Dynamics of agrarian landscapes in Western Thailand : Agro-ecological zonation and agricultural transformations in Kanjanaburi Province: hypotheses for improving farming systems sustainability

Ce document traite de la zonation agroécologique à petite échelle, comme outil essentiel dans la recherche orientée sur les systèmes agraires en vue du développement. Ces systèmes sont définis comme modes d'exploitation adaptés à l'environnement (naturel et humain) y compris échanges de produit et patrimoine culturel; l'étude comprend systèmes de production et de culture, et types d'utilisation des sols. Les diverses relations entre éléments sont analysées dans l'espace et le temps de façon à dégager la dynamique des transformations. Le projet a fait intervenir des équipes pluridisciplinaires comprenant agronomes et spécialistes des ressources naturelles en sociologie et télédétection; le tout aux niveaux de la parcelle et de l'exploitation agricole. Le texte, qui comporte un glossaire technique précis, est illustré de six clichés en couleurs (cultures de maïs, cotonnier, manioc, manguiers) et d'une image digitale en couleurs d'une partie de l'ouest de la Thaïlande vue du satellite Landsat-T

Personalized Food Image Classification: Benchmark Datasets and New Baseline

Food image classification is a fundamental step of image-based dietary assessment, enabling automated nutrient analysis from food images. Many current methods employ deep neural networks to train on generic food image datasets that do not reflect the dynamism of real-life food consumption patterns, in which food images appear sequentially over time, reflecting the progression of what an individual consumes. Personalized food classification aims to address this problem by training a deep neural network using food images that reflect the consumption pattern of each individual. However, this problem is under-explored and there is a lack of benchmark datasets with individualized food consumption patterns due to the difficulty in data collection. In this work, we first introduce two benchmark personalized datasets including the Food101-Personal, which is created based on surveys of daily dietary patterns from participants in the real world, and the VFNPersonal, which is developed based on a dietary study. In addition, we propose a new framework for personalized food image classification by leveraging self-supervised learning and temporal image feature information. Our method is evaluated on both benchmark datasets and shows improved performance compared to existing works. The dataset has been made available at: https://skynet.ecn.purdue.edu/~pan161/dataset_personal.htmlComment: Accepted by IEEE Asilomar conference (2023

DiDiMap. Diet Diary and Consumption Control for Monitoring Bowel Dysfunctions and low-FODMAP Diet App

The purpose of this project was to design and implement a mobile application for people with bowel dysfunctions, intolerances, and food allergies. The application was expected to provide all needed functionality for the target groups day to day challenges. Irritable bowel syndrome, intolerances, and food allergies affect a significant portion of the population. On a world basis, 15\% of the population are affected by IBS alone. Although intolerances and food allergies are handled better than before in terms of adaptation from restaurants, food producers, and grocery stores, there’s still a long way to go. Food producers and caterers must, by law, inform consumers of whether their products contain certain common allergens. If a person has an allergy or intolerance outside the standard, there’s little information to get. A systematic review and an app review mapping existing knowledge and implementations for similar apps were conducted. A mobile application was implemented for a low-FODMAP (Fermentable oligosaccharides, disaccharides, monosaccharides, and polyols) use case based on the conducted reviews features and shortcomings. The app contains features such as optical character recognition to identify potential trigger foods, barcode scanning of food products to retrieve nutritional and intolerance information, and a log to track meals, events during the day, and symptoms. The application also includes a communication platform for connecting and communicating with peers, which can later be expanded into discussion and motivation groups. Unlike other similar applications in the market, the app provides, in addition to peer communication, all needed functionality in a single platform, which enables utilization of log data for consumption control. We conducted a trial of the application with 65 users who were currently following a low-FODMAP diet. Of these 8 people responded to an anonymous survey asking users to rank the system's usability on a scale, and to answer a few application-specific questions. Feedback from user testing indicated a great interest in the app. Through the survey the app gained a system usability score of 85/100, and 75\% thought the app would greatly simplify the process of following the low-FODMAP diet

Calorie Estimation from Fast Food Images Using Support Vector Machine

Calorie estimation is one of the interesting area of research nowadays. The proposed model focuses on estimation of number of calories in the food item by just taking its image as input. Some image processing operations are performed on the image of food item followed by machine learning technique known as support vector machine(SVM). We took data from different resources [20] [21] [22], compile them to create our own dataset. Augmented dataset is used to train the SVM model and results show an accuracy of 90.66%. The experiments we performed conforms the feasibility of the proposed model