, the optimized sprayer achieved a 72.5% reduction in chemical usage and a significant time-saving of 21.0% compared to a standard sprayer for black rot-infested crops. These findings highlight the potential efficiency and resource conservation benefits of innovative sprayer technology in precision agriculture and disease management.

Graphical abstract

Keywords

Disease management / site-specific sprayer / spectral sensor / machine learning models / cauliflower crop / black-rot disease

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Acknowledgements

This research was supported by the Technology Development Programme of Department of Science and Technology, Government of India (DST/TDT/TDP-22/2022).

Compliance with ethics guidelines

Rohit Anand, Roaf Ahmad Parray, Indra Mani, Tapan Kumar Khura, Harilal Kushwaha, Brij Bihari Sharma, Susheel Sarkar, Samarath Godara, Shideh Mojerlou, and Hasan Mirzakhaninafchi declare that they have no conflicts of interest or financial conflicts to disclose. This article does not contain any studies with human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

The Author(s) 2024. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)