Deep learning-based decision support system for weeds detection in wheat fields

In precision farming, identifying weeds is an essential first step in planning an integrated pest management program in cereals. By knowing the species present, we can learn about the types of herbicides to use to control them, especially in non-weeding crops where mechanical methods that are not effective (tillage, hand weeding, and hoeing and mowing). Therefore, using the deep learning based on convolutional neural network (CNN) will help to automatically identify weeds and then an intelligent system comes to achieve a localized spraying of the herbicides avoiding their large-scale use, preserving the environment. In this article we propose a smart system based on object detection models, implemented on a Raspberry, seek to identify the presence of relevant objects (weeds) in an area (wheat crop) in real time and classify those objects for decision support including spot spray with a chosen herbicide in accordance to the weed detected

Accuracy and Efficiency Comparison of Object Detection Open-Source Models

In agriculture, weeds cause direct damage to the crop, and it primarily affects the crop yield potential. Manual and mechanical weeding methods consume a lot of energy and time and do not give efficient results. Chemical weed control is still the best way to control weeds. However, the widespread and large-scale use of herbicides is harmful to the environment. Our study's objective is to propose an efficient model for a smart system to detect weeds in crops in real-time using computer vision. Our experiment dataset contains images of two different weed species well known in our region strained in this region with a temperate climate. The first is the Phalaris Paradoxa. The second is Convolvulus, manually captured with a professional camera from fields under different lighting conditions (from morning to afternoon in sunny and cloudy weather). The detection of weed and crop has experimented with four recent pre-configured open-source computer vision models for object detection: Detectron2, EfficientDet, YOLO, and Faster R-CNN. The performance comparison of weed detection models is executed on the Open CV and Keras platform using python language

Accuracy and Efficiency Comparison of Object Detection Open-Source Models

In agriculture, weeds cause direct damage to the crop, and it primarily affects the crop yield potential. Manual and mechanical weeding methods consume a lot of energy and time and do not give efficient results. Chemical weed control is still the best way to control weeds. However, the widespread and large-scale use of herbicides is harmful to the environment. Our study's objective is to propose an efficient model for a smart system to detect weeds in crops in real-time using computer vision. Our experiment dataset contains images of two different weed species well known in our region strained in this region with a temperate climate. The first is the Phalaris Paradoxa. The second is Convolvulus, manually captured with a professional camera from fields under different lighting conditions (from morning to afternoon in sunny and cloudy weather). The detection of weed and crop has experimented with four recent pre-configured open-source computer vision models for object detection: Detectron2, EfficientDet, YOLO, and Faster R-CNN. The performance comparison of weed detection models is executed on the Open CV and Keras platform using python language

Developing an Efficient System with Mask R-CNN for Agricultural Applications

In order to meet the world's demand for food production, farmers and producers have improved and increased their agricultural production capabilities, leading to a profit acceleration in the field. However, this growth has also caused significant environmental damage due to the widespread use of herbicides. Weeds competing with crops result in lower crop yields and a 30% increase in losses. To rationalize the use of these herbicides, it would be more effective to detect the presence of weeds before application, allowing for the selection of the appropriate herbicide and application only in areas where weeds are present. The focus of this paper is to define a pipeline for detecting weeds in images through the use of a Mask R-CNN-based weed classification and segmentation module. The model was initially trained locally on our machine, but limitations and issues with training time prompted the team to switch to cloud solutions for training

A Strategic Analytics Using Convolutional Neural Networks for Weed Identification in Sugar Beet Fields

Researchers in precision agriculture regularly use deep learning that will help growers and farmers control and monitor crops during the growing season; these tools help to extract meaningful information from large-scale aerial images received from the field using several techniques in order to create a strategic analytics for making a decision. The information result of the operation could be exploited for many reasons, such as sub-plot specific weed control. Our focus in this paper is on weed identification and control in sugar beet fields, particularly the creation and optimization of a Convolutional Neural Networks model and train it according to our data set to predict and identify the most popular weed strains in the region of Beni Mellal, Morocco. All that could help select herbicides that work on the identified weeds, we explore the way of transfer learning approach to design the networks, and the famous library Tensorflow for deep learning models, and Keras which is a high-level API built on Tensorflow

La Responsabilité Sociale de l’Entreprise : entre Engagement Volontaire et Responsabilité Juridique

La RSE est-elle un choix volontaire ou obligatoire ? Il s’agit de la problématique principale que ce papier cherche à éclaircir en mettant en exergue ce phénomène dans ses dimensions managériales et juridiques.Les études précédentes révèlent le caractère volontaire de la RSE (soft low) comme des pratiques qui guident le comportement des organisations sur les plans social, économique, sociétal et envirommental, pour assurer, par conséquent, leur conformité et leur performance globale. Toutefois, quelques orientations de la recherche recommandent le recours aux obligations contraignantes et répressives (hard low) pour mieux pousser les entreprises vers l’engagement et la conformité à l’égard de différents acteurs et partenaires. Ainsi, de nombreuses dispositions restent encore facultatives pour les entreprises et d’autres ne prévoient aucune sanction applicable en cas de non-respect de celles-ci. C’est pourquoi, la recherche d’une complémentarité semble nécessaire entre « soft law » et « hard law ». Le contexte marocain révèle ainsi cette nécessité, car la mise en œuvre de la responsabilité sociale des entreprises se heurte à un certain nombre d’obstacles imputables aux fondements même de droit de la responsabilité qui se semble inadapté à l’organisation des entreprises et à leurs activités.

Digital Farming: A Survey on IoT-based Cattle Monitoring Systems and Dashboards

There is a steady increase in research on livestock monitoring systems that offer new ways to remotely track the health of the livestock, early predict the diseases that may affect them and intervene in the early stages to save the situation by monitoring the various vital biodata of the livestock, as well as monitoring their feeding and tracking their location to prevent any damage or rustling. In this context, this paper comes in order to highlight and discuss the most recently published articles that study the topic of cattle health monitoring and location tracking systems using advanced IoT sensors. In addition, the research provides a review of the most important software and dashboards available in the market that can be used for this purpose. The research constitutes a reference for researchers in this field and for those who wish to develop similar monitoring systems

A Model Proposal for Enhancing Leaf Disease Detection Using Convolutional Neural Networks (CNN): Case Study

Deep learning has gained significant popularity due to its exceptional performance in various machine learning and artificial intelligence applications. In this paper, we propose a comprehensive methodology for enhancing leaf disease detection using Convolutional Neural Networks (CNNs). Our approach leverages the power of CNNs and introduces innovative techniques to improve accuracy and provide insights into the inner workings of the models. The methodology encompasses multiple stages. We describe the methodology as follows: Firstly, we employ advanced preprocessing techniques to enhance the leaf image dataset, including data augmentation methods to augment the training data and improve model accuracy. Secondly, we design and implement a robust Convolutional Neural Network architecture with multiple layers and ReLU activation, enabling the network to effectively learn complex patterns and features from the input images. To facilitate monitoring and control of the CNN processes, we introduce a novel network visualization module. This module offers a filter-level 2D embedding view, providing real-time insights into the inner workings of the network and aiding in the interpretation of the learned features. Additionally, we develop an interactive module that enables real-time model control, allowing researchers and practitioners to fine-tune the model parameters and optimize its performance. To evaluate the effectiveness of our proposed methodology, we conduct extensive experiments using the PlantVillage dataset, which contains a diverse range of plant diseases captured through a large number of leaf images. Through rigorous analysis and evaluation, we demonstrate the superior performance of our approach, achieving classification accuracy exceeding 99%

A Connected farm Metamodeling Using Advanced Information Technologies for an Agriculture 4.0

The agriculture 4.0 revolution is an opportunity for farmers to meet the challenges in food production. It has become necessary to adopt a set of agricultural practices based on advanced technologies following the agriculture 4.0 revolution. This latter enables the creation of added value by combining innovative technologies: precision agriculture, information and communication technology, robotics, and Big Data. As an enterprise, a connected farm is also highly sensitive to strategic changes like organizational changes, changes in objectives, modified variety, new business objects, processes, etc. To strategically control its information system, we propose a metamodeling approach based on the ISO/IS 19440 enterprise meta-model, where we added some new constructs relating to new advanced digital technologies for Smart and Connected agriculture