Dilip Jat

Central Institute of Agricultural Engineering

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Narendra Singh Chandel

Central Institute of Agricultural Engineering

Yogesh Anand Rajwade

Central Institute of Agricultural Engineering

A. Subeesh

Central Institute of Agricultural Engineering

Kumkum Dubey

Barkatullah University

Subir Kumar Chakraborty

Central Institute of Agricultural Engineering

K. P. Singh

Central Institute of Agricultural Engineering

Satya Prakash Kumar

Central Institute of Agricultural Engineering

R. R. Potdar

Central Institute of Agricultural Engineering

Bharat Patel

Chaudhary Charan Singh Haryana Agricultural University

Himanshu Tripathi

NIMS University

Cooperative Institutions

Central Institute of Agricultural Engineering

Indian Council of Agricultural Research

Indian Agricultural Research Institute

Punjab Agricultural University

Indian Institute of Soil Science

Indian Institute of Technology Kharagpur

Jawaharlal Nehru Krishi Vishwa Vidyalaya

Chaudhary Charan Singh Haryana Agricultural University

Motilal Nehru National Institute of Technology

Lovely Professional University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Assessment of Engineering Properties of Proso Millet (Panicum miliaceum)

Journal of Agricultural Engineering (India) (2018)

K. P. Singh Narendra Singh Chandel R. R. Potdar Dilip Jat K. N. Agrawal

Engineering properties of grain and kernel are required to design crop production, material handling and processing equipment. A study was conducted to determine the properties of grain and kernel of Panicum miliaceum at different moisture contents in range of 6.5−26.5 % (d.b.). The length, width, thickness and geometric mean diameter of Panicum miliaceum were observed in the range of 2.22−2.36 mm, 2.08−2.24 mm, 1.93−2.21 mm, 2.07−2.27 mm for grain and 1.87−1.98 mm, 1.73−1.97 mm, 1.63−1.80 mm, 1.74−1.91 mm for kernel, respectively. Sphericity ranged between 0.93−0.97 for the grain, and 0.93−0.96 for the kernel. Surface area, volume and thousand-grain weight increased from 12.73 mm2 to 15.64 mm2 , 4.25 mm2 to 5.80 mm3 , 5.25 g to 6.70 g for grain; and 8.94 mm3 to 11.14 mm2 , 2.50 mm3 to 3.50 mm3 , 3.21 g to 4.15 g for kernel, respectively. True density of grain and kernel increased with increase in moisture content. Bulk density decreased from 765.5 kg.m-3 to 697.57 kg.m-3. Similarly, in case of kernel, bulk density decreased from 845.33 kg.m-3 to 724.97 kg.m-3. Terminal velocity of grain and kernel ranged from 1.68 m.s-1 to 2.77 m.s-1 and 0.80 m.s-1 to 1.89 m.s-1, respectively and increasing trend was observed with moisture content. The coefficient of internal friction of grain and kernel increased with increase in moisture content from 0.48 to 0.68 and 0.69 to 0.90, respectively. The geometric, gravimetric, aerodynamic and frictional properties of grain and kernel of Panicum miliaceum at different moisture contents were found to be different at 5 % significance level.

Panicum miliaceum

Kernel (algebra)

Sphericity

10.52151/jae2018552.1653

Cite

Citations (4)

Good Agricultural Practices through Agricultural Engineering Interventions

PS Tiwari KP Singh Dilip Jat KP Saha

Agricultural communication

Source

Cite

Citations (0)

Mechanized urea spraying system for balers to enhance the nutritional quality of straw: a step to prevent straw burning

Current Science (2022)

Satya Prakash Kumar Dilip Jat Surya Bhasakar Rao M. Chandrasekharaiah K. P. Singh

A large portion of unused crop residues is burnt in the fields primarily to clear the left-over straw and stubbles after the combine harvest.Studies have reported several ill effects of crop-residue burning on soil organic carbon and fertility, including reduction in productivity in the long term, environmental pollution and human health.It also produces greenhouse gases causing global warming.Rice and wheat produce large amounts of residue in India.Non-availability of labour, the high cost of residue removal from the field and the increasing use of combines in harvesting the crops are the main reasons for burning crop residues in the fields.Rice straw is unsuitable animal feed due to its high silica content and wheat straw for due to its hard stem and difficulty chewing in unchopped form.Using supplements like urea and is feeding straw sprayed with urea improves its nutritive value and intake.We have developed a mechanized baler equipped with a urea spraying system for spraying urea during baling operations on crop residues (paddy or wheat straw) to enhance their nutritional value.The developed system was evaluated in combine a harvested wheat residue field and nutritional analysis was performed.The crude protein in untreated wheat straw increased from 3.68% to 10.10% after urea treatment.The metabolizable energy was also found to improve by 3% compared to untreated straw.Thus, urea-treated bales have potential use in dairy farming.

10.18520/cs/v123/i11/1381-1386

Cite

Citations (3)

Yield Prediction in Wheat (<i>Triticum aestivum</i> L.) using Spectral Reflectance Indices

Current Science (2019)

Narendra Singh Chandel P.S. Tiwari Kishan Singh Dilip Jat B. B. Gaikwad

Influence of nitrogen on vegetative growth of wheat is significant, and can be monitored and assessed using vegetation indices derived from canopy reflectance at different phenological growth stages.The aim of the present work was to establish a regression model for yield prediction of wheat using spectral reflectance indices (SRIs), normalized difference nitrogen index (NDNI), normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and soil adjusted vegetation index (SAVI) for selected phenological growth stages of wheat.The canopy spectral reflectance was recorded during three winter seasons (2014-2017) for irrigated wheat.A hyperspectral library of canopy reflectance was developed, which enables the study of spectra independent of different nitrogen management practices.It indicated that the precise level of nitrogen for irrigated wheat may be 90 kg ha -1 in vertisols under agro-climatic of central India.Coefficient of variation (CV) was determined based on significance test between eight levels of nitrogen and SRI values.On the basis of CV, NDVI and NDWI were selected among the four spectral indices for the study of correlation between grain and biomass yields and nitrogen levels for four growth stages, viz.tillering, booting, heading and milking.A regression model was developed to find the best representative stage for yield prediction among the four stages.The regression model indicated that the relations of NDVI with grain and biomass yields were stronger in the heading stage, and it resulted in 96% accurate estimation of grain and biomass yields in irrigated wheat.

Winter wheat

10.18520/cs/v116/i2/272-278

Cite

Citations (25)

Evaluation of Furrow Opener for Application of Fertilizer at Different Depths

Bhartiya Krishi Anusandhan Patrika (2015)

K. P. Singh Manish Kumar K. N. Agrawal H. L. Kushwaha Himanshu Tripathi

To enhance the fertilizer use efficiency at different depth in crop root zone a differential depth furrow opener for tractor-drawn seed-cum-fertilizer drill under raised beds sowing was evaluated at ICAR-Central Institute of Agricultural Engineering, Bhopal. The furrow opener was evaluated based on draft and power requirements under soil bin at four depths (5, 10, 15, and 20 cm), four forward speeds (1.8, 3.2, 4.3 and 5.4 km/h) and three compaction level (200, 400 and 600 kPa). The field performance evaluation of furrow opener was done in two wheat crop varieties; HI-1544 (Triticum aestivum) and HI-8663 (Triticum durum) under raised beds. The experiment consisted fertilizer (NPK mixed) placement depth 0 cm, 5 cm, 10 cm, 15 cm and 20 cm in split-plot design. Highest grain yield was observed in 15 cm depth treatment (6048 kg/ha) followed 10 cm by (5611 kg/ha.) 5cm, (5223 kg/ha.), (4739 kg/ha.) and 0cm depth application (4493 kg/ha).From crop attributes point of view, parallel results were observed for 10 cm and 15 cm. But considering the draft and power requirement, 15 cm deep fertilization required 47% and 42% higher draft and power, respectively as compared to 10 cm deep fertilization.

Tractor

Source

Cite

Citations (0)

AI‐enabled farm‐friendly automatic machine for washing, image‐based sorting, and weight grading of citrus fruits: Design optimization, performance evaluation, and ergonomic assessment

Journal of Field Robotics (2023)

Subir Kumar Chakraborty A. Subeesh R. R. Potdar Narendra Singh Chandel Dilip Jat

Abstract The modernization of postharvest operations and penetration of emerging technologies in horticultural processing have provided intelligent solutions for reducing postharvest losses. Work environmental and occupational health issues require immediate attention as the awkward posture and continuous drudgery‐prone on‐farm sorting and grading activities may lead to musculoskeletal disorders. The main objective of this study was to develop an automatic farm‐friendly machine for real‐time citrus fruit washing, image‐based sorting, and weight grading; designed optimally and equipped with an embedded system comprising a lightweight convolutional neural network (CNN) model. Also included in this study was a thorough ergonomic assessment of the developed machine in a real work environment. The parametric choice of the fruit washing and singulation system was performed by employing computational fluid dynamics modeling and response surface methodology designed optimization. It was observed that under steady‐state conditions, the water jet would arrive at a velocity of 11.36 m/s which would eventually suit a singulation conveyor with a slope of 25°. A noninvasive grading and sorting approach for citrus fruits is presented in this paper that leverages deep learning to classify the fruits into “accept” and “reject” classes. The custom lightweight CNN model “SortNet” has shown excellent classification results with an overall accuracy of 97.6%. The ergonomic evaluation shows that the average body part discomfort score in case of operating an automatic fruit grading machine was much lower (12.3 ± 2.0) than the traditional method (30.9 ± 3.3). Further, in the case of machine operation, the percentage load on the muscles ranged from 28.67 to 34.31 reflecting that subjects can work for longer duration on the machine without fatigue as compared with the traditional manual operation.

Grading (engineering)

10.1002/rob.22193

Cite

Citations (5)

Canopy spectral reflectance for crop water stress assessment in wheat (Triticum aestivum, L.)*

Irrigation and Drainage (2020)

Narendra Singh Chandel Yogesh Anand Rajwade Kamlesh Golhani Prem Shankar Tiwari Kumkum Dubey

Abstract Spectral reflectance in the near‐infrared (NIR) and short‐wave–infrared (SWIR) regions shows decreasing water absorption troughs upon crop water stress. In this study, decreasing water absorption troughs were identified from reflectance of irrigated wheat crop. The crop was grown under two different irrigation methods, sprinkler and flood irrigation. Crop reflectance was measured using an ASD FieldSpec 3 (350–2,500 nm) hand‐held spectroradiometer at 1 week before the critical growth stages. Among different growth stages, wheat crop was found to be under stress in crown root initiation, jointing, and flowering. Results based on crop yield, and raw and first derivative spectral analysis indicated that flood‐irrigated wheat had more stress compared with sprinkler‐irrigated wheat. Five water stress‐sensitive wavelengths (974; 1,195; 1,455; 1,791; and 1,935 nm) were identified from flood‐irrigated wheat. Among them, two wavelengths (974 and 1,195 nm) were found to be highly water sensitive and good indicators for water stress. Detecting crop water stress prior to the critical growth stages of wheat helps in proper irrigation scheduling.

Irrigation scheduling

Spectroradiometer

Water Stress

10.1002/ird.2546

Cite

Citations (11)

Identifying crop water stress using deep learning models

Neural Computing and Applications (2020)

Narendra Singh Chandel Subir Kumar Chakraborty Yogesh Anand Rajwade Kumkum Dubey Mukesh Tiwari

Identification

10.1007/s00521-020-05325-4

Cite