Orbital multispectral imaging: a tool for discriminating management strategies for nematodes in coffee - Orlando, Vinicius Silva Werneck [UNESP]; Vieira, Bruno Sérgio; Martins, George Deroco; Lopes, Everaldo Antônio; Assis, Gleice Aparecida de; Pereira, Fernando Vasconcelos; Galo, Maria de Lourdes Bueno Trindade [UNESP]; Rodrigues, Leidiane da Silva (Springer Nature, 2024)
Background: Remote sensing based on multispectral imaging may be useful for detecting vegetation stress responses in agriculture. Objectives: To evaluate the potential of orbital multispectral imaging in discriminating the most effective strategies for reducing plant-parasitic nematode populations, thereby preventing yield losses in coffee production. Methods: Coffee plants were treated with eleven treatments, including Bacillus spp. isolates, commercial biological products, commercial chemical nematicides, and water (control group). Initial and final nematode populations in the soil were quantified, and surface reflectance data were collected using the Planet orbital multispectral sensor. The data were classified using the random tree algorithm. Results: The population of plant-parasitic nematodes was reduced by 35.90% and 55.13% following the application of B. amyloliquefaciens isolate B266 and B. subtilis isolate B33, respectively. Under the conditions of this experiment, multispectral imaging accurately discriminated the most nematicidal treatments, with a global accuracy of 80%. Conclusions: Orbital multispectral imaging can discriminate the most effective treatments used for nematode management in coffee plants, highlighting its potential as a supportive tool in agriculture.
Keywords: Bacillus spp; Biological control; Machine learning; Pest management; Remote sensing