Smart irrigation and Crop health prediction

2021 ◽  
Author(s):  
E Siddhartha ◽  
Manjunath C Lakkannavar
Keyword(s):  
Crop Health

scholarly journals Climate Change Patterns of Wild Blueberry Fields in Downeast, Maine over the Past 40 Years

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 594
Author(s):  
Rafa Tasnim ◽  
Francis Drummond ◽  
Yong-Jiang Zhang
Keyword(s):  
Climate Change ◽  
Vegetation Index ◽  
Potential Evapotranspiration ◽  
Growing Season ◽  
Spatial Average ◽  
Climate Variables ◽  
Threshold Values ◽  
The Past ◽  
Wild Blueberry ◽  
Crop Health

Maine, USA is the largest producer of wild blueberries (Vaccinium angustifolium Aiton), an important native North American fruit crop. Blueberry fields are mainly distributed in coastal glacial outwash plains which might not experience the same climate change patterns as the whole region. It is important to analyze the climate change patterns of wild blueberry fields and determine how they affect crop health so fields can be managed more efficiently under climate change. Trends in the maximum (Tmax), minimum (Tmin) and average (Tavg) temperatures, total precipitation (Ptotal), and potential evapotranspiration (PET) were evaluated for 26 wild blueberry fields in Downeast Maine during the growing season (May–September) over the past 40 years. The effects of these climate variables on the Maximum Enhanced Vegetation Index (EVImax) were evaluated using Remote Sensing products and Geographic Information System (GIS) tools. We found differences in the increase in growing season Tmax, Tmin, Tavg, and Ptotal between those fields and the overall spatial average for the region (state of Maine), as well as among the blueberry fields. The maximum, minimum, and average temperatures of the studied 26 wild blueberry fields in Downeast, Maine showed higher rates of increase than those of the entire region during the last 40 years. Fields closer to the coast showed higher rates of warming compared with the fields more distant from the coast. Consequently, PET has been also increasing in wild blueberry fields, with those at higher elevations showing lower increasing rates. Optimum climatic conditions (threshold values) during the growing season were explored based on observed significant quadratic relationships between the climate variables (Tmax and Ptotal), PET, and EVImax for those fields. An optimum Tmax and PET for EVImax at 22.4 °C and 145 mm/month suggest potential negative effects of further warming and increasing PET on crop health and productivity. These climate change patterns and associated physiological relationships, as well as threshold values, could provide important information for the planning and development of optimal management techniques for wild blueberry fields experiencing climate change.

Beyond Cloud: A Fused Optic and SAR Based Solution to Monitor Crop Health

2021 ◽  
Author(s):  
Brianna Pagán ◽  
Adekunle Ajayi ◽  
Mamadou Krouma ◽  
Jyotsna Budideti ◽  
Omar Tafsi
Keyword(s):  
Machine Learning ◽  
Satellite Imagery ◽  
Cloud Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Learning Approaches ◽  
Area Index ◽  
Spatial And Temporal Scales ◽  
Field Level ◽  
Crop Health

<p>The value of satellite imagery to monitor crop health in near-real time continues to exponentially grow as more missions are launched making data available at higher spatial and temporal scales. Yet cloud cover remains an issue for utilizing vegetation indexes (VIs) solely based on optic imagery, especially in certain regions and climates. Previous research has proven the ability to reconstruct VIs like the Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) by leveraging synthetic aperture radar (SAR) datasets, which are not inhibited by cloud cover. Publicly available data from SAR missions like Sentinel-1 at relatively decent spatial resolutions present the opportunity for more affordable options for agriculture users to integrate satellite imagery in their day to day operations. Previous research has successfully reconstructed optic VIs (i.e. from Sentinel-2) with SAR data (i.e. from Sentinel-1) leveraging various machine learning approaches for a limited number of crop types. However, these efforts normally train on individual pixels rather than leveraging information at a field level. </p><p>Here we present Beyond Cloud, a product which is the first to leverage computer vision and machine learning approaches in order to provide fused optic and SAR based crop health information. Field level learning is especially well-suited for inherently noisy SAR datasets. Several use cases are presented over agriculture fields located throughout the United Kingdom, France and Belgium, where cloud cover limits optic based solutions to as little as 2-3 images per growing season. Preliminary efforts for additional features to the product including automated crop and soil type detection are also discussed. Beyond Cloud can be accessed via a simple API which makes integration of the results easy for existing dashboards and smart-ag tools. Overall, these efforts promote the accessibility of satellite imagery for real agriculture end users.</p><p> </p>

Plant Volatiles: Useful Signals to Monitor Crop Health Status in Greenhouses

2010 ◽  
pp. 229-247 ◽  
Author(s):  
R. M. C. Jansen ◽  
J. Wildt ◽  
J. W. Hofstee ◽  
H. J. Bouwmeester ◽  
E. J. van Henten
Keyword(s):  
Health Status ◽  
Plant Volatiles ◽  
Crop Health

On-Site Longitudinal Monitoring of Crop Health Using Vapor-Printed Polymer Tattoos

2021 ◽  
Vol MA2021-01 (57) ◽  
pp. 1543-1543
Author(s):  
Trisha L. Andrew ◽  
Jae Joon Kim
Keyword(s):  
Crop Health

Improving Crop Health and Productivity: Appraisal of Induced Mutations and Advanced Molecular Genetic Tools

2020 ◽  
pp. 397-422
Author(s):  
Noor-ul-Huda Ghori ◽  
Tahir Ghori ◽  
Sameen Ruqia Imadi ◽  
Alvina Gul
Keyword(s):  
Molecular Genetic ◽  
Induced Mutations ◽  
Genetic Tools ◽  
Crop Health

Smart Solution for Leaf Disease and Crop Health Detection

2021 ◽  
pp. 231-241
Author(s):  
Alisha Dwari ◽  
Adyasha Tarasia ◽  
Adyasha Jena ◽  
Sukalpa Sarkar ◽  
Sourav Kumar Jena ◽  
...  
Keyword(s):  
Leaf Disease ◽  
Crop Health

scholarly journals Acid Rain Increases Impact of Rice Blast on Crop Health via Inhibition of Resistance Enzymes

Plants ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 881
Author(s):  
Hong-Ru Li ◽  
Hui-Min Xiang ◽  
Jia-Wen Zhong ◽  
Xiao-Qiao Ren ◽  
Hui Wei ◽  
...  
Keyword(s):  
Acid Rain ◽  
Rice Blast ◽  
Crop Yields ◽  
Pyricularia Oryzae ◽  
Plant Pathogen Interaction ◽  
Rice Leaves ◽  
Decreasing Ph ◽  
Crop Health ◽  
Cultivation Experiment

Worldwide, rice blast (Pyricularia oryzae) causes more rice crop loss than other diseases. Acid rain has reduced crop yields globally for nearly a century. However, the effects of acid rain on rice-Pyricularia oryzae systems are still far from fully understood. In this study, we conducted a lab cultivation experiment of P. oryzae under a series of acidity conditions as well as a glasshouse cultivation experiment of rice that was inoculated with P. oryzae either before (P. + SAR) or after (SAR + P.) simulated acid rain (SAR) at pH 5.0, 4.0, 3.0 and 2.0. Our results showed that the growth and pathogenicity of P. oryzae was significantly inhibited with decreasing pH treatments in vitro culture. The SAR + P. treatment with a pH of 4.0 was associated with the highest inhibition of P. oryzae expansion. However, regardless of the inoculation time, higher-acidity rain treatments showed a decreased inhibition of P. oryzae via disease-resistance related enzymes and metabolites in rice leaves, thus increasing disease index. The combined effects of high acidity and fungal inoculation were more serious than that of either alone. This study provides novel insights into the effects of acid rain on the plant–pathogen interaction and may also serve as a guide for evaluating disease control and crop health in the context of acid rain.

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