Wavelet decomposition in signal processing has been widely used in the literature. The popularity of machine learning (ML) algorithms is increasing day by day in agriculture, from irrigation scheduling and yield prediction to price prediction. It is quite interesting to study wavelet-based stochastic and ML models to appropriately choose the most suitable wavelet filters to predict agricultural commodity prices. In the present study, some popular wavelet filters, such as Haar, Daubechies (D4), Coiflet (C6), best localized (BL14), and least asymmetric (LA8), were considered. Daily wholesale price data of onions from three major Indian markets, namely Bengaluru, Delhi, and Lasalgaon, were used to illustrate the potential of different wavelet filters. The performance of wavelet-based models was compared with that of benchmark models. It was observed that, in general, the wavelet-based combination models outperformed other models. Moreover, wavelet decomposition with the Haar filter followed by application of the random forest (RF) model gave better prediction accuracy than other combinations as well as other individual models.

Seasonal production, weather abnormalities, and high perishability introduce a high degree of volatility to potato prices. Price volatility is said to be asymmetric when positive and negative shocks of the same magnitude affect it in a dissimilar way. GARCH is a symmetric model, and it cannot capture asymmetric price volatility. EGARCH, APARCH, and GJR-GARCH models are popularly used to capture asymmetric price volatility. In this paper, an attempt is made to model the price volatility of the weekly wholesale modal price of potatoes for the Agra, Ahmedabad, Bengaluru, Delhi, Kolkata, and Mumbai markets using the above-mentioned models. The News Impact Curves (NICs) are derived from the fitted models, which confirmed the presence of asymmetry in the price volatility. To this end, NICs are used to describe the degree of asymmetry in volatility present in different markets.

Artificial intelligence (AI) is highlighted as a crucial enabler of a circular agriculture economy. Utilising advanced data analytics and predictive modeling, AI allows precise monitoring, analysis, and optimisation of various agricultural processes. AI-driven predictive analytics further optimise resource allocation, reducing the overuse of fertilisers, pesticides, and water while maximising yields and profitability. Furthermore, AI-powered supply chain management enhances traceability and transparency, enabling stakeholders to identify inefficiencies, mitigate risks, and minimise food loss and waste. Additionally, AI facilitates the valorisation of agricultural by-products and waste through innovative recycling and upcycling initiatives, transforming organic waste into valuable inputs such as biofuels, fertilisers, and animal feed. Overall, this chapter delves into the transformative potential of AI-driven circular economy approaches within the agricultural sector, emphasising the critical need for waste reduction, resource optimisation, and enhanced efficiency.