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In a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.

The present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.

Empirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.

As a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.

Based on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.

The purpose of this paper is a new thin-film based sensor proposed for sensitive and selective detection of mercury (Hg²⁺) ions in water. The thin-film platform is easy to use and quick for heavy metal ions (HMIs) detection in the picomolar range. Ion-selective self-assembled monolayer's (SAM) of thiol used for the detection of HMIs above the Au/Ti top surface.

A thin-film based platform is suitable for the on-field experiments and testing of water samples. HMIs (antigen) and thiol-based SAM (antibody) interaction results change in surface morphology and topography. In this study, the authors have used different characterization techniques to check the selectivity of the proposed method. This change in the morphology and topography of thin-film sensor checked with Fourier-transform infrared spectroscopy, surface-enhanced Raman scattering spectroscopy, atomic force microscopy and scanning electron microscopy with energy dispersive x-ray analysis used for high-resolution images.

This thin-film based platform is straightforward to use and suitable for real-time detection of HMIs at the picomolar range. This thin-film based sensor platform capable of achieving a lower limit of detection (LOD) 27.42 ng/mL (136.56 pM) using SAM of Homocysteine-Pyridinedicarboxylic acid to detect Hg²⁺ ions.

A thin-film based technology is perfect for real-time testing and removal of HMIs, but the LOD is higher as compared to microcantilever-based devices.

The excessive use and commercialization of nanoparticle (NPs) are quickly expanding their toxic impact on health and the environment. The proposed method used the combination of thin-film and NPs, to overcome the limitation of NPs-based technique and have picomolar (136.56 pM) range of HMIs detection. The proposed thin-film-based sensor shows excellent repeatability and the method is highly reliable for toxic Hg²⁺ ions detection. The main advantage of the proposed thin-film sensor is its ability to selectively remove the Hg²⁺ ions from water samples just like a filter and a sensor for detection at picomolar range makes this method best among the other current-state of the art techniques.