Farmer's Assistant: A Machine Learning Based Application for Agricultural Solutions

• URL: http://arxiv.org/abs/2204.11340v1
• Date: Sun, 24 Apr 2022 19:31:10 GMT
• Title: Farmer's Assistant: A Machine Learning Based Application for Agricultural Solutions
• Authors: Shloka Gupta, Akshay Chopade, Nishit Jain, Aparna Bhonde
• Abstract summary: We create an open-source easy-to-use web application to address some of these issues which may help improve crop production. In particular, we support crop recommendation, fertilizer recommendation, plant disease prediction, and an interactive news-feed.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Farmers face several challenges when growing crops like uncertain irrigation, poor soil quality, etc. Especially in India, a major fraction of farmers do not have the knowledge to select appropriate crops and fertilizers. Moreover, crop failure due to disease causes a significant loss to the farmers, as well as the consumers. While there have been recent developments in the automated detection of these diseases using Machine Learning techniques, the utilization of Deep Learning has not been fully explored. Additionally, such models are not easy to use because of the high-quality data used in their training, lack of computational power, and poor generalizability of the models. To this end, we create an open-source easy-to-use web application to address some of these issues which may help improve crop production. In particular, we support crop recommendation, fertilizer recommendation, plant disease prediction, and an interactive news-feed. In addition, we also use interpretability techniques in an attempt to explain the prediction made by our disease detection model.

Related papers

• A Machine Learning Approach for Crop Yield and Disease Prediction Integrating Soil Nutrition and Weather Factors [0.0]
  The development of an intelligent agricultural decision-supporting system for crop selection and disease forecasting in Bangladesh is the main objective of this work. The recommended approach uses a variety of datasets on the production of crops, soil conditions, agro-meteorological regions, crop disease, and meteorological factors.
  arXiv  Detail & Related papers  (2024-03-28T09:57:50Z)
• 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)
• Leaf-Based Plant Disease Detection and Explainable AI [16.128084819516715]
  The agricultural sector plays an essential role in the economic growth of a country. Plant Disease is one of the significant factors affecting the agricultural sector. Researchers have explored many applications based on AI and Machine Learning techniques to detect plant diseases.
  arXiv  Detail & Related papers  (2023-12-17T03:40:12Z)
• 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)
• Agave crop segmentation and maturity classification with deep learning data-centric strategies using very high-resolution satellite imagery [101.18253437732933]
  We present an Agave tequilana Weber azul crop segmentation and maturity classification using very high resolution satellite imagery. We solve real-world deep learning problems in the very specific context of agave crop segmentation. With the resulting accurate models, agave production forecasting can be made available for large regions.
  arXiv  Detail & Related papers  (2023-03-21T03:15:29Z)
• Plant Disease Detection using Region-Based Convolutional Neural Network [2.5091819952713057]
  Agriculture plays an important role in the food and economy of Bangladesh. One of the major reasons behind low crop production is numerous bacteria, virus and fungal plant diseases. This paper aims at building a lightweight deep learning model for predicting leaf disease in tomato plants.
  arXiv  Detail & Related papers  (2023-03-16T03:43:10Z)
• Automated Wheat Disease Detection using a ROS-based Autonomous Guided UAV [0.0]
  A smart autonomous system has been implemented on an unmanned aerial vehicle to automate the task of monitoring wheat fields. An image-based deep learning approach is used to detect and classify disease-infected wheat plants. A mapping and navigation system is presented using a simulation in the robot operating system and Gazebo environments.
  arXiv  Detail & Related papers  (2022-06-30T06:12:48Z)
• Towards a Multimodal System for Precision Agriculture using IoT and Machine Learning [0.5249805590164902]
  Technology like Internet of Things (IoT) for data collection, machine Learning for crop damage prediction, and deep learning for crop disease detection is used. Various algorithms like Random Forest (RF), Light gradient boosting machine (LGBM), XGBoost (XGB), Decision Tree (DT) and K Nearest Neighbor (KNN) are used for crop damage prediction. Pre-Trained Convolutional Neural Network (CNN) models such as VGG16, Resnet50, and DenseNet121 are also trained to check if the crop was tainted with some illness or not.
  arXiv  Detail & Related papers  (2021-07-10T19:19:45Z)
• Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations [58.17039841385472]
  We take advantage of all parallel developments in mechanistic modeling and satellite data availability for advanced monitoring of crop productivity. Our model successfully estimates gross primary productivity across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites. This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms.
  arXiv  Detail & Related papers  (2020-12-07T16:23:13Z)
• 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.