AN INTELLIGENT PLANT MONITORING AND EARLY WARNING SYSTEM BASED

Abstract

The present study is aimed at systematizing scientific knowledge about diseases of agricultural crops with the subsequent integration of the data obtained into automated agricultural production management systems. The relevance of the work is due to the need to minimize economic losses in crop production through early diagnosis of pathologies and optimization of phytosanitary control. As part of the study, a classification of plant diseases was carried out.The basil plant (Ocimum basilicum L.), characterized by high susceptibility to phytopathogens under intensive cultivation conditions, was chosen as a model object. To create an automated diagnostic tool, a specialized dataset was collected, including 214 images of basil at various stages of vegetation. The shooting was carried out under controlled conditions using an RGB camera. Each sample is annotated with the localization of damage and the affected area. Special attention is paid to the methodological aspects of the formation of data banks for biological systems. It has been established that the key problems are the high variability of morphological features in plants, the influence of environmental factors on the visual manifestations of diseases. Based on the analysis of the data obtained, the architecture of the early warning system is proposed, which includes three modules: a sensor unit – small cameras and microclimate sensors. The algorithmic block is a neural network model for semantic image segmentation and algorithms for assessing the dynamics of pathology development. The decision – making and notification interface provides recommendations for adjusting irrigation regimes, applying pesticides and trace elements. The convolutional neural network is trained based on the YOLOv11 framework using data augmentation methods (Gaussian noise, affine transformations) and transfer learning. Validation of the model on the test sample showed a detection accuracy of 74.7% (F1-score = 0.72). To reduce false positives, postprocessing of predictions has been implemented, taking into account the spatial and temporal correlation of the data. The developed prototype demonstrates the potential of integrating computer vision and agronomy to create predictive control systems. Further research is planned to expand the dataset and increase parametrs, as well as the introduction of data processing algorithms on edge devices to reduce delays in decision-making. The results obtained can be adapted for other indoor crops, which contributes to the development of precision agriculture and reduces anthropogenic stress on agroecosystems