Authors - Suhail Manzoor, Rahul Gupta, Prakhar Sharma, Yash Mittal, Mohammad Arshad Iqbal Abstract - Plants are mainly suffering from abiotic stress such as drought, salinity, and widely temperature decrease or increase. Thanks to notable advancements in machine learning and hyperspectral imaging, detecting stress in plants has never been easier. For that matter, different machine learning techniques such as Random Forest, Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Kernel Ridge Regression have been used. Hyperspectral image has been widely used in classifying crop water stress by classifiers such as Random Forest and SVM. CNNs have been widely used for plant phenotyping under multiple stresses thanks to their good prediction results, but that entails many computation problems. Other methods, such as Kernel Ridge Regression and Extreme Gradient Boosting have emerged to target specific stress indicators like leaf reflectance spectra at key wavelengths; however, these typically rely on specialized equipment and significant data preprocessing. Here, we synthesize these various approaches to plant stress detection and present an integrated approach for abiotic stress recognition in plants and advocate for models with high generalizability across different environmental conditions or types of biotic stresses.
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