A Comprehensive Review on Crop Stress Detection: Destructive, Non-Destructive, and ML-Based Approaches

Muhammad Aman^1,2*Zahid Ullah Khan³Javed Khan⁴Abdul Sattar Mashori^1,2Aamir Ali⁵Nida Jabeen⁶Ziqi Han²Fuzhong Li^2*

• ¹College of Agricultural Engineering, Shanxi Agricultural University, Taigu, Jinzhong 030801, China
• ²School of Software, Shanxi Agricultural University, Taigu Jinzhong 030801, China
• ³College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
• ⁴Department of Software Engineering, University of Science and Technology, Bannu 28100, KPK, Pakistan
• ⁵College of Agriculture, Shanxi Agricultural University, Taigu, Jinzhong 030801, China
• ⁶School of communications and Information Engineering. Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Agriculture stands as a foundational element of life, closely linked to the progress and development of society. Which includes both humans and animal that depends on a wide range of essential services from agriculture, such as producing oxygen and food, to supplying vital raw materials for clothing, medicine, and other necessities. Given agriculture's vital role in supporting individual well-being and driving global progress, it becomes essential to place a strong emphasis on its protection and long term sustainability. This is crucial for securing resources and maintaining environmental balance for future generations. In this context, in our review we have examined the various factors that can interfere with the normal physiological and developmental functions of plants and crops. These factors, scientifically referred as stressors or stress conditions, include a wide range of both biotic and abiotic challenges. In our work we have systematically addressed all major categories of stress that plants may encounter throughout their lifecycle. Additionally, because plants tend to exhibit recognizable physiological or biochemical responses under stress, we have cataloged the associated stress indicators. These indicators are identified through various assessment techniques, including both destructive and non-destructive approaches. A prominent advancement highlighted in our review is the integration of Machine Learning (ML) algorithms with non-destructive methodologies, which has substantially enhanced the accuracy, scalability, and real time capability of plant stress detection. These ML enhanced systems leverage from high dimensional data that is acquired through remote sensing modalities, such as hyperspectral imaging, thermal imaging, and chlorophyll fluorescence. That ultimately helps in the enabling of early identification of biotic and abiotic stress signatures. Through the advanced pattern recognition, feature extraction, and predictive modeling, ML facilitates proactive anomaly detection and stress forecasting, thereby mitigating yield losses and supporting data driven precision agriculture. This convergence represents a significant step toward intelligent, automated crop monitoring systems. Finally, we conclude the article with a concise discussion on the potential positive roles that certain stress conditions may play in enhancing plant resilience and productivity.