Abstract
The study develops a machine learning-based framework of drought prediction and resilience for wheat cultivation that bypasses some weaknesses in traditional models reliant on outdated climate data and static statistical techniques. Currently used systems cannot adapt to changing environmental conditions and therefore lead to incorrect resource management. While the latter proposed framework, deep learning algorithms, configurations of LSTM and Random Forest, provides real-time predictions and more localized insights using comprehensive datasets. The results show an increase in accuracy (89%) over the existing systems (72 % and 78%), along with improved precision (85%) and recall (90%). Also, prediction errors for all risk categories were significantly smaller, demonstrating the validity of the framework. The user feedback reported high satisfaction with respect to usability and on-time alerts, further asserting the advantage of the model in promoting sustainability while ensuring that the farmers take informed decisions against climate variability.
Abstract from the paper’s OpenAlex record, shown for indexing and research. Copyright remains with the authors or the publisher. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 5
Claim · model-written summary
The framework reached 89% accuracy versus 72% and 78% for existing systems.
- Taxon
- Wheat
Triticum aestivumt0 - Outcome
- accuracye0
- Direction
- increasee0
- Value
- 89%e0
- Qualifier
- 72 % and 78%e0
- Comparator
- existing systemse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“an increase in accuracy (89%) over the existing systems (72 % and 78%)”
t0“Wheat”
Claim · model-written summary
Framework precision was 85%.
- Taxon
- WheatTriticum aestivumt0
- Outcome
- precisione0
- Direction
- improvede0
- Value
- 85%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“improved precision (85%)”
t0“Wheat”
Claim · model-written summary
Framework recall was 90%.
- Taxon
- WheatTriticum aestivumt0
- Outcome
- recalle0
- Value
- 90%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“recall (90%)”
t0“Wheat”
Claim · model-written summary
Prediction errors were significantly smaller for all risk categories.
- Taxon
- WheatTriticum aestivumt0
- Outcome
- prediction errorse0
- Direction
- smallere0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“prediction errors for all risk categories were significantly smaller”
t0“Wheat”
Claim · model-written summary
User feedback reported high satisfaction with usability and timely alerts.
- Taxon
- WheatTriticum aestivumt0
- Outcome
- high satisfaction with respect to usability and on-time alertse0
- Value
- high satisfactione0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The user feedback reported high satisfaction with respect to usability and on-time alerts”
t0“Wheat”