SMARD: A Cost Effective Smart Agro Development Technology for Crops Disease Classification

• URL: http://arxiv.org/abs/2405.10543v1
• Date: Fri, 17 May 2024 05:14:39 GMT
• Title: SMARD: A Cost Effective Smart Agro Development Technology for Crops Disease Classification
• Authors: Tanoy Debnath, Shadman Wadith, Anichur Rahman,
• Abstract summary: "SMARD" project aims to strengthen the country's agricultural sector. It provides farmers with information on crop care, seed selection, and disease management best practices. Farmers can also contact the expert panel through text message, voice call, or video call to purchase fertilizer, seeds, and pesticides at low prices.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Agriculture has a significant role in a country's economy. The "SMARD" project aims to strengthen the country's agricultural sector by giving farmers with the information and tools they need to solve common difficulties and increase productivity. The project provides farmers with information on crop care, seed selection, and disease management best practices, as well as access to tools for recognizing and treating crop diseases. Farmers can also contact the expert panel through text message, voice call, or video call to purchase fertilizer, seeds, and pesticides at low prices, as well as secure bank loans. The project's goal is to empower farmers and rural communities by providing them with the resources they need to increase crop yields. Additionally, the "SMARD" will not only help farmers and rural communities live better lives, but it will also have a good effect on the economy of the nation. Farmers are now able to recognize plant illnesses more quickly because of the application of machine learning techniques based on image processing categorization. Our experiments' results show that our system "SMARD" outperforms the cutting-edge web applications by attaining 97.3% classification accuracy and 96% F1-score in crop disease classification. Overall, our project is an important endeavor for the nation's agricultural sector because its main goal is to give farmers the information, resources, and tools they need to increase crop yields, improve economic outcomes, and improve livelihoods.

Related papers

• Anticipatory Understanding of Resilient Agriculture to Climate [66.008020515555]
  We present a framework to better identify food security hotspots using a combination of remote sensing, deep learning, crop yield modeling, and causal modeling of the food distribution system. We focus our analysis on the wheat breadbasket of northern India, which supplies a large percentage of the world's population.
  arXiv  Detail & Related papers  (2024-11-07T22:29:05Z)
• Generating Diverse Agricultural Data for Vision-Based Farming Applications [74.79409721178489]
  This model is capable of simulating distinct growth stages of plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. Our dataset includes 12,000 images with semantic labels, offering a comprehensive resource for computer vision tasks in precision agriculture.
  arXiv  Detail & Related papers  (2024-03-27T08:42:47Z)
• Agricultural 4.0 Leveraging on Technological Solutions: Study for Smart Farming Sector [2.06242362470764]
  Agriculture 4.0 is a tech-driven revolution in agriculture with the goal of raising industry production and efficiency. Food waste, climate change, population shifts, and resource scarcity are four primary trends responsible for it. The objective is to establish a value chain that is optimized to facilitate enhanced monitoring and decreased labor expenses.
  arXiv  Detail & Related papers  (2024-01-01T17:02:49Z)
• Smart Connected Farms and Networked Farmers to Tackle Climate Challenges Impacting Agricultural Production [5.455648887547882]
  There are rapid advances in information and communication technology, precision agriculture and data analytics, which are creating a fertile field for the creation of smart connected farms (SCF) A network and coordinated farmer network provides unique advantages to farmers to enhance farm production and profitability, while tackling adverse climate events.
  arXiv  Detail & Related papers  (2023-12-19T17:08:43Z)
• Climate Change Impact on Agricultural Land Suitability: An Interpretable Machine Learning-Based Eurasia Case Study [94.07737890568644]
  As of 2021, approximately 828 million people worldwide are experiencing hunger and malnutrition. Climate change significantly impacts agricultural land suitability, potentially leading to severe food shortages. Our study focuses on Central Eurasia, a region burdened with economic and social challenges.
  arXiv  Detail & Related papers  (2023-10-24T15:15:28Z)
• AgroTIC: Bridging the gap between farmers, agronomists, and merchants through smartphones and machine learning [16.079127761987667]
  AgroTIC is a smartphone-based application for agriculture that bridges the gap between farmers, agronomists, and merchants. We present a case study of the AgroTIC app among citrus fruit farmers from the Santander department in Colombia.
  arXiv  Detail & Related papers  (2023-05-21T10:07:51Z)
• Empowering Agrifood System with Artificial Intelligence: A Survey of the Progress, Challenges and Opportunities [86.89427012495457]
  We review how AI techniques can transform agrifood systems and contribute to the modern agrifood industry. We present a progress review of AI methods in agrifood systems, specifically in agriculture, animal husbandry, and fishery. We highlight potential challenges and promising research opportunities for transforming modern agrifood systems with AI.
  arXiv  Detail & Related papers  (2023-05-03T05:16:54Z)
• AGI for Agriculture [30.785325834651644]
  Artificial General Intelligence (AGI) is poised to revolutionize a variety of sectors, including healthcare, finance, transportation, and education. This paper delves into the potential future applications of AGI in agriculture, such as agriculture image processing, natural language processing (NLP), robotics, knowledge graphs, and infrastructure.
  arXiv  Detail & Related papers  (2023-04-12T19:39:49Z)
• A Deep Learning-based Detector for Brown Spot Disease in Passion Fruit Plant Leaves [0.5485240256788552]
  This work focuses on two major diseases woodiness (viral) and brown spot (fungal) diseases. We have partnered with the Uganda National Crop Research Institute (NaCRRI) to develop a dataset of expertly labelled passion fruit plant leaves and fruits.
  arXiv  Detail & Related papers  (2020-07-28T10:17:43Z)
• Learning from Data to Optimize Control in Precision Farming [77.34726150561087]
  Special issue presents the latest development in statistical inference, machine learning and optimum control for precision farming. Satellite positioning and navigation followed by Internet-of-Things generate vast information that can be used to optimize farming processes in real-time.
  arXiv  Detail & Related papers  (2020-07-07T12:44:17Z)
• Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis [110.30849704592592]
  We present Agriculture-Vision: a large-scale aerial farmland image dataset for semantic segmentation of agricultural patterns. Each image consists of RGB and Near-infrared (NIR) channels with resolution as high as 10 cm per pixel. We annotate nine types of field anomaly patterns that are most important to farmers.
  arXiv  Detail & Related papers  (2020-01-05T20:19:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.