Assessment of Extension Delivery Methods to Farmers in Apa Local Government Area of  Benue State, Nigeria

Kughur, P.G.; Aveuya, Agbatse Augustine; Kuza, Y.

doi:10.9734/ajaees/2021/v39i330543

Cited by 2 publications

(2 citation statements)

References 1 publication

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“…1a, the GDP contribution of agriculture, forestry, and fishing is decreasing due to the high and fast growth rates of services in the industries sector [5,6]. In addition to the fact that India's agriculture sector is crucial to the country's economy, it also faces a few challenges like unpredictable climate, poor supply chain, and low productivity [7][8][9]. India today has almost 315 million rural population using smartphones and the Internet.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Model for Detection of Brinjal Weed in the Era of Precision Agriculture

Patel,

Ruparelia,

Tanwar

et al. 2023

CMC

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The overgrowth of weeds growing along with the primary crop in the fields reduces crop production. Conventional solutions like hand weeding are labor-intensive, costly, and time-consuming; farmers have used herbicides. The application of herbicide is effective but causes environmental and health concerns. Hence, Precision Agriculture (PA) suggests the variable spraying of herbicides so that herbicide chemicals do not affect the primary plants. Motivated by the gap above, we proposed a Deep Learning (DL) based model for detecting Eggplant (Brinjal) weed in this paper. The key objective of this study is to detect plant and non-plant (weed) parts from crop images. With the help of object detection, the precise location of weeds from images can be achieved. The dataset is collected manually from a private farm in Gandhinagar, Gujarat, India. The combined approach of classification and object detection is applied in the proposed model. The Convolutional Neural Network (CNN) model is used to classify weed and non-weed images; further DL models are applied for object detection. We have compared DL models based on accuracy, memory usage, and Intersection over Union (IoU). ResNet-18, YOLOv3, CenterNet, and Faster RCNN are used in the proposed work. CenterNet outperforms all other models in terms of accuracy, i.e., 88%. Compared to other models, YOLOv3 is the least memory-intensive, utilizing 4.78 GB to evaluate the data.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Model for Detection of Brinjal Weed in the Era of Precision Agriculture

Patel,

Ruparelia,

Tanwar

et al. 2023

CMC

View full text Add to dashboard Cite

show abstract

“…Traditionally, blanket herbicide spraying is the most common task followed by many farmers to remove weeds. However, the negative consequences of using excess herbicide chemicals are obvious as the wastage from chemicals affects the ecological system and also human health [2]. The use of available mechanical cultivators [3] can also not remove weeds precisely due to the proximity of the rows of weeds and crops.…”

Section: Introductionmentioning

confidence: 99%

A machine learning framework for the identification of crops and weeds based on shape curvature and texture properties

Agarwal

2023

Preprint

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Identification of weeds in a crop is a challenging problem for designing an automotive weed management system. Due to similar and indistinguishable properties between crop and weed, rather than single, various type of information is required for the successful discrimination of weeds against crops. This paper presents a machine learning-enabled weed classification system based on the fusion of handcrafted shape and texture features of a plant at the feature level. The shape features include several shape factors, region-based (Hu moment invariants), and contour-based descriptors (Fourier descriptors and shape curvature). Texture features include GLCM and LBP. A series of experiments are conducted to investigate the utility of shape curvature in the classification of weeds against crops. Furthermore, the potential of shape features including shape curvature and texture features has been examined by modeling the four classifiers, namely, SVM, KNN, MLP, and Naïve Bayes by using the 10-fold cross-validation approach. The classifiers are compared based on performance metrics- accuracy, error rate, recall, specificity, precision, and F1-score. Experimental results show that the SVM classifier achieved 93.67% overall accuracy with shape curvature features in the classification between crop and weed. In addition, the SVM classifier achieved 99.33% overall accuracy with the integration of shape with shape curvature and texture features.

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Assessment of Extension Delivery Methods to Farmers in Apa Local Government Area of Benue State, Nigeria

Cited by 2 publications

References 1 publication

Deep Learning-Based Model for Detection of Brinjal Weed in the Era of Precision Agriculture

Deep Learning-Based Model for Detection of Brinjal Weed in the Era of Precision Agriculture

A machine learning framework for the identification of crops and weeds based on shape curvature and texture properties

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