Leveraging the capabilities of artificial intelligence (AI
Leveraging the capabilities of artificial intelligence (AI), this study seeks to advance agricultural practices by systematically analyzing the interconnections between environmental variables, plant growth indicators, and pest occurrences. Distinct findings and methodologies encompass:
Methodological Approaches: Deployed an array of machine learning algorithms, namely Random Forest, XGBoost, Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM), to scrutinize variables such as pesticide efficiency, presence of biological predators, and environmental factors including temperature, humidity, pH, EC, and PAR.
Inter-Variable Correlations: Unearthed pivotal relationships, notably the pronounced link between ambient temperature and pest prevalence. Further, nuances of plant growth determinants, including illumination (outdoor lux), soil pH, and ambient moisture, were examined in relation to external influences, with a marked emphasis on the pronounced role of bees during specific growth phases (Plant Growth Rates I, II, III).
Data Enhancement Techniques: To augment the analytical accuracy, mutual information was utilized to prioritize salient features, while log(x+1) transformations rectified skewed datasets, bolstering the robustness of statistical interpretations.
Advanced Imaging Protocols: The amalgamation of hyperspectral and infrared imaging, in tandem with CNNs trained on an expansive dataset of over 3,000 images, paved the way for precocious, non-intrusive pest detection. The adoption of tiling techniques further refined image segmentation, especially for pests spanning diverse sizes.
Modeling Outcomes: Remarkable precision was achieved in prognosticating pest outbreaks and plant growth trajectories. The endeavor was channeled towards curtailing the Mean Absolute Error (MAE) for ensemble models, and diminishing epoch-wise training losses for neural architectures.
Comparative Model Analysis: The versatility of Random Forest and XGBoost was manifest in their adept handling of mixed data types and capturing intricate, non-linear interdependencies. In contrast, the depth of neural models, especially RNNs & LSTMs, was underscored by their prowess in elucidating sophisticated relationships, albeit at the cost of augmented data and computational requisites.
Visual Analytics: Insightful graphical renditions elucidated the potency of pesticides vis-à-vis pest counts, and the sway of sowing cycles on pest and pathogen mitigation. Comparative analyses showcased the congruence between empirical findings and model-based predictions.
Keywords: advance agricultural practices, machine learning, Random Forest, XGBoost, Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM), mutual information, convitatianal neural networks (CNNs), Visual Analytics”