Description : | Science and technology aided smart agricultural monitoring cum management practices can make farming not only efficient but also make it sustainable by optimizing the resource usage. In smart agriculture, the science based solutions is integrated into agro-technology machineries with the help of information technology for efficient day to day crop management. Such solutions are fed to agro-machineries through IT enabled systems to atomize farm operations in a precise and optimal way to improve soil and plant health. A major bottleneck in current crop management practices in India is that mostly they are not designed based on objective methods aided by state-of-the-art science and technology solutions. Drawback of existing method is such that it fails in reflecting actual requirement of crop under different stress (both biotic and abiotic) conditions and that often leads towards overexploitation which in turn can result in degrading soil health and scarcity of available resources in long term. The actual challenge is to design crop specific management practice to improve efficiency and yield without compromising long term sustainability of cropping system. Standalone component specific practices designed on experimental farm fields often fail to deliver in real farm field because such approach hardly takes feedback among different components of cropping system. Integrating advanced efficient agro-technologies with science knowledge based tool can optimize resource utilization without compromising yield. For example, a UAV on-board targeted variable rate spraying of precise quantity of fertilizer/pesticide at targeted locations identified through an efficient predictive image analytics system can not only save time but also preserve resource and in long run reduce adverse environmental impacts. Another important aspect of system thinking based integrated modeling approach is that it enables us to test the suitable interventions in the model itself before testing/implementing in real farm filed. This approach makes sure that only suitable interventions are tested on filed before implementation which prevents adverse impacts as well as save cost and time.
The approach described above requires multidisciplinary expertise of science, engineering and technology under a same umbrella to deliver the desired output in time bound manner. The CSIR is a niche organization in the country having multidisciplinary expertise in the above-mentioned areas and is a suitable candidate to develop and implement these smart solutions in the field. This mission mode project is aimed to bring different expertise of CSIR labs such as modelling and predictive analytics, indigenous drone-based surveillance and spraying, IoT based measurement systems, capability in developing indigenous agro-equipments and controlled environment agriculture using grow lights under a same umbrella for region specific smart agricultural management for sustainable improvement in soil and plant health. Capacity building workshops will be conducted at different participating CSIR labs to provide awareness/training to interested agro-industries, startups, agricultural universities, farming communities etc.
Most important technological interventions planned are
1) Soil and Crop monitoring cum management
2) Crop Stress (biotic and abiotic)
3) Agro-technology for Precision farming
4) Crop yield mapping and prediction
The objective and deliverables of the proposed project is described below
Focused objectives
Development of an integrated predictive analytic cum modelling system supported by agro-technologies to draw out field implementable intervention strategies to enhance farm productivity in select agro-ecosystems (Rice, Apple, Mint, Saffron, Gerbera). After successful implementation of the proposed deliverables, the scope will be widened to other crops of national importance by inclusive participation of industry/stakeholders in the next phases.
Expected Outcome
Enhancing quality produce and farmers’ income through targeted interventions at farm fields. Capacity building workshops/training will be conducted for knowledge sharing during the project involving all stake holders, ICAR institutions, Universities, industries and FPOs. In addition, strategies will be worked out to upscale the program by widening the scope (expanding to other crops) in the next phases and building partnership with suitable industries/ stake holders.
Deliverables with detailed specifications
The mission envisages the following significant deliverables for improving plant and soil health:
1.Smart Agrotechnologies for precision farming
c. UAV based on-board targeted variable-rate spraying Pesticides/fertilizer- Prototype
iv. UAV based geo-tagged multispectral, proximal hyperspectral and lidar images during different crop stages and predictive image analytic algorithm to identify stress locations based on multi-spectral field images. Variable rate sprayer on board UAV to apply pesticides/fertilizer on identified precise locations
v. Algorithm for crop growth monitoring using multi-spectral images for identifying phenotypic variability and use this to identify crop stress
vi. Precise (location and quantity) application of soil nutrients based on soil health map generated by Soil Optix sensor
b. Tools for early detection of Pest and disease driven by microclimate observations
iii. IoT based micro climate measurements (under and above the canopy) during the entire crop season and Campaign mode field measurements at various stages of the crop to measure soil, crop and environment parameters
iv. Multivariate predictive (dynamical as well as data driven) tool for early detection biotic stress cross validated with the predictive image analytic tool
c. Agrotechnology for precision farming
vii. Prototype of controlled environment agriculture (aeroponics, hydroponics and vertical farming) using grow lights and IoT
viii. Indigenous IoT data logging system to measure multiple parameters (Soil and Meteorological) to be deployed for field measurements
ix. Soil Compaction Meter for measuring Soil Strength and to recommend appropriate tillage interventions
x. Granular fertilizer application system for the variable rate application of fertilizer during the basal dressing.
xi. Indigenous variable rate applicator for aerial spraying pesticides/fertilizer at targeted locations.
xii. A low-cost indigenous high-throughput phenotyping system to assess water use efficiency
2. Field implementable intervention strategies
System Dynamical Modeling (SDM) will be developed to design and test interventions for sustainable improvement in soil and plant health. This involves integration of component models through deriving nonlinear cause – effect relationships using deep learning techniques. SDM simulation will be carried out and field validated. Tested and validated SDM will be used to design field implementable intervention strategies
Different Verticals of the project
1. Airborne and Spaceborne Remote Sensing Technologies
a. Soil health and Crop monitoring
b. Agri-input optimization
c. Smart Irrigation management
d. Abiotic and biotic stress management
2. Internet of Things Technologies for Agriculture 4.0
a. Controlled Environment Agriculture
b. Site specific soil and crop management
3. Sensor Technologies for reducing the farm inputs
a. Crop phenotyping
b. Variable Rate Application
c. Hydroponics and Aeroponics
d. Spatio-temporal mapping
4. Predictive analytic tools and techniques for targeted interventions
a. Integrated Predictive Modelling system for soil and crop management
b. Design of Intervention strategies and field implementation
c. Agro-Decision Support Systems: prototype |