Simulated Dryland Cotton Yield Response to Selected Scenario Factors Associated With Soil Health

Ale, Srinivasulu; Himanshu, Sushil Kumar; Mauget, Steven A.; Hudson, Darren; Goebel, Tim S.; Liu, Bing; Baumhardt, R. Louis; Bordovsky, James P.; Bräuer, David; Lascano, Robert J.; Gitz, Dennis C.

doi:10.3389/fsufs.2020.617509

Cited by 11 publications

(6 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The northwest plains region of Texas, also known as the Texas High Plains (THP) (Figure 1), is the largest continuous cotton-producing region in the US [2,3]. Cotton production on THP is conducted under both rainfed and irrigated systems, with irrigated yields significantly higher and more stable than rainfed systems [4]. Cotton is produced on approximately 4 million acres on the THP, with roughly 1.8 million acres of irrigated production.…”

Section: Introductionmentioning

confidence: 99%

Improving Irrigation Management of Cotton with Small Unmanned Aerial Vehicle (UAV) in Texas High Plains

Risal,

Niu,

Landivar-Scott

et al. 2024

Water

Self Cite

View full text Add to dashboard Cite

The rapid decline in water availability for irrigation on the Texas High Plains (THP) is a significant problem affecting crop production and the viability of a large regional economy worth approximately USD 7 billion annually. This region is the largest continuous cotton-producing area in the United States, and the timely delivery and efficient use of irrigation water are critical to the sustainability and profitability of cotton production in this region. Current irrigation scheduling must be improved to reduce water consumption without compromising crop production. Presently, irrigation scheduling based on reference evapotranspiration (ETo) is limited due to the lack of reliable and readily available in-field weather data and updated crop coefficients. Additionally, in-field variability in crop water demand is often overlooked, leading to lower irrigation efficiency. To address these challenges, we explored the potential use of an unmanned aerial vehicle (UAV)-based crop monitoring system to support irrigation management decisions. This study was conducted in Lubbock, Texas, in 2022, where high temporal and spatial resolution images were acquired using a UAV from a cotton field experiment with four irrigation levels. Soil moisture and canopy temperature sensors were deployed to monitor crop response to irrigation and rainfall. The results indicated a significant effect of water stress on crop growth (revealed by UAV-based canopy cover (CC) measurements), yield, and fiber quality. Strong correlations between multi-temporal CC and lint yield (R2 = 0.68 to 0.88) emphasized a clear trend: rainfed treatments with lower yields exhibited reduced CC, while irrigated plots with higher CC displayed increased yields. Furthermore, irrigated plots produced more mature and uniform fibers. This study also explored various evapotranspiration calculation approaches indicating that site-specific CC measurements obtained from a UAV could significantly reduce irrigation application. A regression model linking evapotranspiration to canopy cover demonstrated promising potential for estimating water demand in crops with an R2 as high as 0.68. The findings highlight the efficacy of UAV-based canopy features in assessing drought effects and managing irrigation water in water-limited production regions like the THP.

show abstract

Section: Introductionmentioning

confidence: 99%

Improving Irrigation Management of Cotton with Small Unmanned Aerial Vehicle (UAV) in Texas High Plains

Risal,

Niu,

Landivar-Scott

et al. 2024

Water

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, cotton cultivation in THP faces challenges, particularly in balancing water availability with yield optimization. While higher cotton yields are associated with increased water availability [ 4 ], water resource is currently insufficient to provide full irrigation in that region. The Lubbock area, for instance, is facing water limitation because of the significant decline of the water table in the Ogallala Aquifer [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

In-Season Cotton Yield Prediction with Scale-Aware Convolutional Neural Network Models and Unmanned Aerial Vehicle RGB Imagery

Niu,

Peddagudreddygari,

Bhandari

et al. 2024

Sensors

View full text Add to dashboard Cite

In the pursuit of sustainable agriculture, efficient water management remains crucial, with growers relying on advanced techniques for informed decision-making. Cotton yield prediction, a critical aspect of agricultural planning, benefits from cutting-edge technologies. However, traditional methods often struggle to capture the nuanced complexities of crop health and growth. This study introduces a novel approach to cotton yield prediction, leveraging the synergy between Unmanned Aerial Vehicles (UAVs) and scale-aware convolutional neural networks (CNNs). The proposed model seeks to harness the spatiotemporal dynamics inherent in high-resolution UAV imagery to improve the accuracy of the cotton yield prediction. The CNN component adeptly extracts spatial features from UAV-derived imagery, capturing intricate details related to crop health and growth, modeling temporal dependencies, and facilitating the recognition of trends and patterns over time. Research experiments were carried out in a cotton field at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, with three replications evaluating four irrigation treatments (rainfed, full irrigation, percent deficit of full irrigation, and time delay of full irrigation) on cotton yield. The prediction revealed that the proposed CNN regression models outperformed conventional CNN models, such as AlexNet, CNN-3D, CNN-LSTM, ResNet. The proposed CNN model showed state-of-art performance at different image scales, with the R2 exceeding 0.9. At the cotton row level, the mean absolute error (MAE) and mean absolute percentage error (MAPE) were 3.08 pounds per row and 7.76%, respectively. At the cotton grid level, the MAE and MAPE were 0.05 pounds and 10%, respectively. This shows the proposed model’s adaptability to the dynamic interplay between spatial and temporal factors that affect cotton yield. The authors conclude that integrating UAV-derived imagery and CNN regression models is a potent strategy for advancing precision agriculture, providing growers with a powerful tool to optimize cultivation practices and enhance overall cotton productivity.

show abstract

“…The northwest plains region of Texas, known as the Texas High Plains (THP), is the largest continuous cotton-producing region of Texas, which contributes to about 25% and 65% of US and Texas cotton production, respectively [2]. While cotton cultivation in THP encompasses dryland and irrigated systems, higher cotton yields are achieved by effectively applying the increased water availability [3]. However, the water resources are currently insufficient to provide full irrigation in the THP.…”

Section: Introductionmentioning

confidence: 99%

Classification of cotton water stress using convolutional neural networks and UAV-based RGB imagery

Niu,

Landivar,

Duffield

2024

Advan. Mod. Agr.

View full text Add to dashboard Cite

<p>Embracing smart irrigation management techniques empowers growers to irrigate with greater efficiency, thereby promoting sustainable agricultural production. In this context, growers often rely on crop evapotranspiration (ETc) as a key factor in making informed irrigation decisions, underscoring the significance of accurately determining and spatially mapping crop water status. Technological progress, exemplified by the emergence of unmanned aerial vehicles (UAVs), has brought about a revolutionary shift in agricultural monitoring. UAV platforms can capture high-resolution images with centimeter-level spatial accuracy and offer higher temporal coverage compared to satellite imagery. Considering these advancements, this study introduces a robust method for classifying water stress in cotton using a compact UAV platform and convolutional neural networks (CNN). The experiment was conducted at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, where the cotton field was divided into 12 drip zones. The study included three replications to evaluate four irrigation treatments: “rainfed”, “full irrigation”, “percent deficit of full irrigation”, and “time delay of full irrigation”. The results demonstrated that the CNN model successfully classified the cotton water stress using the UAV-based RGB image, achieving an overall best prediction accuracy of approximately 91%. By segmenting the original cotton images into separate canopy and soil areas using morphological image processing methods, the authors also isolated and analyzed the individual contributions of these components to cotton water stress. Additionally, a random forest classifier revealed the relative importance of different image features in the classification process through feature importance analysis. These findings highlighted the state-of-the-art performance of the proposed system in cotton water stress classification and provided valuable insights into the key image features contributing to accurate classification. The authors concluded that integrating UAV-based RGB imagery and CNN models had great potential for assessing water stress in cotton.<strong></strong></p>

show abstract

Simulated Dryland Cotton Yield Response to Selected Scenario Factors Associated With Soil Health

Cited by 11 publications

References 62 publications

Improving Irrigation Management of Cotton with Small Unmanned Aerial Vehicle (UAV) in Texas High Plains

Improving Irrigation Management of Cotton with Small Unmanned Aerial Vehicle (UAV) in Texas High Plains

In-Season Cotton Yield Prediction with Scale-Aware Convolutional Neural Network Models and Unmanned Aerial Vehicle RGB Imagery

Classification of cotton water stress using convolutional neural networks and UAV-based RGB imagery

Contact Info

Product

Resources

About