News Update on Crop Management Research: May – 2019

Opportunities and limitations for image-based remote sensing in precision crop management

This review addresses the potential of image-based remote sensing to produce spatially and temporally distributed data for preciseness crop management (PCM). PCM is associate agricultural management system designed to focus on crop and soil inputs consistent with at intervals, field necessities to optimize profitableness and defend the setting. Progress in. PCM has been hampered by an absence of timely, distributed data on crop and soil conditions. supported a review of the data necessities of PCM, eight areas were known during which image-based remote sensing technology may offer data that’s presently lacking or inadequate. Recommendations were created for applications with potential for near-term implementation with out there remote sensing technology and instrumentation. we tend to found that each aircraft- and satellite-based re-trote sensing may offer valuable data for PCM applications. pictures from aircraft-based sensors have a novel role for observation seasonally variable crop/soil conditions and for time specific and time-critical crop management; current satellitebased sensors have restricted, however necessary, applications; and forthcoming business Earth observation satellites might offer the resolution, timeliness, and top quality needed for several PCM operations. this limitations for image-based remote sensing applications ar in the main thanks to detector attributes, like restricted spectral vary, coarse spacial resolution, slow work time, and inadequate repeat coverage. consistent with consultants in PCM, the potential marketplace for remote sensing merchandise in PCM is nice. Future work ought to be targeted on assimilatory remotely detected infonna- tion into existing call support systems (DSS), and conducting economic and technical analysis of remote sensing applications with season-long pilot comes. [1]

Remote Sensing for Crop Management

Scientists with the Agricultural analysis Service (ARS) and numerous government agencies and personal establishments have provided an excellent deal of basic info relating spectral reflectivity and thermal emittance properties of soils and crops to their science and biophysical characteristics. this data has expedited the event and use of varied remote sensing ways for non-destructive observance of plant growth and development and for the detection of the many environmental stresses that limit plant productivity. plus fast advances in computing and positionlocating technologies, remote sensing from ground-, air-, and space-based platforms is currently capable of providing elaborated spatial  and temporal info on plant response to their native surroundings that’s required for web site specific agricultural management approaches. This manuscript, that emphasizes contributions by ARS researchers, reviews the biophysical basis of remote sensing; examines approaches that are developed, refined, and tested for management of water, nutrients, and pests in agricultural crops; and assesses the role of remote sensing in yield prediction. It concludes with a discussion of challenges facing remote sensing within the future. [2]

Modeling Soybean Growth for Crop Management

Asoybean (Glycine liquid ecstasy (L.) Merr.) crop growth simulation model (SOYGRO) was developed to assist farm managers in creating irrigation and pesterer management selections. Non-linear 1st order differential equations describe dry matter rates of modification, accumulation and depletion of macromolecule pools, and changes in shell and seed numbers. 2 knowledge sets from defoliation and irrigation experiments were used for activity and validation of the model. The model responds well to drought and defoliation stresses for 2 take a look at cases. Sensitivity analyses of SOYGRO disclosed that simulated yield was most sensitive to changes in gross chemical process and growth respiration. The sensitivity of simulated yield to changes in model parameters was hyperbolic by the incidence of either water or defoliation stress. [3]

Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production

Aerial imagination is frequently utilized by crop researchers, growers and farmers to observe crops throughout the season. To extract meaty info from large-scale aerial pictures collected from the sphere, high-throughput constitution analysis solutions ar needed, that not solely turn out high-quality measures of key crop traits, however conjointly support professionals to form prompt and reliable crop management selections. Here, we have a tendency to report AirSurf, an automatic and ASCII text file analytic platform that mixes fashionable pc vision, up-to-date machine learning, and standard software system engineering so as to live yield-related phenotypes from ultra-large aerial imagination. To quantify legion in-field lettuces noninheritable  by fixed-wing lightweight aircrafts equipped with normalised distinction vegetation index (NDVI) sensors, we have a tendency to tailor-made AirSurf by combining pc vision algorithms and a deep-learning classifier trained with over a hundred,000 labeled  lettuce signals. The tailored platform, AirSurf-Lettuce, is capable of grading and categorising iceberg lettuces with high accuracy (>98%). what is more, novel analysis functions are developed to map lettuce size distribution across the sphere, supported that associated world positioning system (GPS) labelled harvest regions are known to alter growers and farmers to conduct preciseness agricultural practises so as to boost the particular yield furthermore as crop marketability before the harvest. [4]

Tillage and Rice Straw Management Affect Soil Enzyme Activities and Chemical Properties after Three Years of Conservation Agriculture Based Rice-wheat System in North-Western India

Aims: to guage the results of rice institution, tillage and rice straw management on changes in soil catalyst activities and chemical properties in soil once 3 cycles of continuous rice-wheat system.

Study Design: The experiment was ordered in split plot style with 3 replications.

Place and period of Study: PAU, Ludhiana, 2010-2013.

Methodology: The experiment was started throughout kharif season of 2010. the planning of associate experiment was having twelve treatments with three replications. the most plot treatments in rice (zero until direct seeded rice, ZT-DSR; typical until direct seeded rice, CT-DSR; zero until direct transplanted rice, ZT-DTR and puddled transplanted rice, PTR) and 3 sub-plot treatments in wheat (conventional until wheat while not rice straw, CTW-R; ZT wheat while not rice straw, ZTW-R, and ZT wheat with rice straw maintained as surface mulch victimization Happy Seeder, ZTW+R).

Results: Zero tillage with rice straw retention (ZTW) as surface mulch (+R) multiplied wheat yield by 9/11 and V-J Day compared with typical tillage (CTW) and ZTW with no residue (-R). considerably higher dehydrogenase, dyestuff diacetate, basic enzyme, phytase and enzyme activities were recorded beneath ZTW+R compared with ZTW/CTW-R in 0-5 cm soil layer. Organic carbon, Olsen-P, offered K and DTPA-extractable micronutrients (Zn, Fe, Mn and Cu) within the surface 0-5 cm soil layer were considerably higher in ZTW+R compared with ZTW/CTW-R. Soil catalyst activities were considerably and completely correlative with one another, soil organic carbon, Olsen-P and grain yield of wheat.

Conclusion: we tend to finished that RCTs (ZTW and rice residue retention) improve soil catalyst activities and chemical properties in surface 0-5 cm soil layer and enhance productivity and property of rice-wheat system. [5]

Reference

[1]  Moran, M.S., Inoue, Y. and Barnes, E.M., 1997. Opportunities and limitations for image-based remote sensing in precision crop management. Remote sensing of Environment61(3), pp.319-346. (Web Link)

[2]  Pinter Jr, P.J., Hatfield, J.L., Schepers, J.S., Barnes, E.M., Moran, M.S., Daughtry, C.S. and Upchurch, D.R., 2003. Remote sensing for crop management. Photogrammetric Engineering & Remote Sensing, 69(6), pp.647-664. (Web Link)

[3]  Wilkerson, G.G., Jones, J.W., Boote, K.J., Ingram, K.T. and Mishoe, J.W., 1983. Modeling soybean growth for crop management. Transactions of the ASAE, 26(1), pp.63-0073. (Web Link)

[4]  Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production

Alan Bauer, Aaron George Bostrom, Joshua Ball, Christopher Applegate, Tao Cheng, Stephen Laycock, Sergio Moreno Rojas, Jacob Kirwan & Ji Zhou

Horticulture Researchvolume 6, Article number: 70 (2019) (Web Link)

[5]  Kharia, S., Thind, H. S., Sharma, S., Sidhu, H. S., Jat, M. L. and Singh, Y. (2017) “Tillage and Rice Straw Management Affect Soil Enzyme Activities and Chemical Properties after Three Years of Conservation Agriculture Based Rice-wheat System in North-Western India”, International Journal of Plant & Soil Science, 15(6), pp. 1-13. doi: 10.9734/IJPSS/2017/33494. (Web Link)

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