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ISSN: 2589-7217
CN: 10-1795/S
p-ISSN: 2097-2113

Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides

Agriculture plays a significant role in the economic sector. The automation in agriculture is the main concern and the emerging subject across the world. The population is increasing tremendously and...

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A comprehensive review on automation in agriculture using artificial intelligence

Agriculture automation is the main concern and emerging subject for every country. The world population is increasing at a very fast rate and with increase in population the need for food increases...

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Review of agricultural IoT technology

Agricultural Internet of Things (IoT) has brought new changes to agricultural production. It not only increases agricultural output but can also effectively improve the quality of agricultural products,...

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Automation and digitization of agriculture using artificial intelligence and internet of things

The growing population and effect of climate change have put a huge responsibility on the agriculture sector to increase food-grain production and productivity. In most of the countries where the expansion...

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Applications of electronic nose (e-nose) and electronic tongue (e-tongue) in food quality-related properties determination: A review

An e-nose or an e-tongue is a group of gas sensors or chemical sensors that simulate human nose or human tongue. Both e-nose and e-tongue have shown great promise and utility in improving assessments...

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Transfer Learning for Multi-Crop Leaf Disease Image Classification using Convolutional Neural Network VGG

In recent times, the use of artificial intelligence (AI) in agriculture has become the most important. The technology adoption in agriculture if creatively approached. Controlling on the diseased leaves...

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Deep learning based computer vision approaches for smart agricultural applications

The agriculture industry is undergoing a rapid digital transformation and is growing powerful by the pillars of cutting-edge approaches like artificial intelligence and allied technologies. At the core...

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A review of imaging techniques for plant disease detection

Agriculture is the basis of every economy worldwide. Crop production is one of the major factors affecting domestic market condition in any country. Agricultural production is also a major prerequisite...

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Deep learning models for automatic identification of plant-parasitic nematode

Plant-parasitic nematodes cause various diseases that can be fatal to the infected plants. It causes losses to the agricultural industry, such as crop failure and poor crop quality. Developing an accurate...

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Deep convolutional neural network models for weed detection in polyhouse grown bell peppers

Conventional weed management approaches are inefficient and non-suitable for integration with smart agricultural machinery. Automatic identification and classification of weeds can play a vital role...

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Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network

Plants are susceptive to various diseases in their growing phases. Early detection of diseases in plants is one of the most challenging problems in agriculture. If the diseases are not identified in...

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A review on computer vision systems in monitoring of poultry: A welfare perspective

Monitoring of poultry welfare-related bio-processes and bio-responses is vital in welfare assessment and management of welfare-related factors. With the current development in information technologies,...

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Examining the interplay between artificial intelligence and the agri-food industry

Artificial intelligence (AI) has advanced at an astounding rate and transformed numerous economic sectors. Nevertheless, a comprehensive understanding of how AI can improve the agri-food industry is...

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A systematic review of machine learning techniques for cattle identification: Datasets, methods and future directions

Increased biosecurity and food safety requirements may increase demand for efficient traceability and identification systems of livestock in the supply chain. The advanced technologies of machine learning...

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Fruit ripeness classification: A survey

Fruit is a key crop in worldwide agriculture feeding millions of people. The standard supply chain of fruit products involves quality checks to guarantee freshness, taste, and, most of all, safety....

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Artificial cognition for applications in smart agriculture: A comprehensive review

Agriculture contributes to 6.4% of the entire world's economic production. In at least nine countries of the world, agriculture is the dominant sector of the economy. Agriculture not only provides the...

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Explainable artificial intelligence and interpretable machine learning for agricultural data analysis

Artificial intelligence and machine learning have been increasingly applied for prediction in agricultural science. However, many models are typically black boxes, meaning we cannot explain what the...

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Blockchain: A new safeguard for agri-foods

Blockchain implementation in agriculture has begun. Blockchain is recognized as an emerging technology in the agri-foods industry which may provide an efficient and robust mechanism for enhancing food...

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Deep learning approach for recognition and classification of yield affecting paddy crop stresses using field images

On-time recognition and early control of the stresses in the paddy crops at the booting growth stage is the key to prevent qualitative and quantitative loss of agricultural yield. The conventional paddy...

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Improving the non-destructive maturity classification model for durian fruit using near-infrared spectroscopy

The maturity state of durian fruit is a key indicator of quality before trading. This research aims to improve the near-infrared (NIR) model for classifying the maturity stage of durian fruit using...

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A computer vision system for defect discrimination and grading in tomatoes using machine learning and image processing

With large-scale production and the need for high-quality tomatoes to meet consumer and market standards criteria, have led to the need for an inline, accurate, reliable grading system during the post-harvest...

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Study on body temperature detection of pig based on infrared technology: A review

Body temperature is an important physiological indicator in the whole process of pig breeding. Temperature measurement is also an effective means to assist in disease diagnosis and pig health monitoring....

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Lightweight convolutional neural network models for semantic segmentation of in-field cotton bolls

Robotic harvesting of cotton bolls will incorporate the benefits of manual picking as well as mechanical harvesting. For robotic harvesting, in-field cotton segmentation with minimal errors is desirable...

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Real-time hyperspectral imaging for the in-field estimation of strawberry ripeness with deep learning

Strawberry is one of the popular fruits with numerous nutrients. The ripeness of this fruits was estimated using the hyperspectral imaging (HSI) system in field and laboratory conditions in this study....

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Disease detection, severity prediction, and crop loss estimation in MaizeCrop using deep learning

The increasing gap between the demand and productivity of maize crop is a point of concern for the food industry, and farmers. Its' susceptibility to diseases such as Turcicum Leaf Blight, and Rust...

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