Abstract
Family farming represents a critical segment of Brazilian agriculture, involving more than 5 million properties and generating 74% of rural jobs in the country. Yield losses caused by crop diseases and pests can be devastating for small-scale producers. However, successful disease control requires correct identification, which challenges smallholders, who often lack technical assistance. The present work proposes a system that detects disease symptoms in images of plant leaves to assist phytopathology experts. The objective is to decrease the experts’ workload and enable consulting services for free or at nominal cost. In addition, the required digital communication channel will promote the formation of a caring community ready to offer unpaid advice for family farmers. The machine learning and refinement of the assistance system are described in detail. The developed classification system achieves a recall value of 95%.
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Acknowledgments
This work was supported by an A.I. for Earth Microsoft Azure Compute Grant. M.B. would like to thank CAPES for the master’s degree scholarship. S.B. is a CNPq fellow. The authors also thank the experts from CliFiPe, especially Dr. Jonas Alberto Rios and Dr. André Angelo Medeiros Gomes, for helpful discussion on the working of a crop clinic.
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Barros, M.d.S. et al. (2021). Supervised Training of a Simple Digital Assistant for a Free Crop Clinic. In: Britto, A., Valdivia Delgado, K. (eds) Intelligent Systems. BRACIS 2021. Lecture Notes in Computer Science(), vol 13074. Springer, Cham. https://doi.org/10.1007/978-3-030-91699-2_12
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DOI: https://doi.org/10.1007/978-3-030-91699-2_12
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