Farm Productivity Tools

Application of optical sensing for plant phenotyping 👨🌾📟 🌱 Optical sensing technologies use light to non-destructively measure plant traits, offering powerful tools for monitoring crop health, stress, and performance. 📡 Common optical sensors for phenotyping include UV-VIS, VIS-NIR, MIR, Raman spectroscopy, and Hyperspectral Imaging (HSI). 1️⃣ UV–VIS spectroscopy (200–800 nm) is used for quantifying nutrients in solutions and for the non-destructive quality evaluation of crops like leafy greens. 2️⃣ Visible-Near Infrared (VIS-NIR) spectroscopy (400–2500 nm) offers rapid, non-destructive analysis of nutrient and quality attributes, such as protein and water content, in plant tissues. 3️⃣ Mid-infrared (MIR) spectroscopy (2500–25,000 nm) provides molecular 'fingerprint' characteristics, making it ideal for analyzing complex organic compounds like cellulose, pectins, and lipids. 4️⃣ Raman spectroscopy provides a unique molecular fingerprint, enabling rapid, non-invasive diagnosis of plant stress and disease, as its signal is not interfered with by water. 5️⃣ Hyperspectral imaging (HSI) combines imaging and spectroscopy to create detailed maps of plant health, identifying the precise location of stress, disease, or nutrient deficiencies across a plant or field. 👨🌾 These technologies are moving crop management beyond simple observation, enabling a shift from reactive problem-solving to predictive and prescriptive strategies for optimizing inputs and yield. Image: applications of optical sensing in indoor farming based on the spectral range (based on: Gorji et al. 2024; DOI: 10.1016/j.saa.2024.124820). #agriculture #science

🌱At Akin Analytics, we’re committed to leveraging advanced drone technologies like these to help farmers make data-driven decisions that optimize yield and sustainability. 🌱🚁 🌱Thermal vs. Multispectral Cameras for Drones in Agriculture: Choosing the Right Sensor for Effective Crop Analysis: In modern precision agriculture, selecting the right drone sensor is critical for accurate and actionable insights. Here’s a quick breakdown of two popular camera types that are transforming aerial crop analysis: 🌡️ Thermal Camera (e.g., FLIR Vue Pro, DJI Zenmuse XT2) • What it Measures: Infrared radiation → Canopy temperature, heat anomalies • Data Output: Temperature maps, heatmaps, anomaly detection • Key Applications: Water stress mapping, irrigation optimization, pest/disease detection, leak detection • Operational Conditions: Works day and night, even under shadows or clouds • Hardware / Cost: Lower resolution, sensitive to temperature differences; mid-to-high cost 🌿 Multispectral Camera (e.g., MicaSense RedEdge, Parrot Sequoia) • What it Measures: Light reflectance across multiple bands (Red, Green, Blue, NIR, Red-edge) → NDVI, vegetation indices • Data Output: Vegetation indices, reflectance maps, crop health scoring • Key Applications: Vegetation health, crop vigor mapping, NDVI/NDRE calculation, biomass estimation, nutrient deficiency detection • Operational Conditions: Requires sunlight; less effective under heavy clouds or shadows • Hardware / Cost: Higher spatial resolution; cost depends on the number of bands and calibration 💡 Takeaway For immediate stress detection (e.g., irrigation issues or pest hotspots), Thermal cameras are ideal. For comprehensive crop health assessment and monitoring vegetation vigor over time, Multispectral cameras excel. Both are invaluable tools, depending on the specific agricultural needs.

Drone DSM + High-Fidelity Multispectral Data = Smarter Crop Decisions Cassava and banana fields reveal a lot through their height and terrain patterns—and drone-based DSM helps turn those patterns into actionable insights. Using high-resolution data captured with an Agrowing multispectral sensor, we could mapped:  • Plant-wise height  • Ground elevation  • Slope, drainage & micro-topography  • Stress zones and missing plants Why it matters: --Short cassava = stress or low yield potential --Height variation in banana = disease, vigor or storm-risk --Low-lying zones = waterlogging --High slopes = drought & erosion risk Plant height + terrain patterns directly improve yield prediction DSM helps correlate canopy height with expected development, biomass, and final productivity. What this combination delivered:  • Per-plant height map  • Terrain & slope model  • Stress zone detection  • Yield-relevant height insights  • Clean, accurate results thanks to Agrowing’s spectral fidelity Drone DSM isn’t just mapping — it’s decision intelligence for every farmer.

We've been testing something that could change how vineyard managers plan their harvest. Our data tool can now detect and count grape clusters as Thorvald moves down the row. What used to require manual scouting and estimation is now captured automatically, giving growers real-time visibility into their crop load before harvest. Here's why this matters: ✔️ More accurate yield forecasts mean better planning for harvest logistics and labor ✔️ Early cluster counts help inform thinning decisions to optimize quality ✔️ Data-driven insights replace guesswork, reducing risk and improving profitability ✔️ All collected during our regular UV-C passes meaning no extra time or labor required For vineyards managing thousands of acres, this kind of precision at scale was nearly impossible before. Now it's part of the workflow. The video shows how a single vine develops throughout the season.

Here are 4 powerful types of drone maps every large-scale farming operation should know about (and how they impact your bottom line): 1️⃣ Orthomosaic Maps 🟢 What it is: High-resolution stitched field view. 🔍 Use: Scouting crops, documenting conditions, compliance & record-keeping. 💰 ROI: Saves hours of manual scouting, ensures traceability for audits, and reduces field inspection costs by up to 70%. 2️⃣ NDVI / NDRE Maps 🟢 What it is: Vegetation health index (infrared imaging). 🔍 Use: Detects crop stress (nutrients, pests, water) before the human eye can. 💰 ROI: Early detection prevents yield loss and improves input efficiency, leading to a 5–15% yield increase on large farms. 3️⃣ Thermal Maps 🟢 What it is: Heat signature imaging. 🔍 Use: Identifies irrigation leaks, water stress zones, and drainage issues. 💰 ROI: Prevents overwatering, saves water (20–30%), reduces pumping costs, and boosts sustainability metrics. 4️⃣ Elevation / Terrain Maps 🟢 What it is: 3D topographic mapping. 🔍 Use: Drainage planning, erosion control, optimized planting patterns. 💰 ROI: Prevents soil loss, ensures optimal land use, and increases long-term productivity. ⚡ Key Benefits for Large-Scale Corporations - Cut operational costs in scouting, irrigation, and compliance. - Increase yields through early detection & precision input use. - Strengthen ESG metrics with measurable sustainability impact. - Gain a competitive edge with data-driven decision-making. 💡 Implementation Tips: Start with orthomosaic + NDVI for quick wins. Integrate thermal mapping if water stress/irrigation is a major cost center. Use drone service providers before investing in your own fleet. Combine drone maps with farm management software for maximum ROI. 👉 Corporations that adopt drone mapping early will see higher yields, lower costs, and stronger sustainability positioning. Which of these 4 maps would make the biggest impact on your operations today? #PrecisionAg #DroneOpsUSA #Droneservices

🚨🚨New Publication Alert!🚨🚨 We are proud to share our latest open-access article published in Agriculture MDPI: 📄 “Automated Crop Measurements with UAVs: Evaluation of an AI-Driven Platform for Counting and Biometric Analysis” 👉 https://lnkd.in/eNUpDN64 In this study, we tested an AI-powered web platform (Solvi.ag) for automated plant counting and biometric traits (e.g., height, area, and size) estimation using drone imagery in pecan orchards and onion fields in Georgia. 🧅🌰 💡 Key findings: UAV counts achieved >97% accuracy for both crops. Tree height and canopy area estimations were highly consistent on mature trees. Onion bulb size could be estimated with errors ranges within the market classes sizes. This article highlights how recent advances in AI and UAV technologies can provide reliable and scalable tools for precision horticulture, supporting automated crop monitoring and providing another tool in the toolbox. 👨🔬 Authors: João Victor da Silva Martins, Marcelo R. Barbosa Júnior, Lucas A. Sales, Regimar G. dos Santos, Wellington S. Ribeiro, and Luan P. de Oliveira Joao Victor Martins Marcelo Rodrigues Barbosa Júnior Lucas de Azevedo Sales Regimar Garcia dos Santos @wellingtonsoutoribeiro #PrecisionAgriculture #AIinAgriculture #UAVs #DigitalFarming #Horticulture #SmartFarming #UGAExtension #PrecisionHorticultureLab #Pecans #Onions #DroneTechnology