A Comprehensive Study on Machine Learning-Based Techniques for Early Crop Stress Detection

A Comprehensive Study on Machine Learning-Based Techniques for Early Crop Stress Detection

Gausiya Yasmeen

Advanced Computing & Research Lab, Department of Computer Application, Integral University, Lucknow, India Email: gausiyay@iul.ac.in

Tasneem Ahmed

Advanced Computing & Research Lab, Department of Computer Application, Integral University, Lucknow, India Email: tasneemrke@gmail.com

Abstract Precision agriculture relies on accurate crop stress detection using advanced technologies like machine learning, deep learning, and digital image processing. These methods minimize losses from both biotic and abiotic stresses, reducing reliance on chemical inputs and optimizing resource utilization. However, challenges like high computational demands, limited data availability, and environmental variability need to be addressed. Machine learning approaches offer powerful tools for processing large datasets from satellite images, drone images, and environmental sensors, detecting early signs of stress. This study evaluates the performance, scalability, and accuracy of various machine learning techniques.

Keywords: Biotic and Abiotic Stress, Digital Image Processing, Machine Learning, Deep Learning, Plant Health.

1. Introduction

   Early detection of crop stress is critical for sustainable agriculture, as it minimizes yield losses and optimizes resource utilization. Machine learning (ML) has emerged as a transformative tool in this domain, enabling the analysis of complex datasets to identify stress indicators before they are visible to the naked eye. This study explores recent advancements in ML-based techniques for early crop stress detection, focusing on methodologies, applications, and challenges. The application of ML techniques for early crop stress detection is transforming modern agriculture [1]. By leveraging satellite images, computer vision, and deep learning, researchers are developing highly accurate models for detecting biotic (pests, diseases) and abiotic (drought, salinity, temperature extremes) stresses before they cause significant damage. Early stress detection enables precision agriculture, optimizing water, nutrient, and pesticide use while improving yields. This study explores recent advancements in ML-based crop stress detection, highlighting cutting-edge models, sensor technologies (hyperspectral imaging, thermal cameras, UAVs), and early warning systems for sustainable farming.

2. CROP STRESS

   Numerous biological and environmental stressors are continuously subjected to have an impact on their productivity, growth, and development. The picture illustrates the various biotic and abiotic stressors that cause plant stress, which manifests as outward signs like curling, discoloration, and structural damage [2]. By interfering with photosynthesis, water balance, and nutrient uptake, abiotic stressors such as excessive radiation, high temperatures, cold stress, water scarcity, and salt in the soil have a direct effect on plant

   metabolism. Conversely, biotic stresses like wind, air pollution, infections, insect infestations, and nutrient deficits further impair plant resilience, increasing their susceptibility to disease and yield reduction. Crops stress caused by various issues is shown in Fig. 1.

   Fig. 1. Various issues cause stress in crops.

   Fig.1 depicts a damaged leaf influenced by various environmental and biological stressors. Abiotic stressors include extreme radiation, high temperature, cold stress, water deficit, and soil salinity, which can cause leaf scorching, cellular damage, and reduced photosynthesis. Biotic stressors include wind, air pollution, pathogens, insects, and nutrient deficiency [3]. These factors can physically damage leaves, cause toxicity, and reduce respiration. Pathogens cause diseases, necrosis, and chlorosis, while insect infestations cause leaf damage and reduce productivity. Nutrient deficiency affects leaf coloration, structure, and metabolic activities. The leaf's discoloration and damage highlight the need for advanced monitoring techniques like satellite images and machine learning-based stress detection to ensure plant health [4].

   In agriculture, where crop health and food security are closely related, an understanding of these stressors is essential for early identification and efficient management [5]. A promising method for tracking and reducing crop stress in real-time is the combination of satellite images and machine learning techniques. Thus, Farmers and researchers can improve crop sustainability and resilience against climate change and environmental challenges by implementing

   tailored interventions that detect the causes and symptoms of early crop stress [6].

3. Machine Learning Techniques for Crop Stress Detection

   ML models have been successfully deployed in greenhouses to predict crop stress using microclimate and physiological data. Combining spectroscopy and ML techniques has shown promise for identifying stress-related parameters [7]. Deep learning models like convolutional neural networks and imaging-based methods have improved crop identification. Advanced ML and deep learning algorithms like long short-term memory (LSTM) and support vector machine (SVM) have been applied to detect drought-induced stress [8]. ML models can also detect early-stage crop diseases, helping farmers take preventive actions and improve yields. Machine learning methods used for wheat disease diagnosis between 2020 and 2024 are thoroughly summarized in Table 1.

   Table 1. ML Techniques used by different authors to detect wheat crop disease

   S.No .	Machine Learning Techniques for Wheat Disease Detection (20202024)
   	ML Technique	Disease Detected	Reference
   1.	Convolutional Neural Networks (CNN)	Leaf rust, powdery mildew	19
   2.	Hybrid Bayesian-Machine Learning	Genetic traits for disease resistance	7
   3.	Support Vector Machines (SVM)	Stripe rust	10
   4.	Random Forest (RF)	Fusarium head blight	15
   5.	Deep Learning (ResNet, DenseNet)	Multiple wheat diseases	19

   From Table 1, it is observed that with a focus on leaf rust and powdery mildew, the first method, convolutional neural networks (CNN), has been extensively employed for image classification and detection of wheat diseases. CNN is an effective method for spotting visual patterns linked to plant diseases because of its capacity to extract hierarchical characteristics from images. Hybrid Bayesian-Machine Learning, another cutting-edge method, has been used to examine genetic characteristics associated with wheat disease resistance [9]. Through the combination of Bayesian probability and machine learning, this method enables scientists to predict and pinpoint the genes that improve resistance to wheat illnesses [8].

   In particular, stripe rust, a prevalent fungal disease that affects wheat crops, has been detected using traditional ML models like SVM. It performs well on tasks involving the categorization of diseases and is renowned for its efficacy in high-dimensional areas [7]. Similarly, Fusarium head blight, a damaging fungal disese in wheat, has been identified using the random forest (RF) method, which is an ensemble learning technique, and is a quite reliable option for disease identification in agricultural research since it combines several decision trees to increase classification accuracy.

   Finally, the detection of several wheat illnesses has made substantial use of deep learning (DL) models like ResNet and DenseNet. Plant disease classification accuracy is greatly increased by these structures, which are renowned for their deep feature extraction capabilities. They have been quite successful in recognizing a variety of wheat diseases due to

   their capacity to catch complex patterns in wheat leaf images [10]. All things considered, these ML methods show notable progress in the identification of wheat diseases, increasing the precision and effectiveness of determining plant health conditions, which eventually helps with improved crop management and disease prevention tactics.

4. Process of crop stress detection

   Crop stress detection is a methodical process that uses deep learning and sophisticated ML algorithms to discover and categorize different stressors that influence crops. A structured pipeline for classifying wheat leaf diseases is shown in Fig 2; this pipeline can be expanded to other general crop stress detection applications [9]. A wheat leaf picture dataset is first gathered, and then pre-processing techniques including background removal, image improvement, and resizing are applied to enhance image quality and get the data ready for analysis [10]. Following pre-processing steps, the images are divided into various disease classifications, such as tan spot, powdery mildew, septoria leaf blotch, rust, and healthy samples. Data augmentation methods including rotation, flipping, zooming, brightness modification, and other manipulations are used to improve model performance. After that, features are extracted and classified using a deep learning model based on CNNs, which recognize complex patterns in the photos to differentiate between different disease classifications [11]. To ensure accurate classification, the trained model is then assessed using performance metrics such as accuracy, precision, recall, and overall score. Precision agriculture and prompt action to safeguard crop health are made possible by this systematic method, which offers an automated and effective means of identifying crop stress brought on by diseases [12]. A flowchart of proposed early crop stress classifying wheat leaf disease using deep learning is shown in Fig. 2.

   It starts with a "Wheat Leaf Images Dataset," which is pre- processed using techniques including scaling, image improvement, and background removal. Five classes such as Rust, Powdery Mildew, Septoria Leaf Blotch, Tan Spot, and Healthy are then created from the pre-processed images [13]. For reference, some images of both healthy and sick wheat leaves are displayed. To improve model generalization, data augmentation methods such as flipping, zooming, rotating, brightness adjustment and other adjustments are then used. A CNN model is then fed to the enhanced dataset to learn features and classify them.

5. Benefits of ML-Based Stress Detection

   A revolutionary approach to precision agriculture is provided by ML-based stress detection in crops, which makes it possible to identify and mitigate a variety of stress factors early on, including illnesses, pest infestations, nutritional shortages, and water scarcity [14]. ML-driven models use satellite images, remote sensing data, and deep learning algorithms to deliver automated, high-accuracy stress detection, in contrast to traditional methods that rely on manual examination and can be laborious and error-prone [15]. These algorithms assist farmers and researchers in making data- driven decisions by analyzing multispectral and hyperspectral images to identify minute changes in vegetation indices like NDVI (Normalized Difference Vegetation Index), NDWI

   (Normalized Difference Water Index), and PSSRA (Plant Senescence Reflectance Index – Structure Adjusted) Stress detection is made quicker, more accurate, and scalable by combining ML techniques such as SVM, RF, and CNN [16]. This minimizes yield losses and maximizes resource allocation. Additionally, smart irrigation, targeted fertilization, and early disease control are supported by real-time monitoring using machine learning models, greatly improving crop health, productivity, and sustainability in contemporary agriculture. The following are some key benefits of ML-based crop detection as follows:

   Fig. 2. Process of Early Crop Stress Detection using Machine Learning (ML)

   1. Early detection allows for timely interventions, reducing crop losses.

   2. Optimized resource use (e.g., water, fertilizers) lowers costs and environmental impacts.

   3. Enhanced decision-making through real-time monitoring improves productivity.

6. Challenges of ML-based Crop Detection

   Crop stress detection using ML has many benefits, but several drawbacks prevent its broad use in agriculture. The

   quality and availability of labeled datasets are a significant obstacle because training machine learning models necessitates big, well-annotated datasets, which can be hard to come by, particularly for certain crop diseases and stressors. Furthermore, model accuracy may be impacted by environmental elements including lighting, soil types, weather variations, and seasonal shifts, making cross-regional generalization challenging.

   Hardware restrictions and computational complexity present another significant obstacle. Not all farmers or agricultural researchers may have access to high-performance graphics processing unit (GPU) and substantial computational resources, which are necessary for advanced models, particularly deep learning architectures like convolutional neural networks (CNN) and U-shaped Convolutional Neural Networks (U-Net) [17]. Furthermore, many traditional farms lack the digital infrastructure required to support ML-driven stress detection, making integration with real-world farming systems a challenge.

   Given that many machine learning models operate as "black boxes," making it challenging for farmers to comprehend the logic behind forecasts, model interpretability, and explainability are also crucial concerns. For ML-based solutions to be adopted by users, it is essential that they offer clear, useful insights. Cost and scalability are further issues, especially for small-scale farmers who would find it difficult to afford high-resolution satellite images, UAVs (drones), or sensor-based monitoring systems [18]. Last but not least, employing cloud-based machine learning systems raises privacy and security issues because sensitive agricultural data may be handled or kept externally. To increase the efficacy and uptake of ML-based agricultural stress detection, these issues must be resolved via better data-gathering techniques, strong model generalization, edge computing, and farmer- friendly AI interfaces. Here are a few highlighted points:

   1. Data Quality: The accuracy of ML models depends heavily on the availability of high-quality datasets. For instance, limited data reduced the performance of simplified models in greenhouse studies.

   2. Scalability: Applying these technologies across diverse agricultural landscapes requires significant computational resources and infrastructure.

   3. Model Interpretability: Complex ML models like deep learning often lack transparency, making it difficult for farmers to understand predictions without expert assistance.

7. Future Directions

   The combination of digital image processing (DIP), Internet of Things (IoT), edge computing, and sophisticated neural network architectures is expected to propel major breakthroughs in ML and DL in crop stress detection in the future. The creation of more resilient and broadly applicable models that can adjust to various environmental circumstances and lessen reliance on location-specific training data is an important avenue. This can be accomplished using self- supervised learning and transfer learning, which enables models to adjust for regional agricultural conditions while utilizing knowledge from global databases.

   By strengthening feature extraction and segmentation capabilities, DL architectures including U-Net, ResNet, and Transformer-based models are anticipated to be essential in advancing image-based stress detection. DL models combined with hyperspectral and multispectral images will allow for more accurate early-stage crop stress identification, even before outward signs manifest. Furthermore, quick stress diagnosis and mitigation techniques will be made possible by real-time monitoring employing edge AI, satellite imaging, and UAVs (drones) [19]. Combining weather, plant physiological data, and soil health measurements with ML algorithms to provide a comprehensive approach to crop health monitoring is another exciting direction.

   Explainable AI (XAI) will also play a key role in increasing the transparency of ML/DL models, allowing agronomists and farmers to analyze forecasts and make well-informed decisions. Additionally, low-cost AI-powered smartphone apps and automated advice systems can help close the technological divide and enable smallholder farmers to use precision agriculture. AI research and government policies will probably work together more in the future to ensure scalable, sustainable, and farmer-friendly ML solutions for world food security. Some key points can be summarized as follows:

   1. Developing larger, high-quality datasets to improve model performance.

   2. Enhancing interpretability through explainable AI techniques.

   3. Integrating ML with Internet of Things (IoT) devices for seamless real-time analysis.

   4. Expanding research on multi-stress scenarios involving drought, pests, and nutrient deficiencies simultaneously.

8. Conclusion

Crop stress detection has been transformed by machine learning and deep learning, which provide effective, scalable, and automated methods for detecting plant illnesses. However, issues including interpretability of the model, computational complexity, environmental variability, and data availability continue to exist. To get beyond these obstacles, multimodal data fusion, explainable AI, edge computing, and transfer learning must all advance. Real-time monitoring, sensor-based data integration, hyperspectral imaging, AI- driven advisory systems, and cloud-based precision agriculture technologies will all influence the future of machine learning and deep learning in agriculture. This technique could improve agricultural resilience to disease outbreaks and climate change, maximize resource use, and increase global food security.

Acknowledgment

The Authors are thankful to the Advanced Computing Research Laboratory, Department of Computer Application, Integral University, Lucknow for providing the necessary support to carry out this work.

References

[1]. Amarasingam, N., Ashan Salgadoe, A. S., Powell, K., Gonzalez, L. F., & Natarajan, S. (2022). A review of UAV platforms, sensors, and applications for monitoring sugarcane crops. In Remote Sensing Applications: Society and Environment (Vol. 26).

[2]. Awasthi, A., Reddy, S. R. N., & Reddy, & S. R. N. (2013). Monitoring for Precision Agriculture using Wireless Sensor Network-A review. Global Journal of Computer Science and Technology, 13(7).

[3]. Bahrami, H., McNairn, H., Mahdianpari, M., & Homayouni, S. (2022). A Meta-Analysis of Remote Sensing Technologies and Methodologies for Crop Characterization. In Remote Sensing (Vol. 14, Issue 22).

[4]. Basso, B., & Antle, J. (2020). Digital agriculture to design sustainable agricultural systems. In Nature Sustainability (Vol. 3, Issue 4).

[5]. Lungu, O. N., Chabala, L. M., & Shepande, C. (2020). Satellite-Based Crop Monitoring and Yield EstimationA Review. Journal of Agricultural Science, 13(1). https://doi.org/10.5539/jas.v13n1p180

[6]. Onojeghuo, A. O., Blackburn, G. A., Huang, J., Kindred, D., & Huang,

W. (2018). Applications of satellite hyper-sensing in Chinese agriculture: Challenges and opportunities. In International Journal of Applied Earth Observation and Geoinformation (Vol. 64). https://doi.org/10.1016/j.jag.2017.09.005

[7]. Gupta, R., Sharma, S., & Kumar, V. (2023). Wheat disease detection and classification using ResNet152. International Journal of Creative Research Thoughts. Retrieved from https://ijcrt.org/papers/IJCRT2305069.pdf.

[8]. Zhang, Y., Li, H., & Wang, T. (2024). Enhancing wheat spike counting and disease detection using a probability density attention mechanism. Plants, 13(24), 3462. Retrieved from https://www.mdpi.com/2223- 7747/13/24/3462

[9]. Wang, J., & Liu, X. (2024). Fusarium head blight detection, spikelet estimation, and severity assessment in wheat using 3D convolutional neural networks. arXiv preprint. Retrieved from https://arxiv.org/abs/2303.05634

[10]. B. Niu, Editorial: Applications of bioinformatics, machine learning and risk analysis for microbial food safety, Frontiers in Microbiology, vol. 15, 2024. Doi: https://doi.org/10.3389/fmicb.2024.1414261

[11]. R. J. Sternberg and L.-F. Zhang, Perspectives on Thinking, Learning, and Cognitive Styles. New York, NY, USA: Routledge, 2014.

[12]. Wu, B., Zhang, M., Zeng, H., Tian, F., Potgieter, A. B., Qin, X., Yan,

N., Chang, S., Zhao, Y., Dong, Q., Boken, V., Plotnikov, D., Guo, H.,

Wu, F., Zhao, H., Deronde, B., Tits, L., & Loupian, E. (2023). Challenges and opportunities in remote sensing-based crop monitoring: a review. In National Science Review (Vol. 10, Issue 4).

[13]. Alami Machichi, M., mansouri, loubna el, imani, yasmina, Bourja, O., Lahlou, O., Zennayi, Y., Bourzeix, F., Hanadé Houmma, I., & Hadria,

R. (2023). Crop mapping using supervised machine learning and deep learning: a systematic literature review. In International Journal of Remote Sensing (Vol. 44, Issue 8).

[14]. Chithambarathanu, M., & Jeyakumar, M. K. (2023). Survey on crop pest detection using deep learning and machine learning approaches. Multimedia Tools and Applications.

[15]. Domingues, T., Brandão, T., & Ferreira, J. C. (2022). Machine Learning for Detection and Prediction of Crop Diseases and Pests: A Comprehensive Survey. In Agriculture (Switzerland) (Vol. 12, Issue 9).

[16]. Kirongo, C. A. (2016). A Review of Image Processing Software Techniques for Early Detection of Plant Drought Stress. International Journal of Computer Applications Technology and Research, 5(6).

[17]. Segarra, J., Buchaillot, M. L., Araus, J. L., & Kefauver, S. C. (2020). Remote sensing for precision agriculture: Sentinel-2 improved features and applications. In Agronomy (Vol. 10, Issue 5).

[18]. Skakun, S., Franch, B., Vermote, E., Roger, J. C., Becker-Reshef, I., Justice, C., & Kussul, N. (2017). Early season large-area winter crop mapping using MODIS NDVI data, growing degree days information and a Gaussian mixture model. Remote Sensing of Environment, 195.

[19]. Wang, C., Liu, B., Liu, L., Zhu, Y., Hou, J., Liu, P., & Li, X. (2021). A

review of deep learning used in the hyperspectral image analysis for agriculture. Artificial Intelligence Review, 54(7).