PAMICRM: Improving Precision Agriculture Through Multimodal Image Analysis for Crop Water Requirement Estimation Using Multidomain Remote Sensing Data Samples

Abstract

The growing necessity for sustainable agriculture in the face of escalating global food demands and climate change underscores the importance of optimizing crop water usage. Current methodologies for estimating crop water requirements, primarily relying on traditional remote sensing and empirical models, face limitations in precision, adaptability to diverse crop types, and dynamic environmental conditions. This paper introduces an innovative machine learning model designed to accurately estimate crop water requirements and optimize irrigation scheduling using remote sensing data samples. The model’s robustness stems from its sophisticated pre-processing technique, employing a Grey Wolf Optimized (GWO)-based Non-Local Means (NLM) algorithm for denoising images sourced from various remote sensing technologies. Subsequently, these refined images undergo a transformation into multidomain features through the integration of a Vision Transformer, Fourier, Entropy, Color Map, and Gabor Maps. This multi-faceted approach ensures a comprehensive analysis of the data, capturing intricate details pertinent to crop water requirements. A key innovation in our model is the implementation of the Dual Elephant Herding Optimization (DEHO) Process. This optimizer adeptly selects features by minimizing intra-class differences and maximizing inter-class variance, thereby enhancing the model’s discriminative capabilities. The selected features are then classified into distinct irrigation schedule classes using a Deep Dyna Q Graph Convolutional Network (DDQGCN). This network not only augments the efficiency of classifying images from diverse sources but also bolsters the model’s capability to adapt to varying crop types and environmental conditions. The efficacy of the proposed model is further augmented by incorporating vector autoregressive moving average with eXogenous inputs (VARMAx) algorithms. This addition enables the conversion of output into temporal features, facilitating precise predictions of soil quality and the dynamic adjustment of irrigation schedules. Empirical testing of this model across different geographies on mango, rice, and cotton crops demonstrated significant improvements over existing methods. The model exhibited an 8.5% increase in precision, 8.3% in accuracy, 4.9% in recall, 5.5% in Area Under the Curve (AUC), and 4.5% in specificity, alongside a 3.5% reduction in response delays. Particularly noteworthy is the model’s enhanced pre-emption capabilities, reflected in a 2.9% higher precision of pre-emption, 2.5% greater accuracy, 1.5% increased recall, 2.4% higher AUC of pre-emption, 1.5% improved specificity, and a 1.9% reduction in delay compared to existing methodologies.

Keywords

• Precision agriculture
• remote sensing
• machine learning
• irrigation optimization
• environmental sustainability