Multiclass insect counting through deep learning-based density maps estimation

The use of digital technologies and artificial intelligence techniques for the automation of some visual assessment processes in agriculture is currently a reality. Image-based, and recently deep learning-based systems are being used in several applications. Main challenge of these applications is to achieve a correct performance in real field conditions over images that are usually acquired with mobile devices and thus offer limited quality. Plagues control is a problem to be tackled in the field. Pest management strategies relies on the identification of the level of infestation. This degree of infestation is established through a counting task manually done by the field researcher so far. Current models were not able to appropriately count due to the small size of the insects and on the last year we presented a density map based algorithm that superseded state of the art methods for a single insect type. In this paper, we extend previous work into a multiclass and multi-stadia approach. Concretely, the proposed algorithm has been tested in two use cases: on the one hand, it counts five different types of adult individuals over multiple crop leaves; and on the other hand, it identifies four different stages for immatures over 2-cm leaf disks. In these leaf disks, some of the species are in different stadia being some of them micron size and difficult to be identified even for the non-expert user. The proposed method achieves good results in both cases. The model for counting adult insects in a leaf achieves a RMSE ranging from 0.89 to 4.47, MAE ranging from 0.40 to 2.15, and R2 ranging from 0.86 to 0.91 for 4 different species in its adult phase (BEMITA, FRANOC, MYZUPE and APHIGO) that may appear together in the same leaf. Besides, for FRANOC, two stadia nymphs and adults are considered. The model developed for counting BEMITA immatures in 2-cm disks obtains R2 values up to 0.98 for big nymphs. This solution was embedded in a docker and can be accessed through an app via REST service in mobile devices. It has been tested in the wild under real conditions in different locations worldwide and over 14 different crops.The authors would like to thank all field researchers that generated the dataset, carried out the annotation process, performed the validation in the wild, and in general, supported the work in Tecnalia and BASF specially to Javier Romero, Carlos Javier Jim ́enez, Amaia Ortiz, Aitor Alvarez and Jone Echazarra

Deep learning-based segmentation of multiple species of weeds and corn crop using synthetic and real image datasets

Weeds compete with productive crops for soil, nutrients and sunlight and are therefore a major contributor to crop yield loss, which is why safer and more effective herbicide products are continually being developed. Digital evaluation tools to automate and homogenize field measurements are of vital importance to accelerate their development. However, the development of these tools requires the generation of semantic segmentation datasets, which is a complex, time-consuming and not easily affordable task. In this paper, we present a deep learning segmentation model that is able to distinguish between different plant species at the pixel level. First, we have generated three extensive datasets targeting one crop species (Zea mays), three grass species (Setaria verticillata, Digitaria sanguinalis, Echinochloa crus-galli) and three broadleaf species (Abutilon theophrasti, Chenopodium albums, Amaranthus retroflexus). The first dataset consists of real field images that were manually annotated. The second dataset is composed of images of plots where only one species is present at a time and the third type of dataset was synthetically generated from images of individual plants mimicking the distribution of real field images. Second, we have proposed a semantic segmentation architecture by extending a PSPNet architecture with an auxiliary classification loss to aid model convergence. Our results show that the network performance increases when supplementing the real field image dataset with the other types of datasets without increasing the manual annotation effort. More specifically, the use of the real field dataset obtains a Dice-Søensen Coefficient (DSC) score of 25.32. This performance increases when this dataset is combined with the single-species class dataset (DSC=47.97) or the synthetic dataset (DSC=45.20). As for the proposed model, the ablation method shows that by removing the proposed auxiliary classification loss, the segmentation performance decreases (DSC=45.96) compared to the proposed architecture method (DSC=47.97). The proposed method shows better performance than the current state of the art. In addition, the use of proposed single-species or synthetic datasets can double the performance of the algorithm than when using real datasets without additional manual annotation effort.We would like to thank BASF technicians Rainer Oberst, Gerd Kraemer, Hikal Gad, Javier Romero and Juan Manuel Contreras, as well as Amaia Ortiz-Barredo from Neiker for their support in the design of the experiments and the generation of the data sets used in this work. This was partially supported by the Basque Government through ELKARTEK project BASQNET(ref K-2021/00014)

Analysis of Few-Shot Techniques for Fungal Plant Disease Classification and Evaluation of Clustering Capabilities Over Real Datasets

Plant fungal diseases are one of the most important causes of crop yield losses. Therefore, plant disease identification algorithms have been seen as a useful tool to detect them at early stages to mitigate their effects. Although deep-learning based algorithms can achieve high detection accuracies, they require large and manually annotated image datasets that is not always accessible, specially for rare and new diseases. This study focuses on the development of a plant disease detection algorithm and strategy requiring few plant images (Few-shot learning algorithm). We extend previous work by using a novel challenging dataset containing more than 100,000 images. This dataset includes images of leaves, panicles and stems of five different crops (barley, corn, rape seed, rice, and wheat) for a total of 17 different diseases, where each disease is shown at different disease stages. In this study, we propose a deep metric learning based method to extract latent space representations from plant diseases with just few images by means of a Siamese network and triplet loss function. This enhances previous methods that require a support dataset containing a high number of annotated images to perform metric learning and few-shot classification. The proposed method was compared over a traditional network that was trained with the cross-entropy loss function. Exhaustive experiments have been performed for validating and measuring the benefits of metric learning techniques over classical methods. Results show that the features extracted by the metric learning based approach present better discriminative and clustering properties. Davis-Bouldin index and Silhouette score values have shown that triplet loss network improves the clustering properties with respect to the categorical-cross entropy loss. Overall, triplet loss approach improves the DB index value by 22.7% and Silhouette score value by 166.7% compared to the categorical cross-entropy loss model. Moreover, the F-score parameter obtained from the Siamese network with the triplet loss performs better than classical approaches when there are few images for training, obtaining a 6% improvement in the F-score mean value. Siamese networks with triplet loss have improved the ability to learn different plant diseases using few images of each class. These networks based on metric learning techniques improve clustering and classification results over traditional categorical cross-entropy loss networks for plant disease identification.This project was partially supported by the Spanish Government through CDTI Centro para el Desarrollo Tecnológico e Industrial project AI4ES (ref CER-20211030), by the University of the Basque Country (UPV/EHU) under grant COLAB20/01 and by the Basque Government through grant IT1229-19

Deep convolutional neural network for damaged vegetation segmentation from RGB images based on virtual NIR-channel estimation

Performing accurate and automated semantic segmentation of vegetation is a first algorithmic step towards more complex models that can extract accurate biological information on crop health, weed presence and phenological state, among others. Traditionally, models based on normalized difference vegetation index (NDVI), near infrared channel (NIR) or RGB have been a good indicator of vegetation presence. However, these methods are not suitable for accurately segmenting vegetation showing damage, which precludes their use for downstream phenotyping algorithms. In this paper, we propose a comprehensive method for robust vegetation segmentation in RGB images that can cope with damaged vegetation. The method consists of a first regression convolutional neural network to estimate a virtual NIR channel from an RGB image. Second, we compute two newly proposed vegetation indices from this estimated virtual NIR: the infrared-dark channel subtraction (IDCS) and infrared-dark channel ratio (IDCR) indices. Finally, both the RGB image and the estimated indices are fed into a semantic segmentation deep convolutional neural network to train a model to segment vegetation regardless of damage or condition. The model was tested on 84 plots containing thirteen vegetation species showing different degrees of damage and acquired over 28 days. The results show that the best segmentation is obtained when the input image is augmented with the proposed virtual NIR channel (F1=0.94) and with the proposed IDCR and IDCS vegetation indices (F1=0.95) derived from the estimated NIR channel, while the use of only the image or RGB indices lead to inferior performance (RGB(F1=0.90) NIR(F1=0.82) or NDVI(F1=0.89) channel). The proposed method provides an end-to-end land cover map segmentation method directly from simple RGB images and has been successfully validated in real field conditions

Analysis of Few-Shot Techniques for Fungal Plant Disease Classification and Evaluation of Clustering Capabilities Over Real Datasets

[EN] Plant fungal diseases are one of the most important causes of crop yield losses. Therefore, plant disease identification algorithms have been seen as a useful tool to detect them at early stages to mitigate their effects. Although deep-learning based algorithms can achieve high detection accuracies, they require large and manually annotated image datasets that is not always accessible, specially for rare and new diseases. This study focuses on the development of a plant disease detection algorithm and strategy requiring few plant images (Few-shot learning algorithm). We extend previous work by using a novel challenging dataset containing more than 100,000 images. This dataset includes images of leaves, panicles and stems of five different crops (barley, corn, rape seed, rice, and wheat) for a total of 17 different diseases, where each disease is shown at different disease stages. In this study, we propose a deep metric learning based method to extract latent space representations from plant diseases with just few images by means of a Siamese network and triplet loss function. This enhances previous methods that require a support dataset containing a high number of annotated images to perform metric learning and few-shot classification. The proposed method was compared over a traditional network that was trained with the cross-entropy loss function. Exhaustive experiments have been performed for validating and measuring the benefits of metric learning techniques over classical methods. Results show that the features extracted by the metric learning based approach present better discriminative and clustering properties. Davis-Bouldin index and Silhouette score values have shown that triplet loss network improves the clustering properties with respect to the categorical-cross entropy loss. Overall, triplet loss approach improves the DB index value by 22.7% and Silhouette score value by 166.7% compared to the categorical cross-entropy loss model. Moreover, the F-score parameter obtained from the Siamese network with the triplet loss performs better than classical approaches when there are few images for training, obtaining a 6% improvement in the F-score mean value. Siamese networks with triplet loss have improved the ability to learn different plant diseases using few images of each class. These networks based on metric learning techniques improve clustering and classification results over traditional categorical cross-entropy loss networks for plant disease identification.This project was partially supported by the Spanish Government through CDTI Centro para el Desarrollo Tecnológico e Industrial project AI4ES (ref CER-20211030), by the University of the Basque Country (UPV/EHU) under grant COLAB20/01 and by the Basque Government through grant IT1229-19

Impacts of soilfaunal communities on above-ground biota

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Wanted! Daniel:a Düsentrieb - MINT oder nicht MINT? Das ist hier die Frage

Im Rahmen des Digitaltages haben wir mit Studierenden, Eltern, Führungskräften und vielen anderen über Karriereentscheidungen für und gegen MINT-Berufe diskutiert. Dabei kristallisierten sich fünf verschiedene Bereiche heraus: • Eigenwahrnehmung junger Menschen • Schule • Eltern • Berufliche Aussichten • Politische und privatwirtschaftliche Initiativen Aus den jeweiligen Feldern ergeben sich verschiedene Herausforderungen, aber auch Chancen. So können Kinder aller Geschlechter schon jung erfahren, dass MINT-Kompetenzen das Handwerkszeug zur Lösung von Problemen in allen Lebensbereichen sind. Mit Unterstützung toleranter Eltern, aufmerksamer Lehrenden, spannenden Aufgabenstellungen in der Schule und einem transparenten Verständnis von beruflichen Chancen können wir junge Männer und Frauen für einen individuellen Karrierepfad im MINT-Bereich begeistern. Gesamtgesellschaftlich heben wir dadurch ungenutzte Potenziale und sind auch in Zukunft gewappnet für die Herausforderungen der digitalen Technologien

Corona und Karrieren – Push oder Rückzug für Frauen?

Die Unterschiede zwischen den Geschlechtern in der Arbeitswelt sind z.T. noch gravierend und könnten sich zukünftig weiter zuspitzen. Corona als Brennglas für Digitalisierung und Flexibilisierung kann aber auch zu einem Push beruflicher Chancen führen. Voraussetzungen sind in den Unternehmen v.a. flexible Arbeits- und Organisationsstrukturen, Ergebnis- statt Präsenzkultur, Wertschätzung von Familienarbeit und der dadurch entwickelten Kompetenzen sowie ein gelebtes Gemeinschaftsgefühl innerhalb der Belegschaft; seitens der Mitarbeiter:innen die individuelle Bereitschaft zu flexiblen Arbeitsformen sowie die Identifikation mit dem Unternehmen. Unterstützen können zusätzlich kreative Ideen oder gezielte Maßnahmen seitens der Unternehmen

Insect counting through deep learning-based density maps estimation

Digitalization and automation of assessments in field trials are established practice for farming product development. The use of image-based methods has provided good results in different applications. Although these models can leverage some problems, they still perform poorly under real field conditions using mobile devices on complex applications. Among these applications, insect counting and detection is necessary for integrated pest management strategies in order to apply specific treatments at early infection stages to reduce economic losses and minimize chemical usage. Currently the counting task for the assessment of the degree of infestation is done manually by the farmer. Current state of the art object counting methods do not provide accurate counting in crowded images with overlapped or touching objects which is the case for insect counting images. This makes necessary to define novel approaches for insect counting. In this work, we propose a novel solution based on deep learning density map estimation to tackle insects counting in wild conditions. To this end, a fully convolutional regression network has been designed to accurately estimate a probabilistic density map for the counting regression problem. The estimated density map is then used for counting whiteflies in eggplant leaves. The proposed method was compared with a baseline based on candidate object selection and classification approach. The results for alive adult whitefly counting by means of density map estimation provided R2 = 0.97 for the counted insects in the main leaf of the image, that outperforms by far the baseline algorithm (R2 = 0.85) based on image processing methods for feature extraction and candidate selection and deep learning-based classifier. This solution was embedded to be used in mobile devices, and it has been gone for exhaustive validation tests, with diverse illumination conditions and background variability, over leaves taken at different heights, with different perspectives and even unfocused images, for the analyzed pest under real conditions

Life-history trade-offs mediate ‘personality’ variation in two colour morphs of the pea aphid, Acyrthosiphon pisum

Life‐history trade‐offs are considered a major driving force in the emergence of consistent behavioural differences (personality variation); but empirical tests are scarce. We investigated links between a personality trait (escape response), life‐history and state variables (growth rate, size and age at first reproduction, age‐dependent reproductive rates, lifetime reproductive success, life span) in red and green colour morphs of clonal pea aphids, Acyrthosiphon pisum. Escape response (dropping/non‐dropping off a plant upon a predatory attack) was measured repeatedly to classify individuals as consistent droppers, consistent nondroppers or inconsistents. Red morphs experienced stronger trade‐offs between early reproduction and life span than green morphs; and red consistent (non)droppers had highest lifetime reproductive success. Red droppers followed a risk‐averse life‐history strategy (high late reproduction), red nondroppers a risk‐prone strategy (high early reproduction), while reproductive rates were equivalent for all green behavioural types and red inconsistents. This suggests that red morphs suffer the highest costs of dropping (they are most conspicuous to predators), which ‘equivalates’ fitness payoffs to both risk‐takers (red non‐droppers) and risk‐averse red droppers. The strong trade‐off also means that committing to a particular lifestyle (being consistent) maximises fitness. Our study suggests that life‐history trade‐offs likely mediate personality variation but effects might depend on interactions with other organismal characteristics (here: colour morph)