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Agricultural advancements have significantly impacted people’s lives and their surroundings in recent years. The insufficient knowledge of the whole agricultural production system and conventional ways of irrigation have limited agricultural yields in the past. The remote sensing innovations recently implemented in agriculture have dramatically rev...
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... By including not only biophysical variables such as microclimate effects, soil structure and quality, but also socio-economic variables, such as land use, urban-farm water accessibility, farm size, demographic data and access to markets, machine learning enables analysis at every step of the agricultural value chain 32,44,45 . Thus the usefulness of machine learning is evident not only when considering ultimate outcome variables, such as the financial well-being of a farm, but also to assess whether adaptive measures were effective in maintaining or increasing crop yield under certain climatic conditions, provide information on the relative importance of an intervention for a desired outcome and generally help with future predictions and strategies 38,40,[46][47][48] . However, the interpretability of machine learning models, especially complex algorithms like support vector machines, deep neural networks, and random forest or boosted trees, can be limited. ...
Climate change has significant implications for economically important crops, yet understanding its specific impact on farm financial wellbeing remains a challenging task. In this study we present self-reported perceptions of fruit farmers about their financial well-being when confronted with different climate change factors. We employed a combination of supervised machine learning and statistical modelling methods to analyze the data. The data collection was conducted through face-to-face interviews with 801 randomly selected cherry and peach farmers in Tunisia and Chile. Specific climate change factors, namely increases in temperature and reductions in precipitation, can have a regionally discernible effect on the self-perceived financial wellbeing of fruit farmers. This effect is less pronounced in Tunisia than in Chile. However, climate change is of lessor importance in predicting farm financial wellbeing, particularly for farms already doing well financially. Social assets, which include reliance on and trust in information sources, community and science, play an important role in increasing the probability of fruit farm financial wellbeing in both Tunisia and Chile. However, the most influential predictive factors differ between the two countries. In Chile, the location of the farm is the primary determinant of financial wellbeing, while in Tunisia it was the presence of social assets.
... The resulting datasets, just for one farm, could encompass millions of data point combinations. Importantly, analysis of such data can provide answers as to whether adaptive measures were effective in maintaining or increasing crop yield and farm incomes under certain climatic conditions, provide information on the relative importance of the intervention to the desired outcome and help with future (yield) predictions (38,40,46,47,48). ...
... Previously published data indicate that climate change can have a detrimental effect on crop yields and farm income (3,47,58,59,60,61,62). Our findings suggest that only certain kinds of climate change can have an adverse effect on fruit farm financial wellbeing. ...
Machine learning and statistical modeling methods were used to analyze the impact of climate change on financial wellbeing of fruit farmers in Tunisia and Chile. The analysis was based on face to face interviews with 801 farmers. Three research questions were investigated. First, whether climate change impacts had an effect on how well the farm was doing financially. Second, if climate change was not influential, what factors were important for predicting financial wellbeing of the farm. And third, ascertain whether observed effects on the financial wellbeing of the farm were a result of interactions between predictor variables. This is the first report directly comparing climate change with other factors potentially impacting financial wellbeing of farms. Certain climate change factors, namely increases in temperature and reductions in precipitation, can regionally impact self-perceived financial wellbeing of fruit farmers. Specifically, increases in temperature and reduction in precipitation can have a measurable negative impact on the financial wellbeing of farms in Chile. This effect is less pronounced in Tunisia. Climate impact differences were observed within Chile but not in Tunisia. However, climate change is only of minor importance for predicting farm financial wellbeing, especially for farms already doing financially well. Factors that are more important, mainly in Tunisia, included trust in information sources and prior farm ownership. Other important factors include farm size, water management systems used and diversity of fruit crops grown. Moreover, some of the important factors identified differed between farms doing and not doing well financially. Interactions between factors may improve or worsen farm financial wellbeing.
... The researchers recommended that automated models should be planned based on ensemble learning and deep learning techniques in future work. Recently, many deep learning techniques have been used successfully in different application areas, such as medical [15], agriculture [16], industry [17], and engineering [18]. Therefore, several DMLs have been utilised to detect phishing websites' URLs, such as the convolutional neural network (CNN) and long short-term memory (LSTM) algorithms, whereby the accuracy obtained was 93.28% [19]. ...
... Drop out layer around 0. 4 14 Flatten layer 1 15 Dropout layer 1 16 Dense layer neurons and relu as activation function 128 neurons 17 ...
Recently, phishing attacks have been a crucial threat to cyberspace security. Phishing is a form of fraud that attracts people and businesses to access malicious uniform resource locators (URLs) and submit their sensitive information such as passwords, credit card ids, and personal information. Enormous intelligent attacks are launched dynamically with the aim of tricking users into thinking they are accessing a reliable website or online application to acquire account information. Researchers in cyberspace are motivated to create intelligent models and offer secure services on the web as phishing grows more intelligent and malicious every day. In this paper, a novel URL phishing detection technique based on BERT feature extraction and a deep learning method is introduced. BERT was used to extract the URLs’ text from the Phishing Site Predict dataset. Then, the natural language processing (NLP) algorithm was applied to the unique data column and extracted a huge number of useful data features in terms of meaningful text information. Next, a deep convolutional neural network method was utilised to detect phishing URLs. It was used to constitute words or n-grams in order to extract higher-level features. Then, the data were classified into legitimate and phishing URLs. To evaluate the proposed method, a famous public phishing website URLs dataset was used, with a total of 549,346 entries. However, three scenarios were developed to compare the outcomes of the proposed method by using similar datasets. The feature extraction process depends on natural language processing techniques. The experiments showed that the proposed method had achieved 96.66% accuracy in the results, and then the obtained results were compared to other literature review works. The results showed that the proposed method was efficient and valid in detecting phishing websites’ URLs.
... The annual output value of 'Pingguoli' exceeds 220 million yuan. The production of 'Pingguoli' has quickly become a pillar industry in the development of Yanbian, increasing farmers' income, mobilising farmers' enthusiasm, greatly promoting the economic and cultural development of Yanbian Korean Autonomous Prefecture, and playing an important role in promoting the construction of new rural areas [5]. ...
Soil is the material base of soil fertility. It can not only fix the root system but also provide nutrients, water, and other necessary conditions for crops to promote growth of crops. As a characteristic agricultural product in the Yanbian area, the production of ‘Pingguoli’ is related to the development of the agricultural economy in the Yanbian area. To solve the agricultural problems caused by excessive fertilisation in ‘Pingguoli’ orchards in the Yanbian area and to study the correlation between rare elements and soil properties. Based on the collection of regional natural economic background and soil data in the study area, four treatments were set up: The soil and ‘Pingguoli’ samples were collected, and the total amount of rare earth elements in the soil samples and the related indexes of ‘Pingguoli’ fruit were detected. Soil is the material basis of soil fertility, and soil management determines crop growth. CF1 treatment could increase ‘Pingguoli’ yield and significantly improve fruit quality. The rate of fruit softening and bad fruit decreased significantly after storage. Reduction of fertilisation can improve quality and save cost, among which CF1 has the best effect and can obtain more benefits when applied in production. Implications: Through experiments, agricultural workers can be more deeply aware of the importance of soil to crops; reducing fertilisation can lead to better crop yield and quality while achieving greater benefits, and consumers can get healthier food.
... ey propose AgroAVNET, a hybrid model based on AlexNet and VGGNET, with a extensive performance improvement compared to existing methods. Literature [14] is dedicated to using past agricultural production data to predict future agricultural production. e authors propose a deep learning model AGR-DL based on CNN and RNN. e experimental results show that the prediction accuracy of the model is better than that of classical algorithms such as SVM, MLP, and AdaBoost. ...
With the continuous growth of the global population, insufficient food production has become an urgent problem to be solved in most countries. At present, using artificial intelligence technology to improve suitability between land and crop varieties to increase crop yields has become a consensus among agricultural researchers. However, there are still many problems in existing works, such as limited crop phenotypic data and the poor performance of artificial intelligence models. In this regard, we take maize as an example to collect a large amount of environmental climate and crop phenotypic traits data at multiple experimental sites and construct an extensive dataset. Then, we introduce a graph neural network model to learn crop suitability evaluation and finally achieve a good evaluation effect. The evaluation results of the model can not only provide a reference for expert evaluation but also judge the suitability of the variety to other test trial sites according to the data of the current one, so as to guide future breeding experiments.
... At the same time, to consider the influence of inflation on agricultural economic growth, the gross output value is calculated using the constant price in 2011 as the base period [14]. The coupling degree between the comprehensive agricultural environment index and agricultural economic growth index in each province from 2011 to 2020 is calculated [15]. The coupling and coordination status of China's agricultural environment and economy in 2020 is shown in Table 3. ...
On the basis of the panel data of 31 provinces in China from 2011 to 2020, this paper first constructs an index system through the Pressure-State-Response (PSR) model and conducts a comprehensive evaluation of China’s agricultural environment according to the entropy weight TOPSIS model. Second, a coupling coordination degree model is established to calculate the degree of coupling coordination between the agricultural economy and the environment in each province. Finally, a spatial Durbin model is established to analyze the influencing factors of China’s agricultural economy. Results show that: ① the overall environment in the eastern region has little change, and the overall level is relatively backward; the agricultural environment in the central region is uneven; the agricultural environment in the western region is quite different from north to south. ② The regions with a high level of coupling coordination are mainly concentrated in the central and southern regions, and the performance is relatively intensive. The agricultural economy and the environment in the western region are extremely uncoordinated, and as is the overall coupling coordination between the agricultural economy and the environment in the eastern region in general. Further improvement is also needed. ③ Fixed asset investment, total power of agricultural machinery, rural electricity consumption, rural population, and rural per capita disposable income all have important influences on China’s agricultural economy. ④ The rural population size has a positive and the largest effect on the agricultural economy, whereas rural per capita disposable income has a negative effect on the agricultural economy. Moreover, improving farmers’ enthusiasm for farming is one of the key issues to be solved urgently.
... Fruit plantation is one of the most important agricultural activities. The production and protection of fruit per capita has recently been considered an essential indicator of a country's growth and quality of life [1]. Population growth of 7.2 to 9.6 billion people is expected produce high-accuracy responses [31,32]. ...
The implementation of intelligent technology in agriculture is seriously investigated as a way to increase agriculture production while reducing the amount of human labor. In agriculture, recent technology has seen image annotation utilizing deep learning techniques. Due to the rapid development of image data, image annotation has gained a lot of attention. The use of deep learning in image annotation can extract features from images and has been shown to analyze enormous amounts of data successfully. Deep learning is a type of machine learning method inspired by the structure of the human brain and based on artificial neural network concepts. Through training phases that can label a massive amount of data and connect them up with their corresponding characteristics, deep learning can conclude unlabeled data in image processing. For complicated and ambiguous situations, deep learning technology provides accurate predictions. This technology strives to improve productivity, quality and economy and minimize deficiency rates in the agriculture industry. As a result, this article discusses the application of image annotation in the agriculture industry utilizing several deep learning approaches. Various types of annotations that were used to train the images are presented. Recent publications have been reviewed on the basis of their application of deep learning with current advancement technology. Plant recognition, disease detection, counting, classification and yield estimation are among the many advancements of deep learning architecture employed in many applications in agriculture that are thoroughly investigated. Furthermore, this review helps to assist researchers to gain a deeper understanding and future application of deep learning in agriculture. According to all of the articles, the deep learning technique has successfully created significant accuracy and prediction in the model utilized. Finally, the existing challenges and future promises of deep learning in agriculture are discussed.
... As with conventional neural networks, DNNs comprise input layers, hidden layers, and output layers, and are able to model complex nonlinear relationships. DNNs have been recently developed into different forms, including recurrent neural networks (RNNs) in natural language processing and convolutional neural networks (CNNs) in computer vision: CNNs are primarily used for image identification as artificial neural networks to imitate visual processing methods, whereas RNNs were designed for the purpose of processing time series data such as audio, sensors, and characters [41,42]. ...
Although the Korean government has provided high-quality architectural building information for a long period of time, its focus on administrative details over three-dimensional (3D) architectural mapping and data collection has hindered progress. This study presents a basic method for extracting exterior building information for the purpose of 3D mapping using deep learning and digital image processing. The method identifies and classifies objects by using the fast regional convolutional neural network model. The results show an accuracy of 93% in the detection of façade and 91% window detection; this could be further improved by more clearly defining the boundaries of windows and reducing data noise. The additional metadata provided by the proposed method could, in the future, be included in building information modeling databases to facilitate structural analyses or reconstruction efforts.
... Smart agriculture has been emphasized to resolve these difficulties [2]. Smart agriculture includes the use of drones to monitor field growth [3,4], autonomous tractors to save labor [5], and machine learning to predict crop harvesting times [6]. This study specifically examines the prediction of crop harvesting times. ...
In recent years, the agricultural field has been confronting difficulties such as the aging of farmers, a shortage of workers, and difficulties for new farmers. Harvesting time prediction has the potential to solve these problems, and is expected to effectively utilize human resources, save labor, and reduce labor costs. To achieve harvesting time prediction, various works are being actively conducted. Methods for harvesting time prediction using meteorological information such as temperature and solar radiation, etc., and methods for harvesting time prediction using neural networks based on color information from fruit bunch images are being investigated. However, the prediction accuracy is still insufficient, and the harvesting time prediction for individual tomato fruits has not been studied. In this study, we propose a novel method to predict the harvesting time for individual tomato fruits. The method uses Mask R-CNN to detect tomato bunches and uses two types of ripeness determination to predict the harvesting time of individual tomato fruits. The experimental results showed that the accuracy of the prediction using the ratio of R values was better for the harvesting time prediction of tomatoes that are close to the harvesting time, and the accuracy of the prediction using the average of the differences between R and G in RGB values was better for the harvesting time prediction of tomatoes that are far from the harvesting time. These results show the effectiveness of the proposed method.
... This device is more able to tolerate the faults. In the idle country one or greater neurons now have longer effect on the resulting, and the output of processes had been acquired from produced neutrons [14]. ...
Changes in environmental factors such as water quality, soil quality, and pollution factors lead to diseases in food producing plants. Identifying plant disease is a very difficult task in agriculture. Plant diseases are also mainly caused by many influences in agriculture which includes hybrid genetics, and the plant lifetime during the infection, environmental changes like climatic changes, soil, temperature, rain, wind, weather etc. The infections may be single or mixed, according to the infections the plants diseases spread. Early detection of plant diseases using recent technologies helps the plants growth. Therefore, Machine Learning techniques are used for early prediction of the diseases. This paper is used to improve the accuracy of detecting plant diseases using the prediction of the soil content in the field land. The techniques Nave Bayes (NB) and Neural Network (NN) were used in the existing system. The proposed system uses Logistic Regression method with Long Short- Term Memory (LSTM) in Neural Networks (NN) for predicting the soil content and also detects the plant diseases, improves the accuracy level in the plant growth.
