Search

The synergy between artificial intelligence (AI) and agricultural sciences has garnered substantial attention, especially in the realm of crop yield prediction. The present bibliometric analysis examines the worldwide research trends about the application of AI in predicting crop yields. The global literature on crop yield prediction using AI published between 1997 and 2022 is searched in the Scopus database. Five hundred and forty research articles were used to compile the analysis; they were located in the Scopus database and processed through the VOSviewer. Our research reveals a significant surge in scholarly publications, particularly focusing on countries including China, the United States, India and Canada. These research endeavours aim to apply AI methodologies for forecasting agricultural produce yields in tandem with developments in remote sensing technologies that facilitate more accurate yield predictions. These insights offer a valuable reference for researchers and illuminate potential directions for future investigations in the domain of AI-based crop yield prediction. [ABSTRACT FROM AUTHOR]

Plants intricately deploy defense systems to counter diverse biotic and abiotic stresses. Omics technologies, spanning genomics, transcriptomics, proteomics, and metabolomics, have revolutionized the exploration of plant defense mechanisms, unraveling molecular intricacies in response to various stressors. However, the complexity and scale of omics data necessitate sophisticated analytical tools for meaningful insights. This review delves into the application of artificial intelligence algorithms, particularly machine learning and deep learning, as promising approaches for deciphering complex omics data in plant defense research. The overview encompasses key omics techniques and addresses the challenges and limitations inherent in current AI-assisted omics approaches. Moreover, it contemplates potential future directions in this dynamic field. In summary, AI-assisted omics techniques present a robust toolkit, enabling a profound understanding of the molecular foundations of plant defense and paving the way for more effective crop protection strategies amidst climate change and emerging diseases. [ABSTRACT FROM AUTHOR]

The study analysed the investment and returns of installing solar-powered pumps, besides their economic feasibility and efficiency in arid and sub-humid regions of Rajasthan based on primary data collected for the year 2018-19. The feasibility analysis while accounting for different climatic regions of Rajasthan is imperative to examine since it influences the depth of water table and required power to extract groundwater which would, affect economic feasibility of pumps. Only subsidised investment in solarpowered tubewells was found to be economically feasible in both regions. The efficiencies of solar-irrigated farms and savings in cost after installing solar-powered pumps were found to be higher for sub-humid region, which may be attributed to better utilisation of solar pumps, cultivation of high-value vegetables and adoption of improved technologies. The major constraint faced by farmers in adoption of solar-powered pumps in arid region was found to be their non-functioning in higher water table situations, while in sub-humid region, it was a high initial investment. Hence, there is need to have custom-made policies according to climatic regions of the country and to increase awareness regarding benefits of adoption of advanced technologies and high-value crops which can complement the benefits of adoption of solar-powered pumps. [ABSTRACT FROM AUTHOR]

For the modelling of time series data having long memory properties, we generally use the autoregressive fractionally integrated moving average (ARFIMA) model. This model performs well compared to the autoregressive integrated moving average (ARIMA) model. However, it cannot capture the nonlinear property of the data. In order to achieve the desired and accurate forecasts, hybridizing the existing forecasting models is an important technique. The hybrid time-series model combines the strength of individual models. Accordingly, this study proposes a hybrid model based on ARFIMA and extreme learning machine (ELM) for agricultural time-series data with long memory properties. For evaluation of the proposed model, the daily mustard price ('/q) of Agra and Bharatpur markets from 1 January 2016 to 31 January 2020 was used. Empirical results show that the forecasting performance of the proposed hybrid model based on ARFIMA and ELM is better than the existing models. [ABSTRACT FROM AUTHOR]