ICTJOURNAL_CH 31/03/21 Drones, 5G et machine learning pour réduire l’emploi de pesticides en Suisse. Cinq entreprises et institutions suisses associent leurs compétences pour développer une agriculture durable dans le pays.
Un objectif qui passe par l'exploration de données, le 5G et l’automatisation, ont annoncé lors d’un événement les partenaires: Agroscope, Fenaco, la Haute école spécialisée de Suisse orientale (FHO), Sunrise UPC et Huawei. Selon Alexander Lehrmann, responsable de l'innovation et du développement chez Sunrise UPC, l'agriculture intelligente pourrait, par exemple, augmenter la production de lait de 30% et réduire l'utilisation de pesticides jusqu'à 90%. Thomas Anken, Head of Digital Production chez Agroscope, a présenté le concept de traitement spécifique plante par plante. BIORXIV 09/03/21 Explainable machine learning models of major crop traits from satellite#monitored continent-wide field trial data.
INRIA 24/11/20 L'intelligence artificielle de PlantNet au service de la biodiversité. Cependant, au cours du premier projet de R&D, entre 2010 et 2014, d'autres collaborateurs viennent renforcer l’équipe.
Hervé Goëau, chercheur Cirad en science des données, lui aussi présent dès le départ, est ainsi rejoint par trois ingénieurs Inria : Julien Champ et Antoine Affouard, en 2012, puis Jean-Christophe Lombardo, en 2014. International Journal of Recent Technology and Engineering (IJRTE) - NOV 2019 - Deep Learning for Image Based Mango Leaf Disease Detection.
Forêt et intelligence artificielle. FRONT. ARTIF. INTELL. 30/11/20 Automatic Detection of Flavescence Dorée Symptoms Across White Grapevine Varieties Using Deep Learning. Introduction Flavescence dorée (FD) is a grapevine disease raising serious concern in Europe.
This disease is caused by several phytoplasmas classified according to their ribosomal DNA (16SrV subgroup C and D) (Filippin et al., 2009) and grouped under the temporary name of Candidatus Phytoplasma vitis (Firrao et al., 2004). The transmission of those phytoplasmas is mediated by infected leafhopper Scaphoideus titanus which transmit the disease when feeding on vine leaves. This leafhopper, native to North America, was first observed in France in 1958 (Bonfils and Schvester, 1960). Adult S. titanus have a limited dispersal distance, reaching 25–30 m, although winds can cause passive dispersal over larger areas. International Journal for Research in Applied Science & Engineering - OCT 2020 - Review of Machine Learning Techniques and Knowledge Analytics on Crop Production.
FORESTS 18/11/20 Integrating Neighborhood Effect and Supervised Machine Learning Techniques to Model and Simulate Forest Insect Outbreaks in British Columbia, Canada. Background and Objectives: Modelling and simulation of forest land cover change due to epidemic insect outbreaks are powerful tools that can be used in planning and preparing strategies for forest management.
In this study, we propose an integrative approach to model land cover changes at a provincial level, using as a study case the simulation of the spatiotemporal dynamics of mountain pine beetle (MPB) infestation over the lodgepole pine forest of British Columbia (BC), Canada. This paper aims to simulate land cover change by applying supervised machine learning techniques to maps of MPB-driven deforestation. Materials and Methods: We used a 16-year series (1999–2014) of spatial information on annual mortality of pine trees due to MPB attacks, provided by the BC Ministry of Forests. We used elevation, aspect, slope, ruggedness, and weighted neighborhood of infestation as predictors. SUSTAINABILITY 03/11/20 Artificial Intelligence Approach for Tomato Detection and Mass Estimation in Precision Agriculture. Application of computer vision and robotics in agriculture requires sufficient knowledge and understanding of the physical properties of the object of interest.
ISPRS Journal of Photogrammetry and Remote Sensing Volume 169, November 2020, Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin. 1.
Introduction. MOBILE INFORMATION SYSTEMS 10/07/19 Application of Deep Learning in Integrated Pest Management: A Real-Time System for Detection and Diagnosis of Oilseed Rape Pests. In this paper, we proposed an approach to detect oilseed rape pests based on deep learning, which improves the mean average precision (mAP) to 77.14%; the result increased by 9.7% with the original model.
We adopt this model to mobile platform to let every farmer able to use this program, which will diagnose pests in real time and provide suggestions on pest controlling. We designed an oilseed rape pest imaging database with 12 typical oilseed rape pests and compared the performance of five models, SSD w/Inception is chosen as the optimal model. Moreover, for the purpose of the high mAP, we have used data augmentation (DA) and added a dropout layer. The experiments are performed on the Android application we developed, and the result shows that our approach surpasses the original model obviously and is helpful for integrated pest management. 1. IRJET - JULY 2020 - MACHINE LEARNING IN CROP DISEASE DETECTION AND YIELD PREDICTION.
Résumé traduit : L'agriculture joue un rôle majeur dans l'économie indienne. La division de l'agriculture indienne fournit dix-huit pour cent du produit intérieur brut (PI😎 de l’Inde environ. En quelques années, réduction drastique de l'agriculture en Inde. La principale source de ces changements radicaux est le changement climatique et l'utilisation de technologies de bas niveau. Prédire le rendement de la récolte bien avant sa récolte et surveiller la récolte aiderait les agriculteurs. L'apprentissage automatique est une approche essentielle pour réaliser des des solutions efficaces à de nombreux problèmes agricoles. Dans cet article, nous discutons des problèmes de l'agriculture comme la détection des maladies des cultures et sa prédiction de rendement à l'aide d'algorithmes d'apprentissage automatique. Convolution algorithme de réseau neuronal (CNN) avec la fonction de traitement d'image intégrée est utilisé pour construire le modèle. Ce modèle est utilisé dans le processus de détection des maladies à l'aide de TensorFlow. Un autre modèle est construit en utilisant l'algorithme de forêt aléatoire afin de prédire le rendement des cultures. – guatemalt
REMOTE SENSING 25/04/20 Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning. Non-destructive crop monitoring over large areas with high efficiency is of great significance in precision agriculture and plant phenotyping, as well as decision making with regards to grain policy and food security.
SCIENTIFIC REPORTS 20/01/20 A Machine Learning Approach to Growth Direction Finding for Automated Planting of Bulbous Plants. An overview of our growth direction estimation algorithm was shown in Fig. 1 and consists of four main components: feature selection, 2D angle prediction, the 2D-to-3D mapping, and the filtering out of plant bulbs whose estimates disagree with each other.
Each of these algorithmic components are presented in detail below. Feature selection The first step in the growth direction estimation algorithm is to extract a small set of features from the x-ray projection images that can simplify the regression step between the images and the corresponding growth directions. We identify three such features of interest below. SCIENCE DAILY 20/02/20 New artificial intelligence algorithm better predicts corn yield. With some reports predicting the precision agriculture market will reach $12.9 billion by 2027, there is an increasing need to develop sophisticated data-analysis solutions that can guide management decisions in real time.
A new study from an interdisciplinary research group at University of Illinois offers a promising approach to efficiently and accurately process precision ag data. "We're trying to change how people run agronomic research. Instead of establishing a small field plot, running statistics, and publishing the means, what we're trying to do involves the farmer far more directly. We are running experiments with farmers' machinery in their own fields. We can detect site-specific responses to different inputs. He adds, "We developed methodology using deep learning to generate yield predictions. PLANTS 31/10/19 Plant Disease Detection and Classification by Deep Learning. The following approaches employed DL models/architectures and also visualization techniques which were introduced for a clearer understanding of plants’ diseases.
For example, [55] introduced the saliency map for visualizing the symptoms of plant disease; [27] identified 13 different types of plant disease with the help of CaffeNet CNN architecture, and achieved CA equal to 96.30%, which was better than the previous approach like SVM. Moreover, several filters were used to indicate the disease spots. Similarly, [25] used AlexNet and GoogLeNet CNN architectures by using the publicly available PlantVillage dataset. IOWA STATE UNIVERSITY 12/09/19 Machine learning in agriculture: ISU scientists are teaching computers to diagnose soybean stress.
Unmanned aerial vehicles could be equipped with hyperspectral technology capable of detecting wavelength ranges beyond those detectable by the human eye. Such technology could combine with machine learning techniques under development at Iowa State to help farmers anticipate stress among their crops before symptoms appear. Photo courtesy of Arti Singh. Larger image. AMES, Iowa – Iowa State University scientists are working toward a future in which farmers can use unmanned aircraft to spot, and even predict, disease and stress in their crops.
Their vision relies on machine learning, an automated process in which technology can help farmers respond to plant stress more efficiently. “At its most basic, machine learning is simply training a machine to do something we do,” Singh said. Potential to predict symptoms before they appear The research team has assembled an enormous dataset of soybean images, some healthy and some undergoing stress and disease, which they then labeled. International Journal of Scientific Research in Computer Science, Engineering and Information Technology - 2018 - Machine Learning based Early Detection of Red Palm Weevil using Remote Sensing Technology in Saudi Arabia. INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH 02/02/20 Crop Yield Prediction Using Machine Learning. International Journal of Computational Intelligence Research - 2018 - Agricultural Robot: Leaf Disease Detection and Monitoring the Field Condition Using Machine Learning and Image Processing.
Inaturalist_org 05/04/19 iNaturalist Computer Vision Explorations. One of our goals with iNaturalist is to provide a crowd-sourced species identification system. This means that if you post a photo of a species you don't recognize to iNaturalist, the community should tell you what you saw. On average observations take 18 days to be identified by the community, with half of all observations identified in the first 2 days. As iNaturalist grows, keeping this identification rate steady requires an ever increasing burden on a relatively small group of identifiers. Fortunately, there have been major advances in machine learning approaches like computer vision in the past few years that might help share the burden with identifiers.
Our goal is to integrate computer vision tools into iNaturalist to help the community provide higher quality identifications faster as iNaturalist continues to grow. I-PROGRAMMER 18/06/17 iNaturalist Launches Deep Learning-Based Identification App. iNaturalist.org has launched an app for Android and iOS that automatically identifies animals and plants at species level.
Trained using TensorFlow it already identifies over 10,000 different species with a new species added to the model every 1.7 hours. iNaturalist.org is an established and popular website. Its mission is to connect experts and amateur "citizen scientists," encouraging people to get interested and involved with the natural world while using the data gathered to potentially help professional scientists monitor changes in biodiversity or even discover new species. Founded in 2008 by students at University of California, Berkeley and recently acquired by the California Academy of Sciences, it used to rely on crowdsourcing. When users posted a photo of a plant or animal, its community of scientists and naturalists will identify it. The crowdsourced model generally works well according to Scott Loarie, iNaturalist's co-director. FOOD NAVIGATOR 14/08/19 Artificial intelligence app helps banana farmers detect TR4 disease.
The app can detect Fusarium wilt, Xanthomonas wilt, bunchy top disease, black sigatoka, yellow sigatoka, and corm weevil. Fusarium Tropical race 4 fungus (TR4) has decimated banana plantations and smallholders’ crops in Asia and Africa and has now spread to Latin America. Last week, Colombian officials officially confirmed the presence of TR4 in La Guajira province, declaring a state of national emergency as a result. Developed with support from Bioversity International and the International Center for Tropical Agriculture (CIAT), the AI-powered tool is built into an app called Tumaini – Swahili for ‘hope’ – that allows farmers to take action quickly, thus preventing a widespread outbreak.
EN_WIKIPEDIA - Inaturalist. iNaturalist is citizen science project and online social network of naturalists, citizen scientists, and biologists built on the concept of mapping and sharing observations of biodiversity across the globe.[2] Observations may be added via the website or from a mobile application.[3][4] The observations provide valuable open data to a variety of scientific research projects, museums, botanic gardens, parks, and other organizations.[5][6] .[7] Users of iNaturalist have contributed over one million observations since its founding in 2008.[8] History[edit] iNaturalist.org began in 2008 as a UC Berkeley School of Information Master's final project of Nate Agrin, Jessica Kline, and Ken-ichi Ueda.[1] Nate Agrin and Ken-ichi Ueda continued work on the site with Sean McGregor, a web developer.
In 2011, Ueda began collaboration with Scott Loarie, a research fellow at Stanford University and lecturer at UC Berkeley. Computers and Electronics in Agriculture Volume 170, March 2020 New perspectives on plant disease characterization based on deep learning. 1. Introduction A plant disease is an alteration of the original state of the plant that affects or modifies its vital functions. It is mainly caused by bacteria, fungi, microscopic animals or viruses, and has a strong impact on agricultural yields and on farm budget. According to the Food and Agriculture Organization of the United Nations, transboundary plant diseases have increased significantly in recent years due to globalization, trade, climate change and the reduction in the resilience of production systems due to decades of agricultural intensification. CGIAR 12/08/19 Artificial intelligence helps banana growers protect the world’s most favorite fruit.
Using artificial intelligence, scientists created an easy-to-use tool to detect banana diseases and pests. With an average 90 percent success rate in detecting a pest or a disease, the tool can help farmers avoid millions of dollars in losses Artificial intelligence-powered tools are rapidly becoming more accessible, including for people in the more remote corners of the globe. This is good news for smallholder farmers, who can use handheld technologies to run their farms more efficiently, linking them to markets, extension workers, satellite images, and climate information.
WIKIPEDIA - Pl@ntNet. Un article de Wikipédia, l'encyclopédie libre. Pl@ntNet est un projet informatique d'identification des plantes à partir de photographies par apprentissage automatique. Ce projet lancé en 2009 est l'œuvre de scientifiques (informaticiens et botanistes) d’un consortium regroupant des instituts de recherche français (Institut de recherche pour le développement (IRD), Centre de coopération internationale en recherche agronomique pour le développement (CIRAD), Institut national de la recherche agronomique (INRA), Institut national de recherche en informatique et en automatique (INRIA) et réseau Tela Botanica, avec le concours de la fondation Agropolis International[1]).
HDIGITAG 11/02/19 [Paroles de doctorants] Etienne David : Développement de méthodes d’apprentissage profond pour la caractérisation des cultures. Développement de méthodes d’apprentissage profond pour la caractérisation des cultures Date de démarrage : mai 2018Université : Avignon UniversitéEcole doctorale : A2E – ED 536 Agrosciences & SciencesDiscipline / Spécialité : Agronomie, InformatiqueDirecteur de thèse : Frédéric Baret, Inra EMMAH AvignonEncadrant(es) : Frédéric Baret et Samuel Thomas, Arvalis institut du végétalFinancement : Cifre Arvalis#DigitAg : Thèse labellisée – Axes 5 et 6 – Challenges 2 & 3 Mots-clés : Phénotypage haut-débit, données agricoles, grandes cultures, apprentissage profond, apprentissage automatique, intelligence artificielle, vision numérique, télédétection, proxi-détection.
CIRAD 15/11/19 Quand l’intelligence artificielle optimise la lutte biologique contre les ravageurs. Environ. Res. Lett - 2020 - Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest. BIORXIV - 2020 - Site-specific machine learning predictive fertilization models for potato crops in Eastern Canada. SUSTAINABILITY 17/02/20 Comparing Machine Learning Approaches for Predicting Spatially Explicit Life Cycle Global Warming and Eutrophication Impacts from Corn Production. Agriculture ranks as one of the top contributors to global warming and nutrient pollution. Quantifying life cycle environmental impacts from agricultural production serves as a scientific foundation for forming effective remediation strategies.
However, methods capable of accurately and efficiently calculating spatially explicit life cycle global warming (GW) and eutrophication (EU) impacts at the county scale over a geographic region are lacking. The objective of this study was to determine the most efficient and accurate model for estimating spatially explicit life cycle GW and EU impacts at the county scale, with corn production in the U.S.’s Midwest region as a case study. INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH 02/02/20 Crop Yield Prediction Using Machine Learning. International Journal of Scientific Research in Computer Science, Engineering and Information Technology - 2018 - Machine Learning based Early Detection of Red Palm Weevil using Remote Sensing Technology in Saudi Arabia.
RTS_ch 16/12/19 Le palmier: l’intelligence artificielle pour surveiller et combattre sa prolifération. KANSAS STATE UNIVERSITY - 2018 - Thèse en ligne: Applications of machine learning to agricultural land values: prediction and causal inference. Precision Agriculture June 2015, Volume 16, Issue 3, pp 239-260 A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. International Research Journal of Engineering and Technology - FEV 2017 - Crop Selection Method Based on Various Environmental Factors Using Machine Learning.
IERI Procedia Volume 6, 2014, Pages 52-56 Crop Pests Prediction Method Using Regression and Machine Learning Technology: Survey. JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Abstract. PENN STATE UNIVERSITY 04/10/16 Artificial intelligence could help farmers diagnose crop diseases. UNIVERSITY PARK, Pa. -- A network of computers fed a large image dataset can learn to recognize specific plant diseases with a high degree of accuracy, potentially paving the way for field-based crop-disease identification using smartphones, according to a team of researchers at Penn State and the Swiss Federal Institute of Technology (EPFL), in Lausanne, Switzerland.
The technology could have particular benefits for producers in developing countries, such as in sub-Saharan Africa, who often do not have the research infrastructure or agricultural extension systems to support smallholder farmers, the researchers said. SAN JOSE STATE UNIVERSITY - Thèse accessible à partir du 24/05/20 - MACHINE LEARNING IN CROP CLASSIFICATION OF TEMPORAL MULTISPECTRAL SATELLITE IMAGE. Abstract Recently, there has been a remarkable growth in Artificial Intelligence (AI) with. FOOD NAVIGATOR 14/08/19 Artificial intelligence app helps banana farmers detect TR4 disease. CIO-MAG 15/07/19 Cameroun : l’intelligence artificielle pour révolutionner l’agriculture.