Browsing by Keyword "Crop protection"
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Item Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions(2019-12) Picon, Artzai; Seitz, Maximiliam; Alvarez-Gila, Aitor; Mohnke, Patrick; Ortiz-Barredo, Amaia; Echazarra, Jone; Tecnalia Research & Innovation; COMPUTER_VISION; VISUALConvolutional Neural Networks (CNN) have demonstrated their capabilities on the agronomical field, especially for plant visual symptoms assessment. As these models grow both in the number of training images and in the number of supported crops and diseases, there exist the dichotomy of (1) generating smaller models for specific crop or, (2) to generate a unique multi-crop model in a much more complex task (especially at early disease stages) but with the benefit of the entire multiple crop image dataset variability to enrich image feature description learning. In this work we first introduce a challenging dataset of more than one hundred-thousand images taken by cell phone in real field wild conditions. This dataset contains almost equally distributed disease stages of seventeen diseases and five crops (wheat, barley, corn, rice and rape-seed) where several diseases can be present on the same picture. When applying existing state of the art deep neural network methods to validate the two hypothesised approaches, we obtained a balanced accuracy (BAC=0.92) when generating the smaller crop specific models and a balanced accuracy (BAC=0.93) when generating a single multi-crop model. In this work, we propose three different CNN architectures that incorporate contextual non-image meta-data such as crop information onto an image based Convolutional Neural Network. This combines the advantages of simultaneously learning from the entire multi-crop dataset while reducing the complexity of the disease classification tasks. The crop-conditional plant disease classification network that incorporates the contextual information by concatenation at the embedding vector level obtains a balanced accuracy of 0.98 improving all previous methods and removing 71% of the miss-classifications of the former methods.Item Project LIFE-FITOVID-Implementation of Demonstrative & Innovative Strategies to reduce the use of plant protection products in viticulture(Julius-Kühn-Archiv, 2015-07-15) Gil, Emilio; Gallart, M.; Picon, Artzai; Echazarra, Jone; Barrio, R.; Sampedro, C.; Barranco, A.; Ranieri, Silvia; Ortiz, A.; Diez-Navajas, A.M.Pesticides are used in viticulture to obtain quality grapes and high worthwhile productions. They are essential to maintain a good prophylaxis in vineyard. Among them, fungicides are the more applied chemical compounds to control fungal pathogens causing devastating diseases, as the case for grapevine downy and powdery mildews. These diseases are more difficult to control in endemic areas, as every growing season appear and are reiteratively treated along it. This fact generates resistance in pathogens, supposing a higher dose for future treatments and more aggressive fungicides; to more exposition of growers to these compounds, decreasing their quality of life because of the latent risks; to an increasing presence of toxic molecules in grape, must and wine, making possible the presence of them in humans by consumption of these products; to affect negatively the surrounding environment, by soil and water flows pollution.