Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions

Convolutional 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

Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild

Fungal infection represents up to 50% of yield losses, making it necessary to apply effective and cost efficient fungicide treatments, whose efficacy depends on infestation type, situation and time. In these cases, a correct and early identification of the specific infection is mandatory to minimize yield losses and increase the efficacy and efficiency of the treatments. Over the last years, a number of image analysis-based methodologies have been proposed for automatic image disease identification. Among these methods, the use of Deep Convolutional Neural Networks (CNNs) has proven tremendously successful for different visual classification tasks. In this work we extend previous work by Johannes et al. (2017) with an adapted Deep Residual Neural Network-based algorithm to deal with the detection of multiple plant diseases in real acquisition conditions where different adaptions for early disease detection have been proposed. This work analyses the performance of early identification of three relevant European endemic wheat diseases: Septoria (Septoria triciti), Tan Spot (Drechslera triciti-repentis) and Rust (Puccinia striiformis & Puccinia recondita)

Deep Learning-Based Method for Accurate Real-Time Seed Detection in Glass Bottle Manufacturing

Glass bottle-manufacturing companies produce bottles of different colors, shapes and sizes. One identified problem is that seeds appear in the bottle mainly due to the temperature and parameters of the oven. This paper presents a new system capable of detecting seeds of 0.1 mm2 in size in glass bottles as they are being manufactured, 24 h per day and 7 days per week. The bottles move along the conveyor belt at 50 m/min, at a production rate of 250 bottles/min. This new proposed method includes deep learning-based artificial intelligence techniques and classical image processing on images acquired with a high-speed line camera. The algorithm comprises three stages. First, the bottle is identified in the input image. Next, an algorithm based in thresholding and morphological operations is applied on this bottle region to locate potential candidates for seeds. Finally, a deep learning-based model can classify whether the proposed candidates are real seeds or not. This method manages to filter out most of false positives due to stains in the glass surface, while no real seeds are lost. The F1 achieved is 0.97. This method reveals the advantages of deep learning techniques for problems where classical image processing algorithms are not sufficient.This work was partially supported by OPENZDM project. This is a project from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No. 101058673 in the call HORIZON-CL4-2021-TWIN-TRANSITION-0

Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case

Disease diagnosis based on the detection of early symptoms is a usual threshold taken into account for integrated pest management strategies. Early phytosanitary treatment minimizes yield losses and increases the efficacy and efficiency of the treatments. However, the appearance of new diseases associated to new resistant crop variants complicates their early identification delaying the application of the appropriate corrective actions. The use of image based automated identification systems can leverage early detection of diseases among farmers and technicians but they perform poorly under real field conditions using mobile devices. A novel image processing algorithm based on candidate hot-spot detection in combination with statistical inference methods is proposed to tackle disease identification in wild conditions. This work analyses the performance of early identification of three European endemic wheat diseases – septoria, rust and tan spot. The analysis was done using 7 mobile devices and more than 3500 images captured in two pilot sites in Spain and Germany during 2014, 2015 and 2016. Obtained results reveal AuC (Area under the Receiver Operating Characteristic –ROC– Curve) metrics higher than 0.80 for all the analyzed diseases on the pilot tests under real conditions

¿POR QUÉ LAS TÉCNICAS DE APRENDIZAJE PROFUNDO SUPERAN A LAS TÉCNICAS DE APRENDIZAJE AUTOMÁTICO TRADICIONALES?

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Project LIFE-FITOVID-Implementation of Demonstrative & Innovative Strategies to reduce the use of plant protection products in viticulture

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.LIFE PROGRA

Lattices and automorphisms of compact complex manifolds

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN028446 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Project LIFE-FITOVID-Implementation of Demostrative & Innovative Strategies to reduce the use of plant protection products in viticulture

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 mildew