Farming with Super Vision: Hyperspectral Technology in Agriculture-2

 
 
Project: Farming with Super Vision: Hyperspectral Technology in Agriculture-2
Work Package 5: Machine Learning Studies in Model Plants and Development of Predictive Artificial Intelligence Algorithms
 
This project aims to develop portable hyperspectral camera systems with high spectral resolution (0.6-9 nm) in the ranges of 350-1050 and 400-1700 nm, compatible with drones and automation systems. The developed system will be applicable in agricultural fields, greenhouses, and for monitoring individual agricultural products. Additionally, the project seeks to develop artificial intelligence for decision support in plant stress and nutrient requirements. This will involve hyperspectral monitoring of Arabidopsis thaliana in laboratory conditions and wheat (Triticum spp.) in field environments. Predictive AI will be developed using machine learning methods applied to data obtained through molecular methods.
You will develop deep learning algorithms to analyse plant images and make predictions over time, specifically CNN for image processing and LSTM for pattern recognition. First, you will process spectral images of plants with CNNs. Since different plant conditions may appear similar, you will use techniques like PCA and autoencoders to highlight differences. Simpler methods like decision trees will also be employed early in the project when data is limited.
Next, you will study how plants change over time using a combination of LSTM and CNN, which helps you understand both the images and their changes. You will incorporate results from the first step to enhance predictions. Although deep learning methods are accurate, they can be complex. Therefore, you will also use simpler models like EBM to explain which factors influence plant growth over time. Your main goals are:
·       To predict multiple plant characteristics with 80% accuracy (0.8 R² score)
·       To correctly identify plant conditions 90% of the time
This approach will help you improve plant growth using advanced technology.
 
References
https://www.lignonanoplatform.net/arastirma-programlari/arastirma-programi-4