Achievements

 Development of precision Soil fertilizer recommendation system:

By combining data from soil wet chemistry, digital soil mapping, and target yield approaches, an e- Precision Fertilizer Recommendation System has been developed. This system can provide farmers with personalised fertilizer recommendations based on their specific field conditions and target yield approaches, enabling them to optimize their crop yields and improve their overall farming practices. This software is accessed via http://65.0.105.229:8080/pfrs/

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    Development of Mobile -based precision soil fertilizer recommendation system (PFRS)

The foundation of all crop production activities is soil, making it the most valuable resource. The decrease in soil fertility is a crucial factor that directly impacts crop productivity. Blanket crop production technologies, including fertilizer application, have accelerated the situation over the decades.  Due to blanket fertilizer recommendations, the application of nutrients is often not well matched to the requirement of the soil and crop. Also, excessive, non-judicious, and imbalanced use of chemical fertilizers can result in the deterioration of soil health. This is becoming a cause for concern for Indian agriculture. The solution lies in part in having a precise, site-specific nutrient management approach that will build a sustainable and profitable agriculture sector. To address this, a mobile-based decision support system is developed, integrating digital soil maps, geospatial tools, and target-based fertilizer recommendations. It is very handy and user-friendly.

Demonstration of Mobile -based precision soil fertilizer recommendation system (PFRS)

    Development of spectra library for Indian soil with IARI and NBSS&LUP.

An efficient method for quickly evaluating soil health and classification is essential, and one such technique is the use of remote sensing applications. By using ground-based sensors to obtain soil spectral data, it is now possible to characterize this data and advance techniques for quantifying soil attributes. However, to achieve this, creating a comprehensive soil spectral database or library is necessary. To create this database, geo-referenced soil samples based on soil variability using conditioned Latin hypercube methodology were collected. These samples were analyzed for soil properties such as pH, EC, particle size, SOC, available nitrogen, available phosphorus, and available potassium. The soil spectral data were obtained using the FieldSpec Pro: Analytical Spectral Devices, Boulder, Colorado spectroradiometer, and wavelengths ranging from 350-2,500 nm. By taking the average of 30 scans performed by the sensor, the reflectance of each sample was calculated. The spectral reflectance graph and attached soil attributes for each sample can be viewed by selecting the point on the map below.