Open-Source Agronomists' Python Library: CWSI and Transpiration Modeling

 

I'm excited to announce the availability of a Python version of the CWSI and transpiration module/class originally developed in C++ for embedded systems (see this post: 𝗕𝗶𝗼𝗽𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗠𝗼𝗱𝗲𝗹𝘀 𝗳𝗼𝗿 𝗖𝗪𝗦𝗜 & 𝗧𝗿𝗮𝗻𝘀𝗽𝗶𝗿𝗮𝘁𝗶𝗼𝗻 𝗖𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀 (𝗖++)).

𝗪𝗵𝗮𝘁 𝗶𝘀 𝗖𝗪𝗦𝗜?

CWSI is a reliable indicator of plant water stress, offering a valuable alternative or complement to traditional soil moisture and soil water potential sensors.

𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗧𝗿𝗮𝗻𝘀𝗽𝗶𝗿𝗮𝘁𝗶𝗼𝗻

 𝗔𝗰𝘁𝘂𝗮𝗹 𝗧𝗿𝗮𝗻𝘀𝗽𝗶𝗿𝗮𝘁𝗶𝗼𝗻 (𝗧𝗮): The amount of water a plant loses through its leaves to the surrounding air.

 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗧𝗿𝗮𝗻𝘀𝗽𝗶𝗿𝗮𝘁𝗶𝗼𝗻 (𝗧𝗽): The maximum amount of water a plant could lose under ideal conditions.


𝗕𝗶𝗼𝗽𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵

By taking a biophysical approach, these models are designed to be site-independent, making them potentially applicable to a wide range of crops. However, crop-specific calibration might be necessary for optimal performance.

𝗜𝗻𝗽𝘂𝘁 𝗗𝗮𝘁𝗮:

 𝗧𝗽 𝗠𝗼𝗱𝗲𝗹: Microclimate parameters (air temperature, solar radiation, relative humidity, wind speed)

 𝗧𝗮 𝗠𝗼𝗱𝗲𝗹 𝗮𝗻𝗱 𝗖𝗪𝗦𝗜: Canopy surface temperatures in addition to microclimate data

𝗟𝗶𝗻𝗸 𝘁𝗼 𝘁𝗵𝗲 𝗚𝗶𝘁𝗛𝘂𝗯 𝗿𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝘆

https://github.com/envitronicslab/CWSI_Function

Comments

Post a Comment

Popular posts from this blog

Leveraging Microclimate Data, Proximal Canopy Sensing, and Machine Learning for Precise Soil Water Potential Prediction

Image
  Introduction Accurately predicting soil water potential is crucial for optimizing irrigation practices and minimizing water waste in agriculture. This project delves into the critical relationship between high resolution, in-field microclimate measurements, canopy surface temperature , time of day, and soil water content and potential. Building upon our previous work in biophysical modeling , we employ advanced machine learning techniques to investigate the impact of measurement timing on prediction accuracy. Enhanced Prediction with Machine Learning This project utilizes a range of machine learning algorithms, including: Linear Regression:  Establishes a linear relationship between features and target variables. Decision Tree Regressor:  Makes decisions based on a series of if-else questions. Random Forest Regressor:  An ensemble method combining multiple decision trees for improved accuracy and reduced overfitting. Gradient Boosting Regressor:  Iteratively b...
Read more

The Rise of the "Human Data Farms": A Satirical Look at the Future of AI

Image
  The race for Artificial General Intelligence (AGI) has reached a fever pitch, with tech giants vying for dominance in the AI landscape. As Elon Musk has pointed out ( https://www.theguardian.com/technology/2025/jan/09/elon-musk-data-ai-training-artificial-intelligence ), the increasing scarcity of data for training AI models is a significant challenge. But as data becomes increasingly scarce, a new and perhaps more sinister solution is emerging: the rise of the "Data Farms." Imagine a dystopian future where tech companies, desperate for the raw materials needed to fuel their AI models, begin constructing self-contained communities. These "Human Data Farms" would resemble a hyper-surveilled version of The Truman Show, where residents are paid handsomely to live their lives under constant observation. Every interaction, every conversation, every emotion – all meticulously recorded and fed into the insatiable maw of AI algorithms. This scenario, while seemingly absur...
Read more

For CEA Growers: Tips for Substrate Monitoring and Automation

Image
  Based on my communications and meetings with our customers on solutions for substrate monitoring and decision making in controlled environment agriculture (CEA), I have identified a numbers of important areas to briefly discuss:   1) Sensor quantity optimization Most growers do not need a lot of sensors to monitor their grow rooms. There are good ways of determining the optimum number of sensors required for different applications. In addition to an unnecessarily high cost, a large number of sensors in one facility can create serious design, installation and maintenance headaches for different parties involved. More data does not translate into better insight or decision-making unless we know how to deal with “big data”. Large amount of data that grower does not know what to do with leads to frustration and mistrust in the technology.   More data does not translate into better insight or decisions unless we know how to deal with “big data”. Large amount of data that gro...
Read more