Image Segmentation With Artificial Neural Network for Nutrient Deficiency in Cotton Crop

Sartin, Maicon Aparecido; Silva, Alexandre C. R. da; Kappes, Claudinei

doi:10.3844/jcssp.2014.1084.1093

Cited by 12 publications

(6 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the difference in P concentration in soil, spectral classification models were established to distinguish different P treatments. Sartin et al [50] studied a large number of models related to nutrient deficiency types. Based on the difference in chlorophyll coloring, the method of artificial neural network was used to segment the leaf image of P-deficiency cotton and proved the superiority of the neural network method in image segmentation.…”

Section: Svm Spectral Classification Modelsmentioning

confidence: 99%

Maize Characteristics Estimation and Classification by Spectral Data under Two Soil Phosphorus Levels

Baiyu

Liu

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

As an essential element, the effect of Phosphorus (P) on plant growth is very significant. In the early growth stage of maize, it has a high sensitivity to the deficiency of phosphorus. The main purpose of this paper is to monitor the maize status under two phosphorus levels in soil by a nondestructive testing method and identify different phosphorus treatments by spectral data. Here, the Analytical Spectral Devices (ASD) spectrometer was used to obtain canopy spectral data of 30 maize inbred lines in two P-level fields, whose reflectance differences were compared and the sensitive bands of P were discovered. Leaf Area Index (LAI) and yield under two P levels were quantitatively analyzed, and the responses of different varieties to P content in soil were observed. In addition, the correlations between 13 vegetation indexes and eight phenotypic parameters were compared under two P levels so as to find out the best vegetation index for maize characteristics estimation. A Back Propagation (BP) neural network was used to evaluate leaf area index and yield, and the corresponding prediction model was established. In order to classify different P levels of soil, the method of support vector machine (SVM) was applied. The results showed that the sensitive bands of P for maize canopy included 763 nm, 815 nm, and 900–1000 nm. P-stress had a significant effect on LAI and yield of most varieties, whose reduction rate reached 41% as a whole. In addition, it was found that the correlations between vegetation indexes and phenotypic parameters were weakened under low-P level. The regression coefficients of 0.75 and 0.5 for the prediction models of LAI and yield were found by combining the spectral data under two P levels. For the P-level identification in soil, the classification accuracy could reach above 86%. These abilities potentially allow for phenotypic parameters prediction of maize plants by spectral data and different phosphorus contents identification with unknown phosphorus fertilizer status.

show abstract

Section: Svm Spectral Classification Modelsmentioning

confidence: 99%

Maize Characteristics Estimation and Classification by Spectral Data under Two Soil Phosphorus Levels

Baiyu

Liu

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The various nutrients deficiency, nutrients toxicity and the type of diseases from the leaves of rice plant using image processing technique comprising clustering and spatial analysis in order to obtain the color variations on leaf were analyzed. Sartin et al (2014) [12] The use of precision agriculture to improve the agriculture production system in cotton plant to find macro nutrients deficiency especially Phosphorous deficiency using artificial neural network segmentation and Otsu method. Wiwart et al (2009) [13] using Euclidean distances between the colors of leaves at successive nodes the change of color in leaves (Faba bean, pea, and yellow pine plant) were analyzed.…”

Section: Review Of Literaturementioning

confidence: 99%

Multiple macro nutrients deficiency identification using fuzzy C means

Sridevy¹

2020

Int. J. Chem. Stud.

View full text Add to dashboard Cite

Every plant needs nutrients for their growth. The leaf analysis in a plant can present the need of a nutrient to be determined. The macronutrients deficiency in plants can be identified by specific type of color variations using leaves image. Early identification of macronutrients can help plants in becoming suitable for the harvest and lessen the utilization of farm inputs. The proposed system implements a novel approach to identify the lack of nutrients in maize leaf images. The maize leaf images are collected from Agricultural University, Coimbatore. The input images are preprocessed where the images in RGB color space are converted to Lab model. Then C means clustering is used to segment the nutrient deficient part from leaf image. The multi-Color space based feature Extraction is used to extract the features from deficient area of maize leaf image and type of nutrients deficiency is identified using Fuzzy rule-based system that gives the percentage of nutrient deficiency affected in maize image. Then, the performance of this work is evaluated using confusion matrix.

show abstract

“…Both traditional computer vision as well as deep learning methods have been used to detect and classify types of nutrient deficiency stress. Many of these results focus on identifying the type of deficiency from close-up images of the plant (Sartin, Da Silva, and Kappes 2014;Sethy et al 2020). Early work on aerial imagery focused on identifying signatures in hyperspectral imagery and shortwave radiation correlated with the presence of NDS (Goel et al 2003;Blackmer, Schepers, and Meyer 1995).…”

Section: Nutrient Deficiency Identificationmentioning

confidence: 99%

Detection and Prediction of Nutrient Deficiency Stress using Longitudinal Aerial Imagery

Dadsetan¹,

Rose²,

Hovakimyan³

et al. 2020

Preprint

View full text Add to dashboard Cite

Early, precise detection of nutrient deficiency stress (NDS) has key economic as well as environmental impact; precision application of chemicals in place of blanket application reduces operational costs for the growers while reducing the amount of chemicals which may enter the environment unnecessarily. Furthermore, earlier treatment reduces the amount of loss and therefore boosts crop production during a given season. With this in mind, we collect sequences of high-resolution aerial imagery and construct semantic segmentation models to detect and predict NDS across the field. Our work sits at the intersection of agriculture, remote sensing, and modern computer vision, and deep learning. First, we establish a baseline for full-field detection of NDS and quantify the impact of pretraining, backbone architecture, input representation, and sampling strategy. We then quantify the amount of information available at different points in the season by building a single-timestamp model based on a UNet. Next, we construct our proposed spatiotemporal architecture, which combines a UNet with a convolutional LSTM layer, to accurately detect regions of the field showing NDS; this approach has an impressive IOU score of 0.53. Finally, we show that this architecture can be trained to predict regions of the field which are expected to show NDS in a later flight -potentially more than three weeks in the future-maintaining an IOU score of 0.47-0.51 depending on how far in advance the prediction is made. We will also release a dataset which we believe will benefit the computer vision, remote sensing, as well as agriculture fields. This work contributes to the recent developments in deep learning for remote sensing and agriculture while addressing a key social challenge with implications for economics and sustainability.

show abstract

Image Segmentation With Artificial Neural Network for Nutrient Deficiency in Cotton Crop

Cited by 12 publications

References 12 publications

Maize Characteristics Estimation and Classification by Spectral Data under Two Soil Phosphorus Levels

Maize Characteristics Estimation and Classification by Spectral Data under Two Soil Phosphorus Levels

Multiple macro nutrients deficiency identification using fuzzy C means

Detection and Prediction of Nutrient Deficiency Stress using Longitudinal Aerial Imagery

Contact Info

Product

Resources

About