An Open Dataset of Sensor Data from Soil Sensors and Weather Stations at Production Farms

• URL: http://arxiv.org/abs/2302.09072v1
• Date: Thu, 16 Feb 2023 21:41:57 GMT
• Title: An Open Dataset of Sensor Data from Soil Sensors and Weather Stations at Production Farms
• Authors: Charilaos Mousoulis, Pengcheng Wang, Nguyen Luu Do, Jose F Waimin, Nithin Raghunathan, Rahim Rahimi, Ali Shakouri, and Saurabh Bagchi
• Abstract summary: This dataset comprises soil sensor data from a representative sample of 3 nodes across 3 production farms, each for 5 months. We correlate this data with the weather data and draw some insights about the absorption of rain in the soil.
• Score: 7.561052256409596
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Weather and soil conditions are particularly important when it comes to farming activities. Study of these factors and their role in nutrient and nitrate absorption rates can lead to useful insights with benefits for both the crop yield and the protection of the environment through the more controlled use of fertilizers and chemicals. There is a paucity of public data from rural, agricultural sensor networks. This is partly due to the unique challenges faced during the deployment and maintenance of IoT networks in rural agricultural areas. As part of a 5-year project called WHIN we have been deploying and collecting sensor data from production and experimental agricultural farms in and around Purdue University in Indiana. Here we release a dataset comprising soil sensor data from a representative sample of 3 nodes across 3 production farms, each for 5 months. We correlate this data with the weather data and draw some insights about the absorption of rain in the soil. We provide the dataset at: https://purduewhin.ecn.purdue.edu/dataset2021.

Related papers

• Machine Learning-based Nutrient Application's Timeline Recommendation for Smart Agriculture: A Large-Scale Data Mining Approach [0.0]
  Inaccurate fertiliser application decisions can lead to costly consequences, hinder food production, and cause environmental harm. We propose a solution to predict nutrient application by determining required fertiliser quantities for an entire season. The proposed solution recommends adjusting fertiliser amounts based on weather conditions and soil characteristics to promote cost-effective and environmentally friendly agriculture.
  arXiv  Detail & Related papers  (2023-10-18T15:37:19Z)
• HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing [50.4506590177605]
  HarvestNet is a dataset for mapping the presence of farms in the Ethiopian regions of Tigray and Amhara during 2020-2023. We introduce a new approach based on the detection of harvest piles characteristic of many smallholder systems. We conclude that remote sensing of harvest piles can contribute to more timely and accurate cropland assessments in food insecure regions.
  arXiv  Detail & Related papers  (2023-08-23T11:03:28Z)
• Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration [55.41644538483948]
  This paper introduces a multimodal dataset from the harsh and unstructured underground environment with aerosol particles. It contains synchronized raw data measurements from all onboard sensors in Robot Operating System (ROS) format. The focus of this paper is not only to capture both temporal and spatial data diversities but also to present the impact of harsh conditions on captured data.
  arXiv  Detail & Related papers  (2023-04-27T20:21:18Z)
• A Novel Dataset for Evaluating and Alleviating Domain Shift for Human Detection in Agricultural Fields [59.035813796601055]
  We evaluate the impact of domain shift on human detection models trained on well known object detection datasets when deployed on data outside the distribution of the training set. We introduce the OpenDR Humans in Field dataset, collected in the context of agricultural robotics applications, using the Robotti platform.
  arXiv  Detail & Related papers  (2022-09-27T07:04:28Z)
• High-Resolution Satellite Imagery for Modeling the Impact of Aridification on Crop Production [2.5402662954395097]
  We introduce a first-of-its-kind dataset, SICKLE, having time-series images at different spatial resolutions from 3 different satellites. The dataset comprises of 2,398 season-wise samples from 388 unique plots distributed across 4 districts of the Delta. We benchmark the dataset on 3 separate tasks, namely crop type, phenology date (sowing, transplanting, harvesting) and yield prediction, and develop an end-to-end framework for predicting key crop parameters in a real-world setting.
  arXiv  Detail & Related papers  (2022-09-25T14:54:50Z)
• 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)
• Total Nitrogen Estimation in Agricultural Soils via Aerial Multispectral Imaging and LIBS [0.6875312133832077]
  Most existing methods to measure soil health indicators (SHIs) are in-lab wet chemistry or spectroscopy-based methods. We develop an artificial intelligence (AI)-driven near real-time unmanned aerial vehicle (UAV)-based multispectral sensing (UMS) solution to estimate total nitrogen (TN) of the soil.
  arXiv  Detail & Related papers  (2021-07-06T02:37:30Z)
• 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)
• Farmland Parcel Delineation Using Spatio-temporal Convolutional Networks [77.63950365605845]
  Farm parcel delineation provides cadastral data that is important in developing and managing climate change policies. This data can also be useful for the agricultural insurance sector for assessing compensations following damages associated with extreme weather events. Using satellite imaging can be a scalable and cost effective manner to perform the task of farm parcel delineation.
  arXiv  Detail & Related papers  (2020-04-11T19:49:09Z)
• Crop Knowledge Discovery Based on Agricultural Big Data Integration [2.597676155371155]
  Agricultural data can be generated through various sources, such as: Internet of Thing (IoT), sensors, satellites, weather stations, robots, farm equipment, agricultural laboratories, farmers, government agencies and agribusinesses. We propose an agricultural data integration method using a constellation schema which is designed to be flexible enough to incorporate other datasets and big data models.
  arXiv  Detail & Related papers  (2020-03-11T00:13:17Z)

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.