Predicting and interpreting cotton yield and its determinants under long-term conservation management practices using machine learning

Dhaliwal, Jashanjeet Kaur; Panday, Dinesh; Saha, Debasish; Lee, Jae‐Hoon; Jagadamma, Sindhu; Schaeffer, Sean M.; Mengistu, Alemu

doi:10.1016/j.compag.2022.107107

Cited by 17 publications

(11 citation statements)

References 80 publications

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“…To create more reliable models, further work should be directed towards combining data from various geographic terrains. Dhaliwal et al 32 highlighted the need for multi-site data to create robust ML model for better cotton lint yield prediction. Also, attempts should be made on using different climatic elements and data in developing machine learning models 33 .…”

Section: Resultsmentioning

confidence: 99%

In-Season Growth Forecasting in Cotton Using Unmanned Aerial System- based Canopy Attributes and LSTM Models

Dhal

Bhandari

Gadepally

et al. 2023

Preprint

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Cotton (Gossypium spp.) is one of the important cash crops in the United States. Monitoring in-season growth metrics, from early season growth to harvest, is crucial for predictive and prescriptive cotton farming. In recent years, forecasting models have garnered considerable attention to predict canopy indices. This allows selection of management options during crop growth to boost cotton yield and profitability. Here, we used unmanned aerial system-derived canopy features, including canopy cover, canopy height, and excess green index, collected from 3500 plots at Driscoll in Corpus Christi, Texas during the years 2019, 2020, and 2021 for in-season growth forecasting. Training datasets in our model were produced by K-Means clustering and Dynamic Time Warping (DTW) techniques were used to compare various Long Short-Term Memory (LSTM) models in predicting the three canopy features. Accuracy was determined using Root Mean Square Error (RMSE). Results indicated higher predictive capacity of Convolutional Neural Networks (CNN) LSTM for canopy cover, and multi-layer stacked LSTMs for canopy height and excess green index respectively. Overall, results show tremendous potential for in-season growth forecasting and management of agricultural inputs like pesticides and fertilizers for improving crop health and productivity.

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Section: Resultsmentioning

confidence: 99%

In-Season Growth Forecasting in Cotton Using Unmanned Aerial System- based Canopy Attributes and LSTM Models

Dhal

Bhandari

Gadepally

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The major goal of this study is to explore the use of ML models for in-season forecasting of crop canopy features. For this, the CC was chosen as a canopy feature as it is significantly important in measuring the canopy leaf area and subsequently for yield estimations [37][38][39] and irrigation scheduling [40]. Our approach is to forecast CC features two weeks in advance with accurate precision so that in-season management decisions can be made.…”

Section: Discussionmentioning

confidence: 99%

Testing the Performance of LSTM and ARIMA Models for In-Season Forecasting of Canopy Cover (CC) in Cotton Crops

Dhal,

Kalafatis,

Braga-Neto

et al. 2024

Remote Sensing

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Cotton (Gossypium spp.), a crucial cash crop in the United States, requires the constant monitoring of growth parameters for informed decision-making. Recently, forecasting models have gained prominence for predicting canopy indicators, aiding in-season planning and management decisions to optimize cotton production. This study employed unmanned aerial system (UAS) technology to collect canopy cover (CC) data from a 40-hectare cotton field in Driscoll, Texas, in 2020 and 2021. Long short-term memory (LSTM) models, trained using 2020 data, were subsequently applied to forecast the CC values for 2021. These models were compared with real-time auto-regressive integrated moving average (ARIMA) models to assess their effectiveness in predicting the CC values up to 14 days in advance, starting from the 28th day after crop emergence. The results showed that multiple-input multi-step output LSTM models achieved higher accuracy in predicting the in-season CC values during the early growth stages (up to the 56th day), with an average testing RMSE of 3.86, significantly lower than other single-input LSTM models. Conversely, when sufficient testing data are available, single-input stacked-LSTM models demonstrated precision in CC predictions for later stages, achieving an average RMSE of 3.06. These findings highlight the potential of LSTM models for in-season CC forecasting, facilitating effective management strategies in cotton production.

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“…Random forests are CI methods that use multiple trees to obtain outputs [199,200]. There has been a steep rise in the use of this approach for various applications in agriculture [201][202][203][204][205][206][207][208][209][210][211]. An excellent survey of decision trees, random forests, and other CI models has been published in [212].…”

Section: Regression Trees and Random Forestsmentioning

confidence: 99%

Application of Computational Intelligence Methods in Agricultural Soil–Machine Interaction: A Review

et al. 2023

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Rapid advancements in technology, particularly in soil tools and agricultural machinery, have led to the proliferation of mechanized agriculture. The interaction between such tools/machines and soil is a complex, dynamic process. The modeling of this interactive process is essential for reducing energy requirements, excessive soil pulverization, and soil compaction, thereby leading to sustainable crop production. Traditional methods that rely on simplistic physics-based models are not often the best approach. Computational intelligence-based approaches are an attractive alternative to traditional methods. These methods are highly versatile, can handle various forms of data, and are adaptive in nature. Recent years have witnessed a surge in adapting such methods in all domains of engineering, including agriculture. These applications leverage not only classical computational intelligence methods, but also emergent ones, such as deep learning. Although classical methods have routinely been applied to the soil–machine interaction studies, the field is yet to harness the more recent developments in computational intelligence. The purpose of this review article is twofold. Firstly, it provides an in-depth description of classical computational intelligence methods, including their underlying theoretical basis, along with a survey of their use in soil–machine interaction research. Hence, it serves as a concise and systematic reference for practicing engineers as well as researchers in this field. Next, this article provides an outline of various emergent methods in computational intelligence, with the aim of introducing state-of-the-art methods to the interested reader and motivating their application in soil–machine interaction research.

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Predicting and interpreting cotton yield and its determinants under long-term conservation management practices using machine learning

Cited by 17 publications

References 80 publications

In-Season Growth Forecasting in Cotton Using Unmanned Aerial System- based Canopy Attributes and LSTM Models

In-Season Growth Forecasting in Cotton Using Unmanned Aerial System- based Canopy Attributes and LSTM Models

Testing the Performance of LSTM and ARIMA Models for In-Season Forecasting of Canopy Cover (CC) in Cotton Crops

Application of Computational Intelligence Methods in Agricultural Soil–Machine Interaction: A Review

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