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Oscar Leonardo García-Navarrete, Adriana Correa-Guimaraes and Luis Manuel Navas-Gracia
Weeds are unwanted and invasive plants that proliferate and compete for resources such as space, water, nutrients, and sunlight, affecting the quality and productivity of the desired crops. Weed detection is crucial for the application of precision agric...
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Ana Corceiro, Nuno Pereira, Khadijeh Alibabaei and Pedro D. Gaspar
The global population?s rapid growth necessitates a 70% increase in agricultural production, posing challenges exacerbated by weed infestation and herbicide drawbacks. To address this, machine learning (ML) models, particularly convolutional neural netwo...
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L. G. Divyanth, D. S. Guru, Peeyush Soni, Rajendra Machavaram, Mohammad Nadimi and Jitendra Paliwal
Applications of deep-learning models in machine visions for crop/weed identification have remarkably upgraded the authenticity of precise weed management. However, compelling data are required to obtain the desired result from this highly data-driven ope...
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Cesare Di Girolamo-Neto, Ieda Del?Arco Sanches, Alana Kasahara Neves, Victor Hugo Rohden Prudente, Thales Sehn Körting, Michelle Cristina Araujo Picoli and Luiz Eduardo Oliveira e Cruz de Aragão
Sugarcane products contribute significantly to the Brazilian economy, generating U.S. $12.2 billion in revenue in 2018. Identifying and monitoring factors that induce yield reduction, such as weed occurrence, is thus imperative. The detection of Bermudag...
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Xinle Zhang, Jian Cui, Huanjun Liu, Yongqi Han, Hongfu Ai, Chang Dong, Jiaru Zhang and Yunxiang Chu
Soybean in the field has a wide range of intermixed weed species and a complex distribution status, and the weed identification rate of traditional methods is low. Therefore, a weed identification method is proposed based on the optimized Faster R-CNN al...
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