This document discusses research priorities and training programs at the Central Institute of Agricultural Engineering in Bhopal, India. It provides details on criteria for selecting training topics, durations, resource institutions, and persons. Specific training programs covered areas like precision agriculture, food technology, biofuels, packaging, and carbon sequestration. The training helped motivate scientists and improve research quality. Recent research projects involved image processing, machine vision, and quality evaluation techniques. Strengthening infrastructure and developing collaborations were identified as future priorities.

1. Pitam Chandra
Director
Central Institute of
Agricultural Engineering
Nabibagh Berasia Road Bhopal

2. Prioritization of institute level
research needs considering
 Vision documents and R&D
agenda of CIAE
 Existing core strength of the
concerned subject area
 Gaps in skills available and skills
needed
 Relevance of the training topic/
programme to short and long term
goals of the institute

3. Training imparted Thematic Area covered
Image processing Precision agriculture, Agro-processing
Fermentation
technology
Bio-fuels, Food technology
Nano technology Food technology, Bio-energy
Packaging Food technology
Carbon Sequestration Biochar technology, utilization of
biomass
resources
Non-thermal Non-
chemical processing
Food technology

4.  Criteria for selection: Future priorities of Institute in context of
its vision, educational and research background of individual
scientist
 Duration: Generally decided by NAIP
 Training basic, strategic and new areas– 3 months
 Training on engineering applications– 3 months
 Training on the implementation strategies- 1 month
 Resource institutions: International institutions with specific
capability in area of training proposed. www, personal
knowledge, peer suggestions
 Resource Person: Identified using global research contributions
of professors of different foreign universities, personal
contacts

5.  Scientists better motivated to prepare
higher quality research projects
 Improved decision making capacity of
researchers
 Higher confidence and better peer
interaction
 Greater positivity among trained
personnel

6.  Protocol for non-destructive quality
evaluation of mangoes
 Software for machine vision based
physical properties (size and shape)
of grains
 A project sponsored by NFBSFAR on
machine based variety identification
 An institute project on machine vision
based sorting of oranges

7. Image based selective herbicide
applicator under NAIP project on
Precision Farming Technology
A study on activation of crop
residue based charcoal to obtain
the activated carbon at very low
cost by nano-cavities
augmentation.
Crop status monitoring for
precision input applicators

8.  National training on Precision Farming,
March 3-12, 2014 at CIAE, Bhopal
 Summer school on Precision Farming, July
5-25, 2011
 National training on Non thermal, Non
Chemical Processing and membrane
technology in food systems at CIAE, Bhopal;
Oct12-21, 2011
 Book on Non thermal Non chemical
Processing in Food System: ISBN 978-81-
909305-2-9
 High rated Research Publications

9.  Strengthening of the laboratory and
instrumentation in the field of precision
farming system with enhanced analytical
capacity.
 Strengthening of research work in the field
of precision farming, image processing and
machine vision system
 New machine vision based equipment
developed are controller based seed cum
fertilizer drill, laser sensor based herbicide
applicator, real-time sprinkler irrigation
system, X-ray Image based Non-destructive
detection system for mango.

10.  Image processing techniques are being
utilized for 1) development of weed
identification system, 2) Identification
of crop variety.
 Up-gradation of TGA for kinetic
analysis: To understand the thermal
stability and de-volatization process of
charcoal, biomass and carbon entities.

11.  Develop close functional linkages with
pertinent national / international
institutions in the frontier areas
 Creation/ strengthening of appropriate
infrastructure
 Seek funds for strategic research
 Human resource development in the
frontier areas

12.  University of Nebraska, Lincoln,
Nebraska, USA.
 Biological Systems Engineering
Department, Virginia Tech, Blacksburg,
VA 24061, USA.
 UCDavis.
 University of Arkansas.
 University of Manitoba, Canada
 North Carolina State University, Raleigh,
NC, USA
 Cornell University, Ithaca, NY, USA
 Michigan State University, East Lansing,
Michigan, USA

13.  Dr. Jayam Subbaiah
 Dr. S. K. Upadhyaya
 Dr Kumar Mallikarjunan
 Dr. Lalit Verma

14.  Concept note entitled "Microwave
assisted extraction technique” as a tool
for extraction of antioxidants from
medicinal plants and its by-products for
functional food/ neutraceutical
submitted for functional food platform
 The CIAE component for ICAR’s Nano
technology platform
 Four concept notes on nanotechnology
and hydrothermal gasification
technology submitted to NFBSFAR

15.  Collaborative project under discussion with
M/s Marine Marshall products , Erode, Tamil
Nadu for joint project/ technical guidance
for the extraction of chitosan and related
products
 Setting up new laboratory on 'Machine
vision for agricultural applications' included
in the XII plan proposal of the institute
 Establishment of a new laboratory on Bio-
energy has been included in the XII plan
EFC of CIAE

16.  Kotwaliwale, Nachiket; Singh, Karan; Kalne, Abhimannyu; Jha, Shyam
Narayan; Seth, Neeraj&Kar, Abhijit. 2011. X-ray imaging methods for
internal quality evaluation of agricultural produce. J Food SciTechnol DOI
10.1007/s13197-011-0485-y. Published online 13 August, 2011.
 Kotwaliwale, Nachiket; Curtis, Evan; Othman, Shadi; Naganathan,
Govindarajan K; Subbiah, Jeyamkondan. 2012. Magnetic resonance imaging
and relaxometry to visualize internal freeze damage to pickling cucumber.
Postharvest Biology and Technology 68:22–31.
 Gangil S. 2014. Dominant thermogravimetric signatures of lignin in cashew
shell as compared to cashew shell cake. Bioresource Technology, Volume
155: 15-20. (Elsevier) NAAS 2014 rating : 10.75
 Gangil S. 2014. Beneficial transitions in thermogravimetric signals and
activation energy levels due to briquetting of raw pigeon pea stalk. FUEL,
Volume 128, Pages 7–13. (Elsevier) (NAAS 2014 rating 9.36)
 Gangil S. 2014. Distinct splitting of cellulose related signals in cashews hell :
A TG-diagnosis. Cellulose (Accepted on 26-05-2014) (NAAS 2014 rating:
9.48)
 Gangil S. 2014. Polymeric Consolidation in Briquetted Biofuel as Compared
to Raw biomaterial: A TG-Vision for Pigeon pea Stalks. Energy and Fuels, 28,
3248-3254. (NAAS 2014 rating 8.85 )

17.  Gangil S. 2014. Thermogravimetric Evidences for Better Thermal Stability in
char Produced under Unconfined Conditions, Accepted manuscript on 28-
Jan-2014 in Environmental Engineering Sciences (NAAS 2014 rating: 7.15)
 Agrahar-Murugkar D, Kotwaliwale N, Manoj Kumar and Gupta, C. 2013.
Effect of roasting parameters on soy-butter product quality. International
Journal of Food Science and Technology.(In press).
 Agrahar-Murugkar, D. 2011. Effect of sprouting of soybean on the chemical
composition and quality of soymilk and tofu. Journal of Food Science and
Technology. DOI: 10.1007/s13197-011-0576-9.
 Agrahar Murugkar, D and Jha K. 2011. Influence of storage and packaging
conditions on the quality of soy flour from sprouted soybean. JFST. 48(3):
325–328.
 K.N. Agrawal, Karan Singh, Ganesh Bora and Dongqing Lin, (2012). Weed
recognition using image-processing technique based on leaf parameters,”
Journal of Agricultural Science and Technology B 2 (2012) 899-908.
 Abhimannyu Kalne, Nachiket Kotwaliwale, Karan Singh and Vijay Kumar
Singh (2012). Non-Destructive Inspection of Mango Fruit (Mangifera indica
L.) Using Digital Radiography, Computed Tomography and Magnetic
Resonance Imaging, Journal of Agricultural Engineering, 49(4), 33-41.

18.  Duration of training needs to be flexible: 3-
12 months
 Scientist proceeding on training is given
pre-departure orientation about the
organization’s expectations
 The scientist under training is monitored for
any problem(s) and learning
 On return from the deputation, scientist is
given opportunity to work in the area of
training for at least three years before
he/she is permitted to move to other goal
posts

19. THANK
YOU