Dose-dependent effects of CeO2 nanomaterials on tomato plant chemistry and insect herbivore resistance

2021 ◽  
Author(s):  
Zhenggao Xiao ◽  
Ningke Fan ◽  
Le Yue ◽  
Feiran Chen ◽  
Haihua Ji ◽  
...  
Keyword(s):  
Crop Yield ◽  
Tomato Plant ◽  
Engineered Nanomaterials ◽  
Insect Herbivore ◽  
Plant Chemistry ◽  
Herbivore Resistance ◽  
Agricultural Applications ◽  
Dose Dependent

The use of engineered nanomaterials (NMs) for agricultural applications is becoming increasingly interesting because NMs have been shown to promote crop yield, and also to some extent, protection against insect...

A review of the use of engineered nanomaterials to suppress plant disease and enhance crop yield

2015 ◽  
Vol 17 (2) ◽  
Author(s):  
Alia Servin ◽  
Wade Elmer ◽  
Arnab Mukherjee ◽  
Roberto De la Torre-Roche ◽  
Helmi Hamdi ◽  
...  
Keyword(s):  
Crop Yield ◽  
Plant Disease ◽  
Engineered Nanomaterials

Correlated Induction of Phytohormones and Glucosinolates Shapes Insect Herbivore Resistance of Cardamine Species Along Elevational Gradients

2019 ◽  
Vol 45 (7) ◽  
pp. 638-648
Author(s):  
Christelle A. M. Robert ◽  
Loïc Pellissier ◽  
Xoaquín Moreira ◽  
Emmanuel Defossez ◽  
Marc Pfander ◽  
...  
Keyword(s):  
Insect Herbivore ◽  
Herbivore Resistance ◽  
Elevational Gradients

A versatile approach based on convolutional neural networks for early identification of diseases in tomato plants

2021 ◽  
Author(s):  
N. V. Megha Chandra ◽  
K. Ashish Reddy ◽  
G. Sushanth ◽  
S. Sujatha
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Convolutional Neural Networks ◽  
Crop Yield ◽  
Tomato Plant ◽  
Early Identification ◽  
Area Under The Curve ◽  
Tomato Plants ◽  
Novel Method ◽  
Optimum Model

Agriculture is one of the primary occupations in many countries. Tomatoes are grown by many farmers in countries where the water resource is available in abundance. Improper methods of cultivation and failure to identify the diseases when it is in the nascent stage results in the reduction of crop yield thus affecting the outcome of cultivation. This paper proposes a novel method of early identification of diseases in tomato plants by making use of convolutional neural networks (CNN) and image processing. Dataset from an open repository was considered for training and testing and the algorithm was capable of identifying nine different varieties of diseases that affect the tomato plant at its early stages. The images of tomato leaves were fed for identification through processing and classification. An optimum model was developed by analyzing various architectures of CNN including the VGG, ResNet, Inception, Xception, MobileNet and DenseNet. The performance of each of these architectures was compared and various metrics like the accuracy, loss, precision, recall and area under the curve (AUC) were analyzed.

scholarly journals The relationship between plant quality and yields of glasshouse tomatoes.

1975 ◽  
Vol 23 (3) ◽  
pp. 248-258
Author(s):  
L.S. Spithost
Keyword(s):  
Multiple Regression ◽  
Crop Yield ◽  
Tomato Plant ◽  
Plant Size ◽  
Nitrate Content ◽  
Regression Equations ◽  
Planting Material ◽  
Subsequent Crop ◽  
The Relationship

The results of experiments on tomato plant raising methods for early cropping under glass showed that the quality of the planting material as assessed by the subsequent crop yield was largely determined by size. A further study of growing methods on 55 nurseries showed that in all cases the plants used for December planting for the early 1969/1970 crop were too small. Multiple regression equations showed that for crops harvested before 18 April, 1970, plant size influenced earliness more than yield. In addition, the higher the plant nitrate content the later was the date of the first pick and the higher the Na content the lower the yield. (Abstract retrieved from CAB Abstracts by CABI’s permission)

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