Integrating Artificial Intelligence (AI) in agriculture marks a new era of precision and efficiency. Convolutional Neural Networks (CNNs) enable early crop disease detection through image-based classification, reducing yield loss. Long Short-Term Memory (LSTM) networks support predictive modelling for yield forecasting and soil health assessment, aiding resource allocation. While mechanization and automation remain global challenges, modern AI and machine learning (ML) applications have transformed agricultural practices. This review explores various AI tools, including ML algorithms, deep learning (DL) models, Internet of Things (IoT), and Decision Support Systems (DSS), and their role in addressing challenges like maximizing crop yield, precision irrigation, pest control, and informed decision-making. The paper further highlights AI applications in plant breeding, irrigation, logistics, and packaging. Despite the advancements, widespread adoption faces barriers such as high costs, privacy concerns, inadequate infrastructure, and limited technical knowledge. The review offers insights into both the potential and limitations of AI in agriculture.

The effect of UV light on growth, biomass, lipid accumulation and biodiesel properties of microalgae was studied. A Microalgae strain Chlorella sorokiniana UUIND6 was cultivated for 14 days as under LED light (Control) and microalgae were exposed to UV light (280–320 nm) in the middle of the photoperiod for 3 days. The growth rate of microalgae was analyzed by spectrophotometer and cell counting, while oil accumulation was analyzed by improved Nile red method. Results showed that microalgae under UV light treated algal cells showed less growth. FAMEs profile of UV treated algal cells mainly contains hexadecanoic acid (C16), stearic acid (C18) fatty acids. PUFA found in very less amount in UV treated cells as compared to control.

The present investigation has focused on developing an eco-friendly method to extract bioactive compounds from orange peel using natural deep eutectic solvents (NADES). NADES-II composed of choline chloride (ChCl) and ethylene glycol (1:2) and 50% water shows the maximal extraction yield with higher antioxidant activity in terms of DPPH and ABTS scavenging activity with a high total phenolic content (TPC) and total flavonoid content (TFC). The microwave-assisted extraction (MAE) process was optimized using response surface methodology (RSM) and an artificial neural network (ANN). ANN showed a higher value of R ² and lower values of other statistical parameters when compared to RSM. The ideal extraction conditions were optimized as a 13 min rising time, 52 °C temperature, a 21 min holding time, and a 20 mL/g liquid-to-solid ratio. MAE was compared with the conventional heating-stirring extraction (HSE) method using the NADES-II solvent under optimum conditions. The results show that higher extraction yield and antioxidant capacities (DPPH and ABTS), TPC, and TFC can be obtained from orange peel using the MAE process compared to the HSE process. Overall, this study shows an optimiztic approach for the extraction of bioactive compounds from an orange peel using eco-friendly solvents and microwave technology. It also highlights the potential of this approach for valorizing orange peel waste.