Weed management through herbicide application in direct-seeded rice and yield modeling by artificial neural network

In direct seeded rice (DSR) cultivation, weed is the major constraint mainly due to absence of puddling in field. The yield loss due to weed interference is huge, may be up to 100%. In this perspective, the present experiment was conducted to study the efficacy of selected herbicides, and to predict the rice yield using artificial neural network (ANN) models. The dry weight and density of weeds were recorded at different growth stages and consequently herbicidal efficacy was evaluated. Experimental results revealed that pre-emergence (PRE) herbicide effectively controlled the germination of grassy weeds. Application bispyribac-sodium as post-emergence (POST) following PRE herbicides (clomazone or pendimethalin) or as tank-mixture with clomazone effectively reduced the density and biomass accumulation of diverse weed flora in DSR. Herbicidal treatments improved the plant height, yield attributes and grain yield (2.7 to 5.5 times) over weedy check. The sensitivity of the best ANN model clearly depicts that the weed control index (WCI) of herbicides was most important than their weed control efficiency (WCE). Besides, the early control of weeds is a better prescription to improve rice yield. Differences in sensitivity values of WCI and WCE across the crop growth stages also suggest that at 15, 30 and 60 days after sowing, herbicides most effectively controlled sedges, broad leaves and grasses, respectively. Based on the grain yield and herbicidal WCE, it can be concluded that the combined application of pendimethalin or clomazone as PRE followed by bispyribac-sodium as POST or tank-mixture of clomazone + bispyribac sodium can effectively control different weed flushes throughout the crop growth period in DSR.

Download Full-text

Prediction and Guidance of Fertilizer Requirement in Different Growth Stages of Crops Based on Artificial Neural Network

Lecture Notes in Electrical Engineering - Innovative Computing ◽

10.1007/978-981-16-4258-6_210 ◽

2022 ◽

pp. 1651-1655

Author(s):

Qiuduo Zhao ◽

Jinxia Fan ◽

Shan Ning ◽

Wenzhe Li

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Growth Stages ◽

Fertilizer Requirement ◽

Artificial Neural ◽

Different Growth Stages

Download Full-text

Regression and Artificial Neural Network Modeling for the Prediction of Gray Leaf Spot of Maize

Phytopathology ◽

10.1094/phyto-95-0388 ◽

2005 ◽

Vol 95 (4) ◽

pp. 388-396 ◽

Cited By ~ 28

Author(s):

P. A. Paul ◽

G. P. Munkvold

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Leaf Spot ◽

Gray Leaf Spot ◽

Predictor Variables ◽

Neural Network Modeling ◽

Growth Stages ◽

Validation Data ◽

Data Set ◽

Artificial Neural

Regression and artificial neural network (ANN) modeling approaches were combined to develop models to predict the severity of gray leaf spot of maize, caused by Cercospora zeae-maydis. In all, 329 cases consisting of environmental, cultural, and location-specific variables were collected for field plots in Iowa between 1998 and 2002. Disease severity on the ear leaf at the dough to dent plant growth stage was used as the response variable. Correlation and regression analyses were performed to select potentially useful predictor variables. Predictors from the best 9 of 80 regression models were used to develop ANN models. A random sample of 60% of the cases was used to train the networks, and 20% each for testing and validation. Model performance was evaluated based on coefficient of determination (R2) and mean square error (MSE) for the validation data set. The best models had R2 ranging from 0.70 to 0.75 and MSE ranging from 174.7 to 202.8. The most useful predictor variables were hours of daily temperatures between 22 and 30°C (85.50 to 230.50 h) and hours of nightly relative humidity ≥90% (122 to 330 h) for the period between growth stages V4 and V12, mean nightly temperature (65.26 to 76.56°C) for the period between growth stages V12 and R2, longitude (90.08 to 95.14°W), maize residue on the soil surface (0 to 100%), planting date (in day of the year; 112 to 182), and gray leaf spot resistance rating (2 to 7; based on a 1-to-9 scale, where 1 = most susceptible to 9 = most resistant).

Download Full-text

Determination of the Most Effective Wavelengths for Prediction of Fuji Apple Starch and Total Soluble Solids Properties

Applied Sciences ◽

10.3390/app10228145 ◽

2020 ◽

Vol 10 (22) ◽

pp. 8145

Author(s):

Razieh Pourdarbani ◽

Sajad Sabzi ◽

Sanaz Jarolmasjed ◽

Thomas Panagopoulos

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Spectral Data ◽

Starch Content ◽

Soluble Solids ◽

Growth Stages ◽

Total Soluble Solids ◽

Artificial Neural ◽

Non Destructive ◽

Hybrid Artificial Neural Network

Proper physical properties and standard chemical properties are among the criteria that consumers use to select fruits. Recently, researchers attempted to develop non-destructive methods for measuring properties, among which the near-infrared (NIR) spectroscopy is of great use. Fuji apples were collected in three different growth stages, and then starch and soluble solids were extracted. Spectral data in the range of 800 to 900 nm were used to predict the amount of starch content and 920 to 980 nm to estimate total soluble solids (TSS). Reflectance spectra were pre-processed and the most effective wavelengths of each property were selected using hybrid artificial neural network-simulated annealing (ANN-SA). Non-destructive estimation of physicochemical properties was conducted using spectral data of the most effective wavelengths using a hybrid artificial neural network-biogeography-based optimization algorithm (ANN-BBO). The results indicated that the regression coefficient of the best state of training for predicting starch was 0.97 and of TSS was 0.96, while R2 was 0.92 for both. The most effective wavelengths were 852.58, 855.54, 849.03, 855.83, 853.47, 844.90 nm for starch and 967.86, 966.67, 964.90, 958.40, 957.22, 963.97 nm for TSS.

Download Full-text