Assessment of Hailstorm Damage in Wheat Crop Using Remote Sensing

Singh, Surendra; Saxena, R. M.; Porwal, Akhilesh; Neetu, .; Ray, S. S.

doi:10.18520/cs/v112/i10/2095-2100

Cited by 20 publications

(11 citation statements)

References 14 publications

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“…If some kind of anomaly goes undetected in the initial stage may harm the crop yield in an unprecedented way. Singh et al [47] assessed hailstorms on India's wheat production and observes that in February and March 2015 alone the hailstorm events caused a decline of 8.4% in national wheat production. For financially weak farmers in a country such as India, where intermittent storage of harvested crops is a rare resource, accurate weather predictions may turn to be miraculous for farmers.…”

Section: B Crop Yield Predictionmentioning

confidence: 99%

Machine Learning Applications for Precision Agriculture: A Comprehensive Review

et al. 2021

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Agriculture plays a vital role in the economic growth of any country. With the increase of population, frequent changes in climatic conditions and limited resources, it becomes a challenging task to fulfil the food requirement of the present population. Precision agriculture also known as smart farming have emerged as an innovative tool to address current challenges in agricultural sustainability. The mechanism that drives this cutting edge technology is machine learning (ML). It gives the machine ability to learn without being explicitly programmed. ML together with IoT (Internet of Things) enabled farm machinery are key components of the next agriculture revolution. In this article, authors present a systematic review of ML applications in the field of agriculture. The areas that are focused are prediction of soil parameters such as organic carbon and moisture content, crop yield prediction, disease and weed detection in crops and species detection. ML with computer vision are reviewed for the classification of a different set of crop images in order to monitor the crop quality and yield assessment. This approach can be integrated for enhanced livestock production by predicting fertility patterns, diagnosing eating disorders, cattle behaviour based on ML models using data collected by collar sensors, etc. Intelligent irrigation which includes drip irrigation and intelligent harvesting techniques are also reviewed that reduces human labour to a great extent. This article demonstrates how knowledge-based agriculture can improve the sustainable productivity and quality of the product.

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Section: B Crop Yield Predictionmentioning

confidence: 99%

Machine Learning Applications for Precision Agriculture: A Comprehensive Review

et al. 2021

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“…As for the classi cation of hail damage, the damage degree of cotton can be divided into four classes: 1) slight damage type, less than 20% of the apical bud breakage rate and less than 30% leaves damage rate; 2) serious damage type, about 40% of the apical bud breakage rate, more than 30% the branches breaking rate with fallen and breaking leaves and buds; 3) very serious damage type, the polished stem following all of apical buds, leaves, branches and buds were destroyed but the axillary buds reserve the growth ability; and 4) destroyed damage type, the hail storm destroyed the epidermis of the stem after the whole plant damaged (Institute of Cotton Research of CAAS 2013; Singh et al 2017). After the hail disaster, cotton is susceptible to various pathogens, the aphids and Spodoptera litura, which are more serious than normal condition.…”

Section: Introductionmentioning

confidence: 99%

Recovery measures and field management strategies after hailstone damage in upland cotton

Nie

et al. 2021

Preprint

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Background Hailstorm might damage cotton plants severely and cause heavy economic loss in field production. It hailed vehemently three times in 2015 and 2016 in Yellow River Basin Cotton Region and damaged cotton seedling and buds. The apical buds, leaves and stems were damaged as well as boll branches and few flower buds in our experiments. Serials strategies were performed to recover and rescue the cotton plants. Based on evaluating the hailstone damage, we fertilized 112.50 kg/ha Urea fertilizer (N content ≥ 46.4%) and intertilled the field timely. Results The recovery of plant individuals sprouted new buds and many new leaves after 12 DAH. Then the unnecessary shoots were pruned to adjust the development of fruit branches. Normally three new boll branches (NBB) in the major stem were reserved and eight subsequent emerging boll branches (EBB) in cotton plants. Five accessions with varied recovery ability and with different yield potential were sampled to compare the yield after hail damage, Sumian 20, 11–0710 and 11–0516 increased, while the other two accessions decreased relative to the normal production without hail damage. BC and RIL populations of upland cotton were used to evaluate the damage ratio of yield, which resulted in yield loss ranged 13.45%-20.27%. Fiber length, fibre elongation, fibre uniformity, and fiber elongation decreased slightly in the five accessions and in two populations. Conclusions The present study indicated that different accessions showed varied recovery ability for yield production, but all of them with a decreased ratio less than 20%. In addition, there was no significant effect on fiber quality in different cotton varieties. These results proved that the cotton plants can compensate by proper field managements, and remedial output could be obtained after hail damage.

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“…In India, limited studies have been conducted to assess the hailstorm damage of crops such as wheat (Ray et al 2016;Singh et al 2017) and other mixed crops (Prabhakar et al 2019) using multispectral satellite data. Singh et al (2017) has proposed a Percentage Difference Index (PDI), which accounts for the changes in NDVI between normal and current (hail storm affected) years. Such approach is limited over areas with stable cropping pattern where year to year variations of crop calendar is minimal.…”

Section: Introductionmentioning

confidence: 99%

“…These researchers have reported a significant decrease of post-event NDVI (in comparison to pre-event NDVI) over the hailstorm damaged vegetation which was completely destroyed, but none of them have commented on the partially damaged crops. In India, limited studies have been conducted to assess the hailstorm damage of crops such as wheat (Ray et al 2016;Singh et al 2017) and other mixed crops (Prabhakar et al 2019) using multispectral satellite data. Singh et al (2017) has proposed a Percentage Difference Index (PDI), which accounts for the changes in NDVI between normal and current (hail storm affected) years.…”

Section: Introductionmentioning

confidence: 99%

Satellite-Based Assessment of Hailstorm Affected Potato Crop for Insurance Purpose

Choudhary¹,

Chakraborty²,

Murthy³

et al. 2021

Preprint

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Assessing the extent of hailstorm affected crop is one of the thrust areas for quantifying mid-season adversaries under crop insurance values chain. This study evaluated the pre- and post-hailstorm responses on spectral bands and vegetation indices derived from Sentinel-2 data for assessing the severity class of the affected potato crop. The potato crop was mapped using pre-event satellite data with overall accuracy of 88% (k=0.82). Pair-wise Games-Howell t-test showed significant differences among the post-hailstorm potato severity classes in Red, Near Infrared & Short-wave Infra-red (SWIR) bands and Normalized vegetation indices. Percentage change (from pre- to post-event) in band reflectance and vegetation indices showed a better sensitivity in differentiating damage severity. Differential behaviour of SWIR-1 (Band-11) and SWIR-2 (Band-12) were observed within severely affected potato crop under dry and wet soil conditions. Decision matrix based on percentage change in Normalized difference Vegetation Index (DNDVI) and Normalized difference Tillage Index (DNDVI) could able to capture the damage severity classes with an overall accuracy of 86.7%. Higher proportion of affected area were found to be associated with larger percentage of Potato yield reduction based on measured yield data at Insurance unit level. The proposed methodology could be adopted for operational assessment of the impact of hailstorm events on crops.

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Assessment of Hailstorm Damage in Wheat Crop Using Remote Sensing

Cited by 20 publications

References 14 publications

Machine Learning Applications for Precision Agriculture: A Comprehensive Review

Machine Learning Applications for Precision Agriculture: A Comprehensive Review

Recovery measures and field management strategies after hailstone damage in upland cotton

Satellite-Based Assessment of Hailstorm Affected Potato Crop for Insurance Purpose

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