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Paneer is a heat-acid-coagulated dairy product, and it has a very low shelf life due to its higher moisture content of about more than 50% (w.b). Drying is the best option to reduce moisture, but it takes time and affects the nature of the product. Here, osmotic dehydration (OD) is applied as pretreatment prior to drying to preserve the color and textural changes and reduce the drying time. Commercial NaCl was chosen as osmotic agent. The paneer samples were taken as ( 2   cm × 2   cm × 2   cm ) cubes and pretreated with NaCl, viz., 6%, 12%, 22% w / v , and surface treatment. The pretreated paneer was dried at 50, 55, and 60°C in a hot-air dryer with constant air velocity of 0.25 m/s. The equilibrium moisture content level was higher in osmotic treated samples compared to the control sample with range between 12.46 and 16.64% (w.b). The osmotic pretreated sample had shortened drying time compared to the control sample. Effective moisture diffusivity and activation energy were calculated, and the osmotic pretreatment impacted positively on it. The drying parameters were fitted with models and found that the Midilli model was ( R 2 > 0.99 ) best fit. The osmotic pretreated samples at 50°C drying temperature show ( P ≤ 0.01 ) better results in color, texture, and sensory profile of dried paneer, while pretreatment is not effective at 55 and 60°C drying temperatures. Except for the carbohydrate content, osmotic pretreated samples retain more fat and protein after the drying process. As a result, it can be concluded that osmotic pretreatment reduces the drying time and retains color, texture, and nutritional characteristics of dried paneer, especially at 50°C drying temperature.

Agriculture is a very prominent sector in our country and has been one of the highest contributors to the GDP. During the 1960s, an all-time high was reached with approximately 50% contribution to the GDP of the country, as more than half of the population was rural and focused primarily on agriculture as means of their livelihood. But from the latest records of 2019, the contribution by this sector has decreased to 15.96 percent. IoT plays a significant tole in remote sensing with machine learning in monitoring crops and surveying, which in turn aids agriculturists in ways for efficient field management. The proposed work integrates the role of Internet of Things and Deep learning deployment in farm management and disease identification of leaves. With the use of Internet of Things through remote sensing this work monitors the agriculture field parameters in remote cloud environment. With modified Resnet model deployed on the cloud for the purpose building a smart disease prediction. This system achieves 99.35% accuracy for the dataset. Overall this approach will provide an opportunity for agriculturists to test the plant disease with a smart phone connected to Internet and take appropriate actions.

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