Plant sensors

arXiv:2510.15757v1 Announce Type: new Abstract: Poultry farming faces increasing pressure to meet productivity targets while ensuring animal welfare and environmental compliance. Yet many small and medium-sized farms lack affordable, integrated tools for continuous monitoring and decision-making, relying instead on manual, reactive inspections. This paper presents Poultry Farm Intelligence (PoultryFI) - a modular, cost-effective platform that integrates six AI-powered modules: Camera Placement Optimizer, Audio-Visual Monitoring, Analytics & Alerting, Real-Time Egg Counting, Production & Profitability Forecasting, and a Recommendation Module. Camera layouts are first optimized offline using evolutionary algorithms for full poultry house coverage with minimal hardware. The Audio-Visual Monitoring module extracts welfare indicators from synchronized video, audio, and feeding data. Analytics & Alerting produces daily summaries and real-time notifications, while Real-Time Egg Counting

arXiv:2509.24992v1 Announce Type: new Abstract: We present a bio-hybrid environmental sensor system that integrates natural plants and embedded deep learning for real-time, on-device detection of temperature and ozone level changes. Our system, based on the low-power PhytoNode platform, records electric differential potential signals from Hedera helix and processes them onboard using an embedded deep learning model. We demonstrate that our sensing device detects changes in temperature and ozone with good sensitivity of up to 0.98. Daily and inter-plant variability, as well as limited precision, could be mitigated by incorporating additional training data, which is readily integrable in our data-driven framework. Our approach also has potential to scale to new environmental factors and plant species. By integrating embedded deep learning onboard our biological sensing device, we offer a new, low-power solution for continuous environmental monitoring and potentially other fields of

A smart new sensor developed in the UK is helping dairy farmers stop pneumonia in calves before it takes hold by tracking pen conditions in ...

arXiv:2509.20340v1 Announce Type: new Abstract: Advanced scientific applications require coupling distributed sensor networks with centralized high-performance computing facilities. Citrus Under Protective Screening (CUPS) exemplifies this need in digital agriculture, where citrus research facilities are instrumented with numerous sensors monitoring environmental conditions and detecting protective screening damage. CUPS demands access to computational fluid dynamics codes for modeling environmental conditions and guiding real-time interventions like water application or robotic repairs. These computing domains have contrasting properties: sensor networks provide low-performance, limited-capacity, unreliable data access, while high-performance facilities offer enormous computing power through high-latency batch processing. Private 5G networks present novel capabilities addressing this challenge by providing low latency, high throughput, and reliability necessary for near-real-time

arXiv:2508.11588v1 Announce Type: new Abstract: Effective and efficient agricultural manipulation and harvesting depend on accurately understanding the current state of the grasp. The agricultural environment presents unique challenges due to its complexity, clutter, and occlusion. Additionally, fruit is physically attached to the plant, requiring precise separation during harvesting. Selecting appropriate sensors and modeling techniques is critical for obtaining reliable feedback and correctly identifying grasp states. This work investigates a set of key sensors, namely inertial measurement units (IMUs), infrared (IR) reflectance, tension, tactile sensors, and RGB cameras, integrated into a compliant gripper to classify grasp states. We evaluate the individual contribution of each sensor and compare the performance of two widely used classification models: Random Forest and Long Short-Term Memory (LSTM) networks. Our results demonstrate that a Random Forest classifier, trained in a

Farmers might be able to get help tending and harvesting crops using a new sensing technology from Carnegie Mellon University's Robotics Institute (RI). Researchers have invented a tool called SonicBoom that can find crops like apples based on the sound they make. The novel technology, still in the early stages of development, may someday be used by farm robots for tasks like pruning vines or locating ripe apples hidden among the leaves.

Financial Times: Sources: Samsung to produce image sensors for iPhone 18 in Austin, Texas; an expert says Apple picked Samsung as a supplier because Sony doesn't have US plants  —  Deal suggests South Korean company's US investments are paying off as Trump dials up his tariff policies

Ever wish you had insight on whether the plants in your home garden are really thriving? A group of Northeastern University researchers recently developed sensors that change color to indicate the health status of plants. This can be used not only for your basic houseplant, but could be used to help small farms monitor their crops in the face of environmental stressors like weather shifts, pollution and disease.

A sensor that can measure hormone concentrations in plants precisely and in real time with minimal damage can shed light on how hormones affect plants' response to disease and stress. With further development, it could also be part of an agricultural toolkit for early detection of disease or stress, enabling farmers to intervene before extensive crop damage.

Tools that offer early and accurate insight into plant health—and allow individual plant interventions—are key to increasing crop yields as environmental pressures increasingly impact horticulture and agriculture.

arXiv:2505.13916v1 Announce Type: new Abstract: Current remote sensing technologies that measure crop health e.g. RGB, multispectral, hyperspectral, and LiDAR, are indirect, and cannot capture plant stress indicators directly. Instead, low-cost leaf sensors that directly interface with the crop surface present an opportunity to advance real-time direct monitoring. To this end, we co-design a sensor-detector system, where the sensor is a novel colorimetric leaf sensor that directly measures crop health in a precision agriculture setting, and the detector autonomously obtains optical signals from these leaf sensors. This system integrates a ground robot platform with an on-board monocular RGB camera and object detector to localize the leaf sensor, and a hyperspectral camera with motorized mirror and an on-board halogen light to acquire a hyperspectral reflectance image of the leaf sensor, from which a spectral response characterizing crop health can be extracted. We show a successful

Microsoft and the FFA are expanding their FarmBeats for Students program to classrooms across the U.S., providing students with hands-on experience in precision agriculture. The initiative equips teachers and students with AI-driven kits, helping them tackle real-world farming challenges.

arXiv:2504.03785v1 Announce Type: cross Abstract: In the era of growing interest in healthy buildings and smart homes, the importance of sustainable, health conscious indoor environments is paramount. Smart tools, especially VOC sensors, are crucial for monitoring indoor air quality, yet interpreting signals from various VOC sources remains challenging. A promising approach involves understanding how indoor plants respond to environmental conditions. Plants produce terpenes, a type of VOC, when exposed to abiotic and biotic stressors - including pathogens, predators, light, and temperature - offering a novel pathway for monitoring indoor air quality. While prior work often relies on specialized laboratory sensors, our research leverages readily available commercial sensors to detect and classify plant emitted VOCs that signify changes in indoor conditions. We quantified the sensitivity of these sensors by measuring 16 terpenes in controlled experiments, then identified and tested the

Precise information about agricultural soils is key to managing them more efficiently and sustainably. Researchers at the Leibniz institutes FBH and ATB have recently enhanced an existing sensor platform for mobile soil mapping of agricultural fields.

Screen-printed, biodegradable soil sensors that can be composted at the end of their lifecycle could enable farmers to improve crop yields while reducing electronic waste, researchers say.

Plants send distress signals when under attack from pests, drought, or disease, but these signals are often invisible to the naked eye. Now, scientists have developed a tiny, wearable sensor that attaches to plant leaves and detects stress before visible damage occurs. By measuring hydrogen peroxide levels—an early biochemical warning—this patch helps farmers and gardeners [...]

arXiv:2502.18671v1 Announce Type: cross Abstract: Wireless Sensor Networks have risen as a highly promising technology suitable for precision agriculture implementations, enabling efficient monitoring and control of agricultural processes. In precision agriculture, accurate and synchronized data collection is crucial for effective analysis and decision making. Using principles of information theory, we can define conditions and parameters that influence the efficient transmission and processing of information. Existing technologies have limitations in maintaining consistent time references, handling node failures, and unreliable communication links, leading to inaccurate data readings. Reliable data storage is demanding now-a-days for storing data on local monitoring station as well as in online live server. Sometime internet is not working properly due to congestion and there is frequent packet loss. Current solutions often synchronize records based on database timestamps, leading to

arXiv:2502.18671v1 Announce Type: new Abstract: Wireless Sensor Networks have risen as a highly promising technology suitable for precision agriculture implementations, enabling efficient monitoring and control of agricultural processes. In precision agriculture, accurate and synchronized data collection is crucial for effective analysis and decision making. Using principles of information theory, we can define conditions and parameters that influence the efficient transmission and processing of information. Existing technologies have limitations in maintaining consistent time references, handling node failures, and unreliable communication links, leading to inaccurate data readings. Reliable data storage is demanding now-a-days for storing data on local monitoring station as well as in online live server. Sometime internet is not working properly due to congestion and there is frequent packet loss. Current solutions often synchronize records based on database timestamps, leading to

arXiv:2502.16028v1 Announce Type: new Abstract: In precision agriculture and plant science, there is an increasing demand for wireless sensors that are easy to deploy, maintain, and monitor. This paper investigates a novel approach that leverages recent advances in extremely low-power wireless communication and sensing, as well as the rapidly increasing availability of unmanned aerial vehicle (UAV) platforms. By mounting a specialized wireless payload on a UAV, battery-less sensor tags can harvest wireless beacon signals emitted from the drone, dramatically reducing the cost per sensor. These tags can measure environmental information such as temperature and humidity, then encrypt and transmit the data in the range of several meters. An experimental implementation was constructed at AERPAW, an NSF-funded wireless aerial drone research platform. While ground-based tests confirmed reliable sensor operation and data collection, airborne trials encountered wireless interference that

Keeping that picture-perfect outdoor oasis can be quite a challenge, especially when trying to get the perfect amount of water.This is why it’s a good idea to install an irrigation system to make it easier and provide the right amount of moisture your landscaping needs.  Automating your watering schedule keeps your grass, shrubs, and plants... The post Smart Wireless Soil Sensors: Take the Guesswork Out of Home Irrigation appeared first on Mr Water Geek.

InnerPlant has completed the FDA's New Protein Consultation (NPC) process for the fluorescent protein used in its InnerSoy product. The post InnerPlant takes ‘meaningful step’ in regulatory journey for genetically engineered ‘sensor plants’ appeared first on AgFunderNews.

Greenhouses and open farms that welcome visitors to purchase locally grown produce and meat have become increasingly important to food productivity. Not only are farmers looking for ways to monitor conditions to help improve greenhouse crop growth and yield, but keeping harvested food fresh in storage conditions is also a major concern.

Nikkei Asia: Q&A with Sony Semiconductor Manufacturing President Yoshihiro Yamaguchi on shipping 20B+ image sensors, a new manufacturing plant in Kumamoto, Japan, and more  —  KUMAMOTO, Japan/TOKYO — With more than 20 billion image sensors shipped to date and a new plant being constructed …

Sensors are capable of detecting pH changes in plant xylem. Continue reading World’s first COF sensors: Detecting dehydration in plants on Tech Explorist.

MANHATTAN, Kan. – Kansas State University researchers have received a $2 million award from the National Science Foundation’s Global Centers program to develop sensors that can more accurately detect nutrients, […] The post K-State Researchers Aim to Develop Soil Sensors That Will Measure Farm Fields at the Nanoscale appeared first on Morning Ag Clips.

Cutting back on animal protein in our diets can save on resources and greenhouse gas emissions. But convincing meat-loving consumers to switch up their menu is a challenge. Looking at this problem from a mechanical engineering angle, Stanford engineers are pioneering a new approach to food texture testing that could pave the way for faux filets that fool even committed carnivores.

The increasing global demand for plant-based foods makes the use of pesticides necessary in order to protect crops from pests and ensure crop yields. However, there is one major disadvantage: the widespread use of pesticides has led to a considerable reduction in insect populations in the past. The decline in wild bees, which make a significant contribution to pollination and are therefore essential for agricultural yields, is particularly worrying.

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arXiv:2408.11068v1 Announce Type: new Abstract: This paper presents a highly sensitive differential soil moisture sensor (DSMS) using a microstrip line loaded with triangular two turn resonator(T2 SR) and complementary of the rectangular two turn spiral resonator(CR2 SR),simultaneously.Volumetric Water Content (VWC) or permittivity sensing is conducted by loading the T2 SR side with dielectric samples.Two transmission notches are observed for identical loads relating to T2 SR and CR2 SR.The CR2 SR notch at 4.39 GHz is used as a reference for differential permittivity measurement method.Further, the resonance frequency of T2 SR is measured relative to the reference value. Based on this frequency difference,the permittivity of soil is calculated which is related to the soil VWC.Triangular two turn resonator (T2 SR) resonance frequency changes from 4 to 2.38 GHz when VWC varies 0 percent to 30 percent.The sensor's operation principle is described through circuit model analysis and

arXiv:2408.10462v1 Announce Type: new Abstract: This paper presents a Time-Domain Transmissometry Soil Moisture Sensor (TDT-SMS) using a Dispersive Phase Shifter (DPS), consisting of an interdigital capacitor that is loaded with a stacked 4-turn Complementary Spiral Resonator (S4-CSR). Soil moisture measurement technique of the proposed sensor is based on the complex permittivity sensing property of a DPS in time domain. Soil relative permittivity which varies with its moisture content is measured by burying the DPS under a soil mass and changing its phase difference while excited with a 114 MHz sine wave (single tone). DPS output phase and magnitude are compared with the reference signal and measured with a phase/loss detector. The proposed sensor exhibits accuracy better than +-1.2 percent at the highest Volumetric Water Content (VWC=30 percent) for sandy-type soil. Precise design guide is developed and simulations are performed to achieve a highly sensitive sensor. The measurement

Engineers created a compact sensor with infrared imaging for drones, enhancing crop management by allowing for precise irrigation and pest control, which could lower grocery prices and boost harvests. An international team of engineers has developed a compact and lightweight sensor system with infrared imaging capabilities that can be easily mounted on a drone for [...]

EAST LANSING, Mich. — Unpredictable precipitation is one of the most challenging elements of being a farmer. Not enough moisture, and plant growth is hindered. Too much can saturate the soil while setting the stage for diseases to thrive. Recently, Michigan growers have experienced both extremes. Some of the driest and wettest months on record […] The post MSU Researchers Develop Low-Cost Sensors to Help Farmers Irrigate More Efficiently, Manage Diseases appeared first on Morning Ag Clips.

By integrating advanced sensor technology, CoreScan delivers high-definition maps of soil compaction, nutrient and water storage, organic matter/carbon and more. The post Veris Technologies Unveils Soil Sensor Probe CoreScan appeared first on CropLife.

arXiv:2407.07734v1 Announce Type: new Abstract: In this paper, a joint sensing and communication system is presented for smart agriculture. The system integrates an Ultra-compact Soil Moisture Sensor (UCSMS) for precise sensing, along with a Pattern Reconfigurable Antenna (PRA) for efficient transmission of information to the base station. A multiturn complementary spiral resonator (MCSR) is etched onto the ground plane of a microstrip transmission line to achieve miniaturization. The UCSMS operates at 180 MHz with a 3-turn complementary spiral resonator (3-CSR), at 102 MHz with a 4- turn complementary spiral resonator (4-CSR), and at 86 MHz with a 5-turn complementary spiral resonator (5-CSR). Due to its low resonance frequency, the proposed UCSMS is insensitive to variations in the Volume Under Test (VUT) of soil. A probe-fed circular patch antenna is designed in the Wireless Local Area Network (WLAN) band (2.45 GHz) with a maximum measured gain of 5.63 dBi. Additionally, four

A research team has investigated low-cost depth imaging sensors with the objective of automating plant pathology tests. The team achieved 97% accuracy in distinguishing between resistant and susceptible plants based on cotyledon loss. This method operates 30 times faster than human annotation and is robust across various environments and plant densities.