Assessing crop damage from dicamba on non‐dicamba‐tolerant soybean by hyperspectral imaging through machine learning

Zhang, Jingcheng; Huang, Yanbo; Reddy, Krishna N.; Wang, Bin

doi:10.1002/ps.5448

Cited by 41 publications

(34 citation statements)

References 40 publications

(62 reference statements)

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“…Because this study is unique in this regard, there is a lack of literature to compare with. Still, the accuracy found here is similar to or even higher than those obtained by modeling different stresses effects in plants [21,[24][25][26][27].…”

Section: Discussionsupporting

confidence: 77%

“…Recently, machine learning approaches have been used in modeling the hyperspectral response of different conditions associated with vegetation [21]. The popular techniques used for analyzing data include regression analysis, vegetation indices, linear polarizations, wavelet-based filtering, and, currently, machine learning algorithms like random forest, decision tree, support vector machine (SVM), k-nearest neighbor (kNN), artificial neural networks (ANN), naïve Bayes (NB), and others [22][23][24][25]. To evaluate the hyperspectral response of plants, machine learning has already been implemented in different scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks

et al. 2019

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Modeling the hyperspectral response of vegetables is important for estimating water stress through a noninvasive approach. This article evaluates the hyperspectral response of water-stress induced lettuce (Lactuca sativa L.) using artificial neural networks (ANN). We evenly split 36 lettuce pots into three groups: control, stress, and bacteria. Hyperspectral response was measured four times, during 14 days of stress induction, with an ASD Fieldspec HandHeld spectroradiometer (325–1075 nm). Both reflectance and absorbance measurements were calculated. Different biophysical parameters were also evaluated. The performance of the ANN approach was compared against other machine learning algorithms. Our results show that the ANN approach could separate the water-stressed lettuce from the non-stressed group with up to 80% accuracy at the beginning of the experiment. Additionally, this accuracy improved at the end of the experiment, reaching an accuracy of up to 93%. Absorbance data offered better accuracy than reflectance data to model it. This study demonstrated that it is possible to detect early stages of water stress in lettuce plants with high accuracy based on an ANN approach applied to hyperspectral data. The methodology has the potential to be applied to other species and cultivars in agricultural fields.

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Section: Discussionsupporting

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks

et al. 2019

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“…These results suggest that the proposed system could utilize any one of the algorithms for the task of identifying lesions on cotton leaves [ 51 ]. However, the performance is far from ideal, since Mohanty et al [ 52 ] obtained a figure of 99.35% for accuracy in the detection of leaf diseases; Brahimi et al [ 53 ] found a figure of 99.18% for tomato leaves, and Zhang et al [ 54 ], an overall accuracy greater than 90% in the identification of damage to soybeans tolerant to the herbicide dicamba. All of these values are substantially superior to those obtained here.…”

Section: Resultsmentioning

confidence: 99%

Identification of Cotton Leaf Lesions Using Deep Learning Techniques

Caldeira

Santiago

Teruel

2021

Sensors

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The use of deep learning models to identify lesions on cotton leaves on the basis of images of the crop in the field is proposed in this article. Cultivated in most of the world, cotton is one of the economically most important agricultural crops. Its cultivation in tropical regions has made it the target of a wide spectrum of agricultural pests and diseases, and efficient solutions are required. Moreover, the symptoms of the main pests and diseases cannot be differentiated in the initial stages, and the correct identification of a lesion can be difficult for the producer. To help resolve the problem, the present research provides a solution based on deep learning in the screening of cotton leaves which makes it possible to monitor the health of the cotton crop and make better decisions for its management. With the learning models GoogleNet and Resnet50 using convolutional neural networks, a precision of 86.6% and 89.2%, respectively, was obtained. Compared with traditional approaches for the processing of images such as support vector machines (SVM), Closest k-neighbors (KNN), artificial neural networks (ANN) and neuro-fuzzy (NFC), the convolutional neural networks proved to be up to 25% more precise, suggesting that this method can contribute to a more rapid and reliable inspection of the plants growing in the field.

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“…For instance, 2,4-dichlorophenoxyacetic acid (2,4-D) is one of the most used herbicides to control broadleaf weeds in agriculture; however, they often damage the neighboring 2,4-D sensitive cotton field resulting in the loss of millions of dollars in the USA and Australia [23]. Likewise, severe crop injury has been reported due to the off-target movement of dicamba to the neighboring fields with non-dicamba tolerant crops [24,25]. Further, increasing use of herbicides has led to the accumulation of agricultural contaminants like arsenic, cadmium, lead, and mercury in soil and water resources [26].…”

Section: Introductionmentioning

confidence: 99%

Crop Diversification for Improved Weed Management: A Review

Sharma¹,

Shrestha²,

Kunwar³

et al. 2021

Preprint

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Weeds are among the major constraints to any crop production system, reducing productivity and profitability. Herbicides are among the most effective methods to control weeds, and reliance on herbicides for weed control has increased significantly with the advent of herbicide-resistant crops. Unfortunately, over-reliance on herbicides leads to environmental-health issues and herbicide-resistant weeds, causing human-health and ecological concerns. Crop diversification can help manage weeds sustainably in major crop production systems. It acts as an organizing principle under which technological innovations and ecological insights can be combined to manage weeds sustainably. Diversified cropping can be defined as the conscious inclusion of functional biodiversity at temporal and/or spatial levels to improve the productivity and stability of ecosystem services. Crop diversification helps to reduce weed density by negatively impacting weed seed germination and weed growth. Additionally, diversified farming systems are more resilient to climate change than monoculture systems and provide better crop yield. However, there are a few challenges to adopting a diversified cropping system, which ranges from technology innovations, government policies, farm-level decisions, climate change, and market conditions. In this review, we discuss how crop diversification supports sustainable weed management, the challenges associated with it, and the future of weed management with respect to the diversification concept.

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Assessing crop damage from dicamba on non‐dicamba‐tolerant soybean by hyperspectral imaging through machine learning

Cited by 41 publications

References 40 publications

Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks

Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks

Identification of Cotton Leaf Lesions Using Deep Learning Techniques

Crop Diversification for Improved Weed Management: A Review

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