Neatleaf’s Post

Let's 𝗱𝗶𝘃𝗲 𝗱𝗲𝗲𝗽𝗲𝗿 into how 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 can help growers achieve 𝗵𝗶𝗴𝗵𝗲𝗿 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 and 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝘂𝘀𝗲 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 🌱 All real-time available, without needing prior knowledge of data analysis 💻 Interested in applying this technique in your growing environment? Please read the full article 📖 I learned something new and had fun while writing it! 🖋 #HorticultureInnovation #AgriTech #SustainableFarming #GreenhouseTechnology

Forty soybean cultivars from subsequent harvests Abstract: The selection of forage grasses that are more adapted to adverse conditions, such as water scarcity or dry rain periods , is extremely important. Mainly due to the severe climate changes, and the search for more sustainable ways of farming. Forage grasses form the basis of the diet of beef cattle and are also used as a source of biofuels, for erosion control and soil improvement. This work presents a machine learning methodology to obtain classification models for nine forage cultivars, subject to moderate and severe water stress. The Naïve Bayes algorithm is used together with the Kernel Density Estimation method to obtain the densities used in the classification models. Before learning the models, the grouped cross val idation technique and also the g rid search are used to search for the best set of hyperparameters. The best accuracy and precision results are 0.88 and 0.90, respectively. It is observed that the classification performance depends on the cultivars used in the training and test sets. At the end, the estimated probability densities are also analyzed by comparing them with some statistics obtained for each variable and water stress or control environment s The proposed methodology is a complementary approach to classical statistical methods. It provides abstract models for obtaining information about the cultivar's harvesting environment. For citation: Oliveira, B. R.; Zuffo, A. M.; Silva, F. C.; Mezzomo, R; Barrozo, L. M.; Zanatta, T. S.; Santos, J. C.; Sousa, C. H.; Coelho, Y. P.; Caldas, A. d. (2023). Dataset: Forty soybean cultivars from subsequent harvests. Trends in Agricultural and Environmental Sciences, (e230005), DOI: 10.46420/TAES.e230005 Paper link: https://lnkd.in/d7jzAytd https://lnkd.in/djc2Fr3w

Our newest study, Dynamics of Data in Agriculture 2023, is now available! This report provides a snapshot of how field data management software is being used on farms today and explores potential future changes. With responses from 841 U.S. commercial crop farmers, the study reveals extensive insights into software adoption, brand usage, feature importance, and overall satisfaction levels among farmers. It also investigates the factors influencing brand choice and the role of third-party involvement in software usage on farms. To get an overview of these insights and more, click the link below. Also, if you're attending the World Agritech conference, don’t forget to reach out to Krista MacLean for an opportunity to discuss the report's findings in person. https://lnkd.in/gFd77JnT

Wondering how Agrograph came to be, what we provide to customers, and even the secret behind our success? 👀 Check out our FAQs at https://hubs.ly/Q028T7hm0

Schenkeveld makes use of 𝗜𝗜𝗩𝗢’𝘀 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀. These smart algorithms set crucial parameters using specially developed rules. Joost Barendse, Director at Schenkeveld, stresses the importance of this approach: "𝗧𝗵𝗲 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 𝗺𝗲𝗮𝗻 𝗮 𝘁𝗼𝗼𝗹 𝗳𝗼𝗿 𝘂𝘀 𝘁𝗼 𝗺𝗮𝗸𝗲 𝗳𝗲𝘄𝗲𝗿 𝗺𝗶𝘀𝘁𝗮𝗸𝗲𝘀 𝗶𝗻 𝗰𝘂𝗹𝘁𝗶𝘃𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝘄𝗲 𝘁𝗵𝗶𝗻𝗸 𝘁𝗵𝗮𝘁 𝘁𝗼𝘄𝗮𝗿𝗱𝘀 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝘄𝗲 𝘄𝗶𝗹𝗹 𝗵𝗮𝘃𝗲 𝘁𝗼 𝘄𝗼𝗿𝗸 𝗺𝗼𝗿𝗲 𝗮𝗻𝗱 𝗺𝗼𝗿𝗲 𝘄𝗶𝘁𝗵 𝗱𝗮𝘁𝗮, 𝘁𝗵𝗶𝘀 𝘀𝘆𝘀𝘁𝗲𝗺 𝗵𝗲𝗹𝗽𝘀 𝗴𝗿𝗼𝘄𝗲𝗿𝘀 𝗺𝗮𝗸𝗲 𝗯𝗲𝘁𝘁𝗲𝗿 𝗰𝗵𝗼𝗶𝗰𝗲𝘀." 🌱   Want to know more? Click the link below for the complete article. ⬇️   #intelligentalgorithms #customerstory #readysetgrow

The PGRO Vining Pea Tool is an online forecasting system allowing users to view advanced predictions of harvest dates and yields of vining pea crops. Using crop data combined with remotely-sensed weather data, multispectral canopy reflectance and powerful machine learning, users can generate forecasts far in advance of current capabilities. PGRO has developed this innovative forecasting system using over 20 years of historic commercial vining pea data. Aiming to reduce the wastage and inefficient processing associated with fluctuating and unpredictable vining pea harvests as well as the requirement for extensive quality sampling prior to harvest, the tool presents an advancement in the technology available to the vining pea industry. Users can simply upload field boundaries and crop data (templates are available for download within the app), and the tool will do the rest, automatically pulling together the required weather, spatial, and satellite data. Watch the expalanatory video here https://lnkd.in/euctBWHF A write up can also be read in the March edition of CPM magazine https://lnkd.in/ePvKrirx

⚠️Learn MyEV: How to Map Small to Large Plot Vineyard Research⚠️ 💻Webinar Training – June 3, 2024, 9:00 AM – 10:00 AM PST Calling all #Extension faculty, #educators, #cropconsultants and #growers! This online training will provide the necessary background for data set up, tools, and resources you need to use the #MyEV (My Efficient Vineyard) platform. MyEV is an open-access tool developed from the Efficient Vineyard Project and is used to integrate #vineyard data into vineyard maps that can inform farm management decision-making. Participants will learn what and how to prepare for gathering data from small to large plot trials and how to take existing data and upload to MyEV to evaluate visual maps. The presenters will walk through the steps required to effectively use past and new data from both small plot work and envision its use for larger-scale vineyard trials and regional studies. The event is free, but be sure to register in advance for access to the webinar. The event will be recorded and made available to all who register. This event is made possible through the High Resolution Vineyard Nutrition Project, which is funded by the USDA-NIFA's Specialty Crop Research Initiative Coordinated Agricultural Projects (CAP) grant. Project Award Number: 2020-51181-32159. Terry Bates, Nick Gunner at Orbitist Patty Skinkis Nataliya Shcherbatyuk, PhD Register here 👇 https://lnkd.in/gww-bvUx

Post: Hello, LinkedIn Connections! Ever thought how data-analytics could drive sustainability in the agriculture industry? 🌱 Let's dig into an enthralling farming scenario. Imagine you're a farmer with vast fields of crops. Despite trying various methods and technologies, you've been noticing a significant drop in your harvest yield. You are wondering - where's the bug? Welcome to the world of Data Analytics! 📊💼 By applying data analytics, we can analyze various factors like weather data, soil composition, pest history, and even your fertilization schedule, all from the data you already have on hand! It turns out, the data reveals some interesting insights: The current fertilization schedule doesn't sync up with the soil's nutrient absorption rate, thereby leading to nutrient loss and decreased productivity. With this insight, you adjust your fertilizing schedule, align it with the absorption rate, and voila! The harvest yield improves significantly, impacting not just your revenues but also contributing towards sustainable farming practices. Now, that's the power of data analytics - turning data into gold (or in this case, bountiful harvest 🌽). Got a data puzzle to solve or want to learn more about the power of data analytics in various industries? Let's connect! Leave a comment or drop me a DM. #DataAnalytics #AgricultureIndustry #SustainableFarming #DataTransformations #LinkedInConnects

Scientific advances in predictive modeling now allow long-range weather forecasting, pathogen identification, spore transport across states and regions, and more. In the near future farmers may be able to use data-driven decision-support tools to prevent diseases, reduce costly crop inputs and determine the best crops to plant in each growing season. “With the help of the National Predictive Modeling Tool Initiative, combined with the work that plant pathologists have already accomplished, this modeling effort is going to deliver high-quality decision-making tools for farmers nationwide." Read more in Agriview: https://lnkd.in/eU_AQrEn.

Here’s my own real-time scenario demonstrating the need for 𝐝𝐨𝐦𝐚𝐢𝐧 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 and clearcut 𝐛𝐚𝐜𝐤𝐠𝐫𝐨𝐮𝐧𝐝 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 of 𝐝𝐚𝐭𝐚 !! While working on a dashboard in the 𝗮𝗴𝗿𝗶𝗰𝘂𝗹𝘁𝘂𝗿𝗲 𝗱𝗼𝗺𝗮𝗶𝗻, I encountered data containing information about various crops grown, details on regions like districts and villages, and the area of cultivation for each crop type. The data included around 15 different types of crops, typically collected from farmers. While creating the dashboard, I noticed that the data for the area of cultivation was inconsistent. Some records were entered in hectares, some in acres, others in square units, and some used local land measurement units like katha, bigha, etc. First, I spent time learning about the crop types, how they are grown, their growing duration, and whether they are seasonal or not. Then, I researched the local units and their measurement methods. Afterward, I converted all those units into a single unit, "acres," which is the most common for land measurement. With these conversions, I proceeded with creating the dashboard. This is a simple use case I have shared as an example. There are thousands of use cases that require thorough understanding before presenting insights. I post random learnings from my day-to-day work that I find valuable ! 🔽 Divakar U #dataanalytics #datascience #workexperince #domainknowledge #datainsights