scholarly journals Analysis of Weed Growth in Rabi Crop Agriculture Using Deep Convolutional Neural Networks

2021 ◽  
Vol 2070 (1) ◽  
pp. 012101
Author(s):  
Anand Muni Mishra ◽  
Prabhjot kaur ◽  
Yogesh Shahare ◽  
Vinay Gautam
Keyword(s):  
Image Classification ◽  
Crop Production ◽  
Fall Detection ◽  
Madhya Pradesh ◽  
Crop Damage ◽  
North India ◽  
Weed Species ◽  
Deep Convolutional Neural Networks ◽  
Crop Agriculture ◽  
Rabi Crops

Abstract Weed interference for the duration of crop establishment is a severe difficulty for wheat in North India [22.9734 ° N, 78.6569 ° E]. In situ far-flung detection for precision herbicide application minimizes the danger of both crop damage and herbicide input. This research paper focuses on the comparative study of crop growth and its effect at three different places in Madhya Pradesh [24.5840° N, 81.5020° E] India[20.5937° N, 78.9629° E]. These weed species included Pigweed (Amaranthaceae ), Goosefoot [Chenopodiaceae], Wild oat species (Poaceae), livid amaranth (Amaranthus blitum L.), Fathen[Chenopodiaceae (L.) Wild.], and Bermuda grass (Poaceae L.) a significant weed for rabi crop production in India with sensitivity to clopyralid, is the best available put up broadleaf herbicide. The intention of the Takes a look to assess the accuracy of four different CNNs architectures to locate the weed images of the Rabi crop of the family of various Rabi crops growing in competition with Rabi crops at 3 sites in Madhya Pradesh. Four CNNs have been compared, including object detection-primarily based ResNet-50, image classification-based VGGNet-16, Inception v4 and EfficientNet-B7 the EfficientNet-B7 networks have been trained to hit upon both leaves or canopies Everlasting of weeds. Image classification the use of ResNet-50 and VGGNet-16 was largely unsuccessful all through validation with whole pics (Fl-score < 0.04). CNN training elevated the usage of cropped photographs Eternal Broad Fall detection at some stage invalidation for VGGNet (F1-score = 0.77) and ResNet-50 (F1-Score = 0.62). The rabi crop weed leaf-trained inception V4 and EfficientNet-B7 achieved the highest F1-Score (0.94) and F1 Score (0.96) respectively, The aim of leaf-based EfficientNet-B7 extended false positives, even though such errors could be won over with extra training images for network desensitization training. Photograph-based faraway sensing rabi crop will become the most viable CNN test for weeds in competition with the EfficientNet-B7 crop.

scholarly journals ​Challenges of Herbicide Resistant Weeds in Wheat in India and its Management: A Review

2021 ◽  
Author(s):  
Kartik Sharma ◽  
Sachin Dhanda ◽  
Munish Leharwan ◽  
Kuldeep Singh
Keyword(s):  
Crop Production ◽  
Management Strategies ◽  
Winter Season ◽  
Cropping System ◽  
North India ◽  
Current Status ◽  
Yield Reduction ◽  
Weed Species ◽  
Cropland Area ◽  
Herbicide Resistant

Agriculture is an important part of the India’s economy. India ranks first in net cropland area in the world with 179.8 mha which is 9.6% of global net cropland area and India’s agriculture sector makes up 16% of the country’s economy, while accounting for 49% of employment (FAOSTAT, 2020). The rice-wheat cropping system (RWCS) is extensive in the subtropical areas of the Indo-Gangetic Plains of India while maize-wheat is widespread in tropical, sub-tropical and warm temperate regions. In north India, rice is grown in the summer season (June/July to September/October) whereas wheat is grown in the winter season (October/November to February/March). The area under wheat in India was 30.59 mha with an annual production of 99.78 mt and average productivity of 3.22 t ha-1 (Anonymous, 2019). The weeds are accounting as a major factor in yield reduction of wheat. The mechanical weed control is not so much effective in controlling weeds in wheat because of narrow inter row spacing. Further, the manual weeding is not much feasible because of mimicry weeds like Phalaris minor which are very much similar to wheat during initial stages. Therefore, the role of herbicides cannot be neglected. But the continuous application of herbicides with same mode of action year by year has resulted in evolution of herbicide resistance in weed species. The management of herbicide resistant weeds in crop production is a major challenge. This review mainly focuses on the current status of herbicide resistant weeds in India associated with wheat along with their management strategies.

Influence of NPK fertilization on weed flora in maize field

2014 ◽  
Vol 63 (1) ◽  
pp. 139-148 ◽  
Author(s):  
Éva Lehoczky ◽  
M. Kamuti ◽  
N. Mazsu ◽  
J. Tamás ◽  
D. Sáringer-Kenyeres ◽  
...  
Keyword(s):  
Plant Nutrition ◽  
Crop Production ◽  
N Availability ◽  
Weed Species ◽  
Negative Effects ◽  
Production Factors ◽  
Maize Field ◽  
Weed Flora ◽  
Npk Fertilization

Plant nutrition is one of the most important intensification factors of crop production. The utilization of nutrients, however, may be modified by a number of production factors, including weed presence. Thus, the knowledge of occurring weed species, their abundance, nutrient and water uptake is extremely important to establish an appropriate basis for the evaluation of their risks or negative effects on crops. That is why investigations were carried out in a long-term fertilization experiment on the influence of different nutrient supplies (Ø, PK, NK, NPK) on weed flora in maize field.The weed surveys recorded similar diversity on the experimental area: the species of A. artemisiifolia, S. halepense and D. stramonium were dominant, but C. album and C. hybridum were also common. These species and H. annuus were the most abundant weeds.Based on the totalized and average data of all treatments, density followed the same tendency in the experimental years. It was the highest in the PK treated and untreated plots, and significantly exceeded the values of NK fertilized areas. Presumably the better N availability promoted the development of nitrophilic weeds, while the mortality of other small species increased.Winter wheat and maize forecrops had no visible influence on the diversity and the intensity of weediness. On the contrary, there were consistent differences in the density of certain weed species in accordance to the applied nutrients. A. artemisiifolia was present in the largest number in the untreated control and PK fertilized plots. The density of S. halepense and H. annuus was also significantly higher in the control areas. The number of their individuals was smaller in those plots where N containing fertilizers were used. Contrary to them, the density of D. stramonium, C. album and C. hybridum was the highest in the NPK treatments.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

scholarly journals Economics of autonomous equipment for arable farms

2021 ◽  
Author(s):  
James Lowenberg-DeBoer ◽  
Kit Franklin ◽  
Karl Behrendt ◽  
Richard Godwin
Keyword(s):  
Precision Agriculture ◽  
Environmental Performance ◽  
Crop Production ◽  
Practical Experience ◽  
Production Costs ◽  
Farm Size ◽  
Medium Size ◽  
Economic Implications ◽  
The United Kingdom ◽  
Crop Agriculture

AbstractBy collecting more data at a higher resolution and by creating the capacity to implement detailed crop management, autonomous crop equipment has the potential to revolutionise precision agriculture (PA), but unless farmers find autonomous equipment profitable it is unlikely to be widely adopted. The objective of this study was to identify the potential economic implications of autonomous crop equipment for arable agriculture using a grain-oilseed farm in the United Kingdom as an example. The study is possible because the Hands Free Hectare (HFH) demonstration project at Harper Adams University has produced grain with autonomous equipment since 2017. That practical experience showed the technical feasibility of autonomous grain production and provides parameters for farm-level linear programming (LP) to estimate farm management opportunities when autonomous equipment is available. The study shows that arable crop production with autonomous equipment is technically and economically feasible, allowing medium size farms to approach minimum per unit production cost levels. The ability to achieve minimum production costs at relatively modest farm size means that the pressure to “get big or get out” will diminish. Costs of production that are internationally competitive will mean reduced need for government subsidies and greater independence for farmers. The ability of autonomous equipment to achieve minimum production costs even on small, irregularly shaped fields will improve environmental performance of crop agriculture by reducing pressure to remove hedges, fell infield trees and enlarge fields.

scholarly journals The effectiveness of weed regulation methods in spring wheat cultivated in integrated, conventional and organic crop production systems

2012 ◽  
Vol 52 (4) ◽  
pp. 486-493 ◽  
Author(s):  
Beata Feledyn-Szewczyk
Keyword(s):  
Spring Wheat ◽  
Crop Production ◽  
Dry Matter ◽  
Production Systems ◽  
Integrated System ◽  
Integrated Systems ◽  
Conventional System ◽  
Weed Species ◽  
Organic System ◽  
Weed Communities

Abstract The research was conducted from 2008 to 2010, and compared the influence of different weed control methods used in spring wheat on the structure of the weed communities and the crop yield. The study was carried out at the Experimental Station of the Institute of Soil Science and Plant Cultivation - State Research Institute in Osiny as part of a long-term trial where these crop production systems had been compared since 1994. In the conventional and integrated systems, spring wheat was grown in a pure stand, whereas in the organic system, the wheat was grown with undersown clover and grasses. In the conventional system, herbicides were applied two times in a growing season, but in the integrated system - only once. The effectiveness of weed management was lower in the organic system than in other systems, but the dry matter of weeds did not exceed 60 g/m2. In the integrated system, the average dry matter of weeds in spring wheat was 4 times lower, and in the conventional system 10 times lower than in the organic system. Weed diversity was the largest in spring wheat cultivated in the organic system. In the conventional and integrated systems, compensation of some weed species was observed (Viola arvensis, Fallopia convolvulus, Equisetum arvense). The comparison of weed communities using Sorenson’s indices revealed more of a similarity between systems in terms of number of weed species than in the number of individuals. Such results imply that qualitative changes are slower than quantitative ones. The yield of grain was the biggest in the integrated system (5.5 t/ha of average). It was 35% higher than in the organic system, and 20% higher than in conventional ones.

A Survey of Glyphosate-Resistant Waterhemp (Amaranthus rudis) in Missouri Soybean Fields and Prediction of Glyphosate Resistance in Future Waterhemp Populations Based on In-Field Observations and Management Practices

2013 ◽  
Vol 27 (4) ◽  
pp. 656-663 ◽  
Author(s):  
Kristin K. Rosenbaum ◽  
Kevin W. Bradley
Keyword(s):  
Crop Production ◽  
Management Practices ◽  
Production Systems ◽  
Glyphosate Resistance ◽  
Continuous Cropping ◽  
Field Observations ◽  
Weed Species ◽  
Herbicide Treatment ◽  
Amaranthus Rudis

A survey of soybean fields containing waterhemp infestations was conducted just prior to harvest in 2008 and 2009 to determine the frequency and distribution of glyphosate-resistant waterhemp in Missouri, and to determine if there are any in-field parameters that may serve as indicators of glyphosate resistance in this species in future crop production systems. Glyphosate resistance was confirmed in 99 out of 144, or 69%, of the total waterhemp populations sampled, which occurred in 41 counties of Missouri. Populations of glyphosate-resistant waterhemp were more likely to occur in fields with no other weed species present at the end of the season, continuous cropping of soybean, exclusive use of glyphosate for several consecutive seasons, and waterhemp plants showing obvious signs of surviving herbicide treatment compared to fields characterized with glyphosate-susceptible waterhemp. Therefore, it is suggested that these four site parameters, and certain combinations of these parameters, serve as predictors of glyphosate resistance in future waterhemp populations.

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