| S.No |
Name of the project |
Abstract |
| 1 |
COOK BUDDY |
Cook Buddy is a comprehensive recipe app designed to revolutionize the culinary experience for users of all skill levels. With an intuitive interface and an extensive database of diverse recipes, this app serves as a digital companion for aspiring home chefs, seasonedcooks, and anyone eager to explore the realm of gastronomy. Our app boasts a diverse collection of recipes ranging from quick and easy weekday meals to elaborate gourmet creations. Users can explore recipes from various cuisines, dietary preferences, and cooking techniques. Each recipe is accompanied by detailed instructions, cook lists, andstep-by-step tutorials, empowering users to recreate dishes with ease. Users can also engage with interactive features such as timers, conversion tools, and cooking tips for a seamless cooking experience. Users can add their own recipes to the community which makes the scope and the resources of the app wider. This can be achieved after the userbecomes culmination by sending culmination request. Moreover users can save the recipes which they like for future reference. The admin have all the access for the recipes in the system, the admin can add, edit, delete and edit an recipe, he can also make can recipe active and inactive and the recipes accepted the admin alone will be displayed in the application. |
| 2 |
INTELLIGENT PLANT HEALTH |
Plant disease detection is a crucial task in agriculture, as it can help prevent significant crop losses caused by diseases. Machine learning has emerged as a promising solution for this problem. In this paper, we present an abstract of the state-of-the-art techniques for plant disease detection using machine learning. We start by introducing the challenges associated with plant disease detection, including the high dimensionality of image data, the need for large datasets, and the requirement for accurate labelling. Overall, this abstract highlight the potential of machine learning in plant disease detection and its importance for sustainable agriculture., we present the results of recent studies that have used machine learning for plant disease detection and discuss their limitations and potential future directions. Next, we discuss the various machine learning algorithms used for plant disease detection, including convolutional neural networks (CNNs), support vector machines (SVMs), and decision trees. We then discuss the importance of data augmentation and transfer the potential of machine learning in plant disease detection and its importance for sustainable agriculture. Identification of the plant diseases is the key to preventing the losses in the yield and quantity of the agricultural product. |
| 3 |
EXPERIMENTAL STUDY OF PERFORMANCE IMPROVEMENT OF SOLAR BUBBLE DRYER FOR AGRICULTURAL PRODUCTS |
The project “Experimental Study of Performance Improvement of Solar Bubble Dryer for Agricultural Products” aims to revolutionize solar drying technology in agriculture. Our goal is to enhance both efficiency and affordability. Through innovative redesign and optimization strategies, we aim to boost drying efficiency by 30% while cutting costs compared to current market options. We prioritize reducing power consumption, measured in watt- hours, for sustainability and cost-effectiveness. Using interdisciplinary methods and rigorous experimentation, we seek to develop a cost-effective solution for efficient agricultural product preservation. This initiative has the potential to positively impact sustainable food processing practices, reducing food loss and improving access to high- quality preserved agricultural products. Through collaboration and knowledge sharing, we aim to encourage the wider adoption of solar drying technology, contributing to a more sustainable agricultural sector. We are excited about the prospect of leading this innovative project. By leveraging our knowledge and skills, we aim to overcome technical challenges and achieve significant advancements in solar drying technology. Through collaboration with industry partners and stakeholders, we plan to gain valuable insights and ensure the practical applicability of our solutions. By disseminating our findings through publications and presentations, we hope to inspire future research and foster continued innovation in sustainable agricultural practices. |
| 4 |
PLANT DISEASE DETECTION |
Agriculture field has a high impact on our life. Agriculture is the most important sector of our Economy. Proper management leads to a profit in agricultural products. Farmers do not expertise in leaf disease so they produce less production. Plant leaf diseases detection is the important because profit and loss are depending on production. CNN is the solution for leaf disease detection and classification. Main aim of this research is to detect the apple, grape, corn, potato and tomato plants leaf diseases. Plant leaf diseases are monitoring of large fields of crops disease detection, and thus automatically detected some feature of diseases as per that provide medical treatment. Proposed Deep CNN model has been compared with popular transfer learning approach such as VGG16. Plant leaf disease detection is the one of the required research topic as it may prove benefits in monitoring large fields of crops, and thus automatically detect the symptoms of diseases as soon as they appear on plant leaves. In this project we focus on providing a quick and effective solution to every farmer who is affected with crop damaging pests. |
| 5 |
Farm Management System |
This report describes the development and implementation of a farm management system project that enables farmers to directly sell their products to customers. The project aims to eliminate intermediaries in the farm-to-market supply chain, thereby providing farmers with a more profitable and efficient means of selling their products. The system includes features such as product listings, inventory management, and online ordering, which allows customers to purchase farm products directly from the website. The report outlines the project’s objectives, methodology, and outcomes, highlighting the system’s benefits to farmers and customers alike. Overall, the farm management system project offers a practical solution to the challenges faced by small-scale farmers in accessing markets and generating income. |
| 6 |
ORCHARD MANAGEMENT IN OPEN FIELDS WITH DEEP LEARNING-BASED FRUIT MONITORING |
Mango is an important agricultural produce with high export value as it is being consumed internationally. This work presents a method for detection and counting of mangoes in RGB images for further yield estimation. The RGB images are acquired in open field conditions from a mango orchard in the pre-harvest stage. The proposed method uses, deep convolutional neural network based architecture for mango detection using semantic segmentation. Further, mango objects are detected in the semantic segmented output using contour based connected object detection. Results are analysed using the precision, recall, F1 parameters derived from contingency matrix. Results demonstrate the robustness of detection for a multitude of factors such as scale, occlusion, distance and illumination conditions, characteristic to open field conditions. Further mango fruit size also determined for the estimation of fruit maturation and size distribution, for further decision making to harvest and marketing. To detect fruit, cascade detection with histogram of oriented gradients (HOG) feature is applied. Finally, fruit lineal dimensions were calculated using the RGB-D depth information, fruit image size and the thin lens formula. We believe this work represents the first practical implementation of machine vision fruit sizing in field, with practicality gauged in terms of cost and simplicity of operation. |
| 7 |
Mango leaf disease prediction |
The Convolutional Neural Network CNN works by obtaining a picture and designating it with some weightage supported by the various objects of the image, to distinguish them from one another. CNN needs little or no pre-processing information as compared to different deep learning algorithms. Early diagnosis and correct identification of mango plant disease prediction will manage the unfolding of the diseases Mango leaf diseases damage mango quality and yield. This research uses deep learning to automatically identify leaf diseases in different mango plant kinds. The planned work is Associated with the Nursing correct identification approach for the mango plant disease prediction exploitation of the Convolutional Neural Network. It includes generating comfortable method pathological pictures Associate in nursing coming up with a model and a design of the Convolutional Neural Network to discover mango leaf diseases. The image augmentation method is employed to extend the number of images. completely different information augmentation techniques square measure applied to stop overfitting and improve generalization. |
| 8 |
NutriDetect: ML-Powered Analyzer for freshness and nutrition in Fruits and vegetables |
This research presents an innovative approach to classify fruits and vegetables and provide detailed nutritional analysis and freshness assessment. Leveraging OpenCV for image processing and Convolutional Neural Networks (CNN) for machine learning, our system accurately identifies and categorizes produce from images. It also extracts essential nutrient information from a CSV dataset. Integrated with a freshness detection model, it empowers consumers to make informed decisions when selecting fresh and nutritious produce. |
| 9 |
HealthHub: Food Item Recognition with Calorie Estimation and HealthConscious Product Suggestions |
Accurately measuring the calorie content of food is essential for promoting healthy eating habits and managing dietary intake. However, calorie estimation poses challenges due to the diverse composition of ingredients and variations in cooking methods. This paper presents a novel approach for estimating food calorie content based on ingredient recognition and thermal information. The proposed method utilizes convolutional neural networks (CNN) for image classification to identify food items and extract their corresponding ingredients from a comprehensive database enriched with nutritional knowledge. Additionally, thermal imaging is employed to analyze the heat patterns of food ingredients, aiding in the segmentation and classification process. Fuzzy logic techniques are applied to classify ingredient boundaries based on their thermal signatures and intensity levels. The classified ingredients are then aggregated, and their calorie content is calculated using established nutrition knowledge and area ratios. Comparative analysis against conventional methods demonstrates the efficacy of the proposed approach in accurately estimating food calories. Furthermore, the HealthHub Food Item Recognition system integrates this calorie estimation functionality with health-conscious product suggestions, enhancing its utility for promoting balanced nutrition and facilitating informed dietary choices. |
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