Editorial

7 pages, 193 KiB

Open AccessEditorial

by Dimitre D. Dimitrov

Agriculture 2023, 13(1), 155; https://doi.org/10.3390/agriculture13010155 - 07 Jan 2023

Cited by 1 | Viewed by 4049

The Special Issue “Internet and Computers for Agriculture” reflects the rapidly growing need for new information and communication technology (ICT) involvement in agriculture which is changing globally [...] Full article

(This article belongs to the Special Issue Internet and Computers for Agriculture)

Research

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25 pages, 376 KiB

Open AccessArticle

Decision Support in Horticultural Supply Chains: A Planning Problem Framework for Small and Medium-Sized Enterprises

by Marius Drechsler and Andreas Holzapfel

Agriculture 2022, 12(11), 1922; https://doi.org/10.3390/agriculture12111922 - 15 Nov 2022

Cited by 2 | Viewed by 1881

Abstract

This paper investigates and systematizes planning problems along the supply chain of small and medium-sized companies in the horticultural market of ornamental plants, perennials, and cut flowers. The sector faces considerable challenges such as multiple planning uncertainties, product perishability, and considerable lead times. [...] Read more.

This paper investigates and systematizes planning problems along the supply chain of small and medium-sized companies in the horticultural market of ornamental plants, perennials, and cut flowers. The sector faces considerable challenges such as multiple planning uncertainties, product perishability, and considerable lead times. However, decisions in practice are often based on rules of thumb. Data-driven decision support is thus necessary to professionalize supply chain, logistics, and operations planning in the sector. We explore the practical planning problems with the help of expert interviews with people in charge of typical companies active in the market. We structure the planning problems along the supply chain according to their time horizon and highlight the critical elements of the planning tasks and horticultural specifics. We examine the status quo of research on decision support for these planning tasks with the help of a structured literature review, highlight research gaps, and outline promising future research directions. We find that the tactical planning domains of material/product requirement, production, and demand planning are especially critical in practice, and that there is a great need for research to develop practically relevant decision support systems. Such systems are currently available only to a limited extent in literature and are not fully compatible with requirements in the ornamental horticultural sector. By structuring and detailing the relevant decision problems, we contribute to an understanding of planning problems and decision-making in horticultural supply chains, and we provide a first comprehensive overview of planning problems, aligned literature, and research gaps for the horticultural business. Full article

(This article belongs to the Special Issue Internet and Computers for Agriculture)

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17 pages, 47689 KiB

Open AccessArticle

A LoRaWAN IoT System for Smart Agriculture for Vine Water Status Determination

by Antonio Valente, Carlos Costa, Leonor Pereira, Bruno Soares, José Lima and Salviano Soares

Agriculture 2022, 12(10), 1695; https://doi.org/10.3390/agriculture12101695 - 14 Oct 2022

Cited by 6 | Viewed by 4143

Abstract

In view of the actual climate change scenario felt across the globe, resource management is crucial, especially with regard to water. In this sense, continuous monitoring of plant water status is essential to optimise not only crop management but also water resources. Currently, [...] Read more.

In view of the actual climate change scenario felt across the globe, resource management is crucial, especially with regard to water. In this sense, continuous monitoring of plant water status is essential to optimise not only crop management but also water resources. Currently, monitoring of vine water status is done through expensive and time-consuming methods that do not allow continuous monitoring, which is especially inconvenient in places with difficult access. The aim of the developed work was to install three groups of sensors (Environmental, Plant and Soil) in a vineyard and connect them through LoRaWAN protocol for data transmission. The results demonstrate that the implemented system is capable of continuous data communication without data loss. The reduced cost and superior range of LoRaWAN compared to WiFi or Bluetooth is especially important for applications in remote areas where cellular networks have little coverage. Altogether, this methodology provides a remote, continuous and more effective method to monitor plant water status and is capable of supporting producers in more efficient management of their farms and water resources. Full article

(This article belongs to the Special Issue Internet and Computers for Agriculture)

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18 pages, 6114 KiB

Open AccessEditor’s ChoiceArticle

An Attention Mechanism-Improved YOLOv7 Object Detection Algorithm for Hemp Duck Count Estimation

by Kailin Jiang, Tianyu Xie, Rui Yan, Xi Wen, Danyang Li, Hongbo Jiang, Ning Jiang, Ling Feng, Xuliang Duan and Jianjun Wang

Agriculture 2022, 12(10), 1659; https://doi.org/10.3390/agriculture12101659 - 10 Oct 2022

Cited by 77 | Viewed by 13621

Abstract

Stocking density presents a key factor affecting livestock and poultry production on a large scale as well as animal welfare. However, the current manual counting method used in the hemp duck breeding industry is inefficient, costly in labor, less accurate, and prone to [...] Read more.

Stocking density presents a key factor affecting livestock and poultry production on a large scale as well as animal welfare. However, the current manual counting method used in the hemp duck breeding industry is inefficient, costly in labor, less accurate, and prone to double counting and omission. In this regard, this paper uses deep learning algorithms to achieve real-time monitoring of the number of dense hemp duck flocks and to promote the development of the intelligent farming industry. We constructed a new large-scale hemp duck object detection image dataset, which contains 1500 hemp duck object detection full-body frame labeling and head-only frame labeling. In addition, this paper proposes an improved attention mechanism YOLOv7 algorithm, CBAM-YOLOv7, adding three CBAM modules to the backbone network of YOLOv7 to improve the network’s ability to extract features and introducing SE-YOLOv7 and ECA-YOLOv7 for comparison experiments. The experimental results show that CBAM-YOLOv7 had higher precision, and the recall, mAP@0.5, and mAP@0.5:0.95 were slightly improved. The evaluation index value of CBAM-YOLOv7 improved more than those of SE-YOLOv7 and ECA-YOLOv7. In addition, we also conducted a comparison test between the two labeling methods and found that the head-only labeling method led to the loss of a high volume of feature information, and the full-body frame labeling method demonstrated a better detection effect. The results of the algorithm performance evaluation show that the intelligent hemp duck counting method proposed in this paper is feasible and can promote the development of smart reliable automated duck counting. Full article

(This article belongs to the Special Issue Internet and Computers for Agriculture)

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16 pages, 1262 KiB

Open AccessArticle

Influences of Government Policies and Farmers’ Cognition on Farmers’ Participation Willingness and Behaviors in E-Commerce Interest Linkage Mechanisms during Farmer–Enterprise Games

by Xiaolu Wei and Junhu Ruan

Agriculture 2022, 12(10), 1625; https://doi.org/10.3390/agriculture12101625 - 06 Oct 2022

Cited by 6 | Viewed by 1884

Abstract

E-commerce interest linkage mechanisms serve as an effective solution to the problems of farmer–market cooperation, agricultural supply-side reforms, and farmers’ income growth. This study, guided by the theory of planned behavior, undertook an evolutionary game analysis of farmer–enterprise cooperation with government interventions with [...] Read more.

E-commerce interest linkage mechanisms serve as an effective solution to the problems of farmer–market cooperation, agricultural supply-side reforms, and farmers’ income growth. This study, guided by the theory of planned behavior, undertook an evolutionary game analysis of farmer–enterprise cooperation with government interventions with farmers. Based on data from 554 questionnaires administered in Mei County, Shaanxi Province, China, this study found a difference between the realistic and optimal choices of farmers. In addition, this study used a structural equation model to investigate the influence of government policies and farmers’ cognition on the participation willingness and behaviors of farmers in e-commerce interest-linkage mechanisms. The results showed that the optimal choice for farmers in a farmer–enterprise cooperative game is participation in e-commerce, and government policies can be used to improve farmer–enterprise e-commerce interest-linkage mechanisms. Farmers’ basic characteristics and experiences impacted their cognition of e-commerce, which, in turn, had a significant positive effect on their e-commerce participation willingness and behaviors. Government policies had a positive effect on farmers’ experiences, cognition of e-commerce, and participation behaviors, but no direct positive impact on farmers’ willingness to participate. Government policies and farmers’ basic characteristics interacted and acted together on the participation willingness and behavior of farmers. Full article

(This article belongs to the Special Issue Internet and Computers for Agriculture)

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15 pages, 3483 KiB

Open AccessEditor’s ChoiceArticle

Tea Sprout Picking Point Identification Based on Improved DeepLabV3+

by Chunyu Yan, Zhonghui Chen, Zhilin Li, Ruixin Liu, Yuxin Li, Hui Xiao, Ping Lu and Benliang Xie

Agriculture 2022, 12(10), 1594; https://doi.org/10.3390/agriculture12101594 - 02 Oct 2022

Cited by 14 | Viewed by 2272

Abstract

Tea sprout segmentation and picking point localization via machine vision are the core technologies of automatic tea picking. This study proposes a method of tea segmentation and picking point location based on a lightweight convolutional neural network named MC-DM (Multi-Class DeepLabV3+ MobileNetV2 (Mobile [...] Read more.

Tea sprout segmentation and picking point localization via machine vision are the core technologies of automatic tea picking. This study proposes a method of tea segmentation and picking point location based on a lightweight convolutional neural network named MC-DM (Multi-Class DeepLabV3+ MobileNetV2 (Mobile Networks Vision 2)) to solve the problem of tea shoot picking point in a natural environment. In the MC-DM architecture, an optimized MobileNetV2 is used to reduce the number of parameters and calculations. Then, the densely connected atrous spatial pyramid pooling module is introduced into the MC-DM to obtain denser pixel sampling and a larger receptive field. Finally, an image dataset of high-quality tea sprout picking points is established to train and test the MC-DM network. Experimental results show that the MIoU of MC-DM reached 91.85%, which is improved by 8.35% compared with those of several state-of-the-art methods. The optimal improvements of model parameters and detection speed were 89.19% and 16.05 f/s, respectively. After the segmentation results of the MC-DM were applied to the picking point identification, the accuracy of picking point identification reached 82.52%, 90.07%, and 84.78% for single bud, one bud with one leaf, and one bud with two leaves, respectively. This research provides a theoretical reference for fast segmentation and visual localization of automatically picked tea sprouts. Full article

(This article belongs to the Special Issue Internet and Computers for Agriculture)

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19 pages, 12920 KiB

Open AccessArticle

Identification of Buffalo Breeds Using Self-Activated-Based Improved Convolutional Neural Networks

by Yuanzhi Pan, Hua Jin, Jiechao Gao and Hafiz Tayyab Rauf

Agriculture 2022, 12(9), 1386; https://doi.org/10.3390/agriculture12091386 - 03 Sep 2022

Cited by 5 | Viewed by 2236

Abstract

The livestock of Pakistan includes different animal breeds utilized for milk farming and exporting worldwide. Buffalo have a high milk production rate, and Pakistan is the third-largest milk-producing country, and its production is increasing over time. Hence, it is essential to recognize the [...] Read more.

The livestock of Pakistan includes different animal breeds utilized for milk farming and exporting worldwide. Buffalo have a high milk production rate, and Pakistan is the third-largest milk-producing country, and its production is increasing over time. Hence, it is essential to recognize the best Buffalo breed for a high milk- and meat yield to meet the world’s demands and breed production. Pakistan has the second-largest number of buffalos among countries worldwide, where the Neli-Ravi breed is the most common. The extensive demand for Neli and Ravi breeds resulted in the new cross-breed “Neli-Ravi” in the 1960s. Identifying and segregating the Neli-Ravi breed from other buffalo breeds is the most crucial concern for Pakistan’s dairy-production centers. Therefore, the automatic detection and classification of buffalo breeds are required. In this research, a computer-vision-based recognition framework is proposed to identify and classify the Neli-Ravi breed from other buffalo breeds. The proposed framework employs self-activated-based improved convolutional neural networks (CNN) combined with self-transfer learning. Moreover, feature maps extracted from CNN are further transferred to obtain rich feature vectors. Different machine learning (Ml) classifiers are adopted to classify the feature vectors. The proposed framework is evaluated on two buffalo breeds, namely, Neli-Ravi and Khundi, and one additional target class contains different buffalo breeds collectively called Mix. The proposed research achieves a maximum of 93% accuracy using SVM and more than 85% accuracy employing recent variants. Full article

(This article belongs to the Special Issue Internet and Computers for Agriculture)

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17 pages, 12613 KiB

Open AccessArticle

A Novel Lightweight Grape Detection Method

by Shuzhi Su, Runbin Chen, Xianjin Fang, Yanmin Zhu, Tian Zhang and Zengbao Xu

Agriculture 2022, 12(9), 1364; https://doi.org/10.3390/agriculture12091364 - 01 Sep 2022

Cited by 8 | Viewed by 1740

Abstract

This study proposes a novel lightweight grape detection method. First, the backbone network of our method is Uniformer, which captures long-range dependencies and further improves the feature extraction capability. Then, a Bi-directional Path Aggregation Network (BiPANet) is presented to fuse low-resolution feature maps [...] Read more.

This study proposes a novel lightweight grape detection method. First, the backbone network of our method is Uniformer, which captures long-range dependencies and further improves the feature extraction capability. Then, a Bi-directional Path Aggregation Network (BiPANet) is presented to fuse low-resolution feature maps with strong semantic information and high-resolution feature maps with detailed information. BiPANet is constructed by introducing a novel cross-layer feature enhancement strategy into the Path Aggregation Network, which fuses more feature information with a significant reduction in the number of parameters and computational complexity. To improve the localization accuracy of the optimal bounding boxes, a Reposition Non-Maximum Suppression (R-NMS) algorithm is further proposed in post-processing. The algorithm performs repositioning operations on the optimal bounding boxes by using the position information of the bounding boxes around the optimal bounding boxes. Experiments on the WGISD show that our method achieves 87.7% mAP, 88.6% precision, 78.3% recall, 83.1% F1 score, and 46 FPS. Compared with YOLOx, YOLOv4, YOLOv3, Faster R-CNN, SSD, and RetinaNet, the mAP of our method is increased by 0.8%, 1.7%, 3.5%, 21.4%, 2.5%, and 13.3%, respectively, and the FPS of our method is increased by 2, 8, 2, 26, 0, and 10, respectively. Similar conclusions can be obtained on another grape dataset. Encouraging experimental results show that our method can achieve better performance than other recognized detection methods in the grape detection tasks. Full article

(This article belongs to the Special Issue Internet and Computers for Agriculture)

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26 pages, 4423 KiB

Open AccessArticle

A Machine Learning Model for Early Prediction of Crop Yield, Nested in a Web Application in the Cloud: A Case Study in an Olive Grove in Southern Spain

by Juan J. Cubillas, María I. Ramos, Juan M. Jurado and Francisco R. Feito

Agriculture 2022, 12(9), 1345; https://doi.org/10.3390/agriculture12091345 - 31 Aug 2022

Cited by 9 | Viewed by 3097

Abstract

Predictive systems are a crucial tool in management and decision-making in any productive sector. In the case of agriculture, it is especially interesting to have advance information on the profitability of a farm. In this sense, depending on the time of the year [...] Read more.

Predictive systems are a crucial tool in management and decision-making in any productive sector. In the case of agriculture, it is especially interesting to have advance information on the profitability of a farm. In this sense, depending on the time of the year when this information is available, important decisions can be made that affect the economic balance of the farm. The aim of this study is to develop an effective model for predicting crop yields in advance that is accessible and easy to use by the farmer or farm manager from a web-based application. In this case, an olive orchard in the Andalusia region of southern Spain was used. The model was estimated using spatio-temporal training data, such as yield data from eight consecutive years, and more than twenty meteorological parameters data, automatically charged from public web services, belonging to a weather station located near the sample farm. The workflow requires selecting the parameters that influence the crop prediction and discarding those that introduce noise into the model. The main contribution of this research is the early prediction of crop yield with absolute errors better than 20%, which is crucial for making decisions on tillage investments and crop marketing. Full article

(This article belongs to the Special Issue Internet and Computers for Agriculture)

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23 pages, 4156 KiB

Open AccessArticle

IoAT Enabled Smart Farming: Urdu Language-Based Solution for Low-Literate Farmers

by Sehrish Munawar Cheema, Muhammad Ali, Ivan Miguel Pires, Norberto Jorge Gonçalves, Mustahsan Hammad Naqvi and Maleeha Hassan

Agriculture 2022, 12(8), 1277; https://doi.org/10.3390/agriculture12081277 - 22 Aug 2022

Cited by 11 | Viewed by 13034

Abstract

The agriculture sector is the backbone of Pakistan’s economy, reflecting 26% of its GPD and 43% of the entire labor force. Smart and precise agriculture is the key to producing the best crop yield. Moreover, emerging technologies are reducing energy consumption and cost-effectiveness [...] Read more.

The agriculture sector is the backbone of Pakistan’s economy, reflecting 26% of its GPD and 43% of the entire labor force. Smart and precise agriculture is the key to producing the best crop yield. Moreover, emerging technologies are reducing energy consumption and cost-effectiveness for saving agricultural resources in control and monitoring systems, especially for those areas lacking these resources. Agricultural productivity is thwarted in many areas of Pakistan due to farmers’ illiteracy, lack of a smart system for remote access to farmland, and an absence of proactive decision-making in all phases of the crop cycle available in their native language. This study proposes an internet of agricultural things (IoAT) based smart system armed with a set of economical, accessible devices and sensors to capture real-time parameters of farms such as soil moisture level, temperature, soil pH level, light intensity, and humidity on frequent intervals of time. The system analyzes the environmental parameters of specific farms and enables the farmers to understand soil and environmental factors, facilitating farmers in terms of soil fertility analysis, suitable crop cultivation, automated irrigation and guidelines, harvest schedule, pest and weed control, crop disease awareness, and fertilizer guidance. The system is integrated with an android application ‘Kistan Pakistan’ (prototype) designed in bilingual, i.e., ‘Urdu’ and ‘English’. The mobile application is equipped with visual components, audio, voice, and iconic and textual menus to be used by diverse literary levels of farmers. Full article

(This article belongs to the Special Issue Internet and Computers for Agriculture)

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26 pages, 3820 KiB

Open AccessArticle

An Aquatic Product Price Forecast Model Using VMD-IBES-LSTM Hybrid Approach

by Junhao Wu, Yuan Hu, Daqing Wu and Zhengyong Yang

Agriculture 2022, 12(8), 1185; https://doi.org/10.3390/agriculture12081185 - 09 Aug 2022

Cited by 8 | Viewed by 1938

Abstract

Changes in the consumption price of aquatic products will affect demand and fishermen’s income. The accurate prediction of consumer price index provides important information regarding the aquatic product market. Based on the non-linear and non-smooth characteristics of fishery product price series, this paper [...] Read more.

Changes in the consumption price of aquatic products will affect demand and fishermen’s income. The accurate prediction of consumer price index provides important information regarding the aquatic product market. Based on the non-linear and non-smooth characteristics of fishery product price series, this paper innovatively proposes a fishery product price forecasting model that is based on Variational Modal Decomposition and Improved bald eagle search algorithm optimized Long Short Term Memory Network (VMD-IBES-LSTM). Empirical analysis was conducted using fish price data from the Department of Marketing and Informatization of the Ministry of Agriculture and Rural Affairs of China. The proposed model in this study was subsequently compared with common forecasting models such as VMD-LSTM and SSA-LSTM. The research results show that the VMD-IBES-LSTM model that was constructed in this paper has good fitting results and high prediction accuracy, which can better explain the seasonality and trends of the change of China’s aquatic product consumer price index, provide a scientific and effective method for relevant management departments and units to predict the aquatic product consumer price, and have a certain reference value for reasonably coping with the fluctuation of China’s aquatic product market price. Full article

(This article belongs to the Special Issue Internet and Computers for Agriculture)

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17 pages, 7466 KiB

Open AccessEditor’s ChoiceArticle

GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases

by Jianwu Lin, Xiaoyulong Chen, Renyong Pan, Tengbao Cao, Jitong Cai, Yang Chen, Xishun Peng, Tomislav Cernava and Xin Zhang

Agriculture 2022, 12(6), 887; https://doi.org/10.3390/agriculture12060887 - 20 Jun 2022

Cited by 33 | Viewed by 4910

Abstract

Most convolutional neural network (CNN) models have various difficulties in identifying crop diseases owing to morphological and physiological changes in crop tissues, and cells. Furthermore, a single crop disease can show different symptoms. Usually, the differences in symptoms between early crop disease and [...] Read more.

Most convolutional neural network (CNN) models have various difficulties in identifying crop diseases owing to morphological and physiological changes in crop tissues, and cells. Furthermore, a single crop disease can show different symptoms. Usually, the differences in symptoms between early crop disease and late crop disease stages include the area of disease and color of disease. This also poses additional difficulties for CNN models. Here, we propose a lightweight CNN model called GrapeNet for the identification of different symptom stages for specific grape diseases. The main components of GrapeNet are residual blocks, residual feature fusion blocks (RFFBs), and convolution block attention modules. The residual blocks are used to deepen the network depth and extract rich features. To alleviate the CNN performance degradation associated with a large number of hidden layers, we designed an RFFB module based on the residual block. It fuses the average pooled feature map before the residual block input and the high-dimensional feature maps after the residual block output by a concatenation operation, thereby achieving feature fusion at different depths. In addition, the convolutional block attention module (CBAM) is introduced after each RFFB module to extract valid disease information. The obtained results show that the identification accuracy was determined as 82.99%, 84.01%, 82.74%, 84.77%, 80.96%, 82.74%, 80.96%, 83.76%, and 86.29% for GoogLeNet, Vgg16, ResNet34, DenseNet121, MobileNetV2, MobileNetV3_large, ShuffleNetV2_×1.0, EfficientNetV2_s, and GrapeNet. The GrapeNet model achieved the best classification performance when compared with other classical models. The total number of parameters of the GrapeNet model only included 2.15 million. Compared with DenseNet121, which has the highest accuracy among classical network models, the number of parameters of GrapeNet was reduced by 4.81 million, thereby reducing the training time of GrapeNet by about two times compared with that of DenseNet121. Moreover, the visualization results of Grad-cam indicate that the introduction of CBAM can emphasize disease information and suppress irrelevant information. The overall results suggest that the GrapeNet model is useful for the automatic identification of grape leaf diseases. Full article

(This article belongs to the Special Issue Internet and Computers for Agriculture)

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23 pages, 1922 KiB

Open AccessArticle

A Conceptual Model for Development of Small Farm Management Information System: A Case of Indonesian Smallholder Chili Farmers

by Henriyadi Henriyadi, Vatcharaporn Esichaikul and Chutiporn Anutariya

Agriculture 2022, 12(6), 866; https://doi.org/10.3390/agriculture12060866 - 15 Jun 2022

Cited by 1 | Viewed by 3105

Abstract

Farm Management Information Systems (FMIS) assists farmers in managing their farms more effectively and efficiently. However, the use of FMIS to support crop cultivation is, at the present time, relatively expensive for smallholder farmers. Due to some handicaps, providing an FMIS that is [...] Read more.

Farm Management Information Systems (FMIS) assists farmers in managing their farms more effectively and efficiently. However, the use of FMIS to support crop cultivation is, at the present time, relatively expensive for smallholder farmers. Due to some handicaps, providing an FMIS that is suitable for small-holder farmers is a challenge. To analyze this gap, this study followed 3 steps, namely: (1) identified commodity and research area, (2) performed Farmers’ Information Needs Assessment (FINA), and (3) developed the conceptual model using the Soft System Methodology. Indonesian smallholder chili farmers are used as a case study. The most required information of smallholder’ farmers was identified through a qualitative questionnaire. Despite this, not all identified information needs could be accurately mapped. Thus, this indicates the need for a new FMIS conceptual model that is suitable for smallholder farmers. This study proposes an FMIS conceptual model for farm efficiency that incorporates five layers, namely farmers’ information needs, data quality assessment, data extraction, SMM (split, match and merge), and presentation layer. SMM layer also provides a method to comprehensively tackle three main problems in data interoperability problems, namely schema heterogeneity, schema granularity, and mismatch entity naming. Full article

(This article belongs to the Special Issue Internet and Computers for Agriculture)

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28 pages, 1915 KiB

Open AccessEditor’s ChoiceArticle

Research on the Time-Dependent Split Delivery Green Vehicle Routing Problem for Fresh Agricultural Products with Multiple Time Windows

by Daqing Wu and Chenxiang Wu

Agriculture 2022, 12(6), 793; https://doi.org/10.3390/agriculture12060793 - 30 May 2022

Cited by 70 | Viewed by 3950

Abstract

Due to the diversity and the different distribution conditions of agricultural products, split delivery plays an important role in the last mile distribution of agricultural products distribution. The time-dependent split delivery green vehicle routing problem with multiple time windows (TDSDGVRPMTW) is studied by [...] Read more.

Due to the diversity and the different distribution conditions of agricultural products, split delivery plays an important role in the last mile distribution of agricultural products distribution. The time-dependent split delivery green vehicle routing problem with multiple time windows (TDSDGVRPMTW) is studied by considering both economic cost and customer satisfaction. A calculation method for road travel time across time periods was designed. A satisfaction measure function based on a time window and a measure function of the economic cost was employed by considering time-varying vehicle speeds, fuel consumption, carbon emissions and customers’ time windows. The object of the TDSDGVRPMTW model is to minimize the sum of the economic cost and maximize average customer satisfaction. According to the characteristics of the model, a variable neighborhood search combined with a non-dominated sorting genetic algorithm II (VNS-NSGA-II) was designed. Finally, the experimental data show that the proposed approaches effectively reduce total distribution costs and promote energy conservation and customer satisfaction. Full article

(This article belongs to the Special Issue Internet and Computers for Agriculture)

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15 pages, 3334 KiB

Open AccessArticle

Climate Resilience and Environmental Sustainability: How to Integrate Dynamic Dimensions of Water Security Modeling

by Syed Abu Shoaib, Muhammad Muhitur Rahman, Faisal I. Shalabi, Ammar Fayez Alshayeb and Ziad Nayef Shatnawi

Agriculture 2022, 12(2), 303; https://doi.org/10.3390/agriculture12020303 - 21 Feb 2022

Cited by 1 | Viewed by 2333

Abstract

Considering hydro-climatic diversity, integrating dynamic dimensions of water security modeling is vital for ensuring environmental sustainability and its associated full range of climate resilience. Improving climate resiliency depends on the attributing uncertainty mechanism. In this study, a conceptual resilience model is presented with [...] Read more.

Considering hydro-climatic diversity, integrating dynamic dimensions of water security modeling is vital for ensuring environmental sustainability and its associated full range of climate resilience. Improving climate resiliency depends on the attributing uncertainty mechanism. In this study, a conceptual resilience model is presented with the consideration of input uncertainty. The impact of input uncertainty is analyzed through a multi-model hydrological framework. A multi-model hydrological framework is attributed to a possible scenario to help apply it in a decision-making process. This study attributes water security modeling with the considerations of sustainability and climate resilience using a high-speed computer and Internet system. Then, a subsequent key point of this investigation is accounting for water security modeling to ensure food security and model development scenarios. In this context, a four-dimensional dynamic space that maps sources, resource availability, infrastructure, and vibrant economic options is essential in ensuring a climate-resilient sustainable domain. This information can be disseminated to farmers using a central decision support system to ensure sustainable food production with the application of a digital system. Full article

(This article belongs to the Special Issue Internet and Computers for Agriculture)

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16 pages, 2949 KiB

Open AccessEditor’s ChoiceArticle

Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning

by Peng Xu, Qian Tan, Yunpeng Zhang, Xiantao Zha, Songmei Yang and Ranbing Yang

Agriculture 2022, 12(2), 232; https://doi.org/10.3390/agriculture12020232 - 06 Feb 2022

Cited by 33 | Viewed by 5921

Abstract

Maize is one of the essential crops for food supply. Accurate sorting of seeds is critical for cultivation and marketing purposes, while the traditional methods of variety identification are time-consuming, inefficient, and easily damaged. This study proposes a rapid classification method for maize [...] Read more.

Maize is one of the essential crops for food supply. Accurate sorting of seeds is critical for cultivation and marketing purposes, while the traditional methods of variety identification are time-consuming, inefficient, and easily damaged. This study proposes a rapid classification method for maize seeds using a combination of machine vision and deep learning. 8080 maize seeds of five varieties were collected, and then the sample images were classified into training and validation sets in the proportion of 8:2, and the data were enhanced. The proposed improved network architecture, namely P-ResNet, was fine-tuned for transfer learning to recognize and categorize maize seeds, and then it compares the performance of the models. The results show that the overall classification accuracy was determined as 97.91, 96.44, 99.70, 97.84, 98.58, 97.13, 96.59, and 98.28% for AlexNet, VGGNet, P-ResNet, GoogLeNet, MobileNet, DenseNet, ShuffleNet, and EfficientNet, respectively. The highest classification accuracy result was obtained with P-ResNet, and the model loss remained at around 0.01. This model obtained the accuracy of classifications for BaoQiu, ShanCu, XinNuo, LiaoGe, and KouXian varieties, which reached 99.74, 99.68, 99.68, 99.61, and 99.80%, respectively. The experimental results demonstrated that the convolutional neural network model proposed enables the effective classification of maize seeds. It can provide a reference for identifying seeds of other crops and be applied to consumer use and the food industry. Full article

(This article belongs to the Special Issue Internet and Computers for Agriculture)

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31 pages, 6755 KiB

Open AccessArticle

An Agile AI and IoT-Augmented Smart Farming: A Cost-Effective Cognitive Weather Station

by Amine Faid, Mohamed Sadik and Essaid Sabir

Agriculture 2022, 12(1), 35; https://doi.org/10.3390/agriculture12010035 - 29 Dec 2021

Cited by 21 | Viewed by 5494

Abstract

Internet of Things (IoT) can be seen as the electricity of 21st century. It has been reshaping human life daily during the last decade, with various applications in several critical domains such as agriculture. Smart farming is a real-world application in which Internet [...] Read more.

Internet of Things (IoT) can be seen as the electricity of 21st century. It has been reshaping human life daily during the last decade, with various applications in several critical domains such as agriculture. Smart farming is a real-world application in which Internet of Things (IoT) technologies like agro-weather stations can have a direct impact on humans by enhancing crop quality, supporting sustainable agriculture, and eventually generating steady growth. Meanwhile, most agro-weather solutions are neither customized nor affordable for small farmers within developing countries. Furthermore, due to the outdoor challenges, it is often a challenge to develop and deploy low-cost yet robust systems. Robustness, which is determined by several factors, including energy consumption, portability, interoperability, and system’s ease of use. In this paper, we present an agile AI-Powered IoT-based low-cost platform for cognitive monitoring for smart farming. The hybrid Multi-Agent and the fully containerized system continuously surveys multiple agriculture parameters such as temperature, humidity, and pressure to provide end-users with real-time environmental data and AI-based forecasts. The surveyed data is ensured through several heterogeneous nodes deployed within the base station and in the open sensing area. The collected data is transmitted to the local server for pre-processing and the cloud server for backup. The system backbone communication is based on heterogeneous protocols such as MQTT, NRF24L01, and WiFi for radio communication. We also set up a user-friendly web-based graphical user interface (GUI) to support different user profiles. The overall platform design follows an agile approach to be easy to deploy, accessible to maintain, and continuously modernized. Full article

(This article belongs to the Special Issue Internet and Computers for Agriculture)

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20 pages, 29626 KiB

Open AccessArticle

ACE-ADP: Adversarial Contextual Embeddings Based Named Entity Recognition for Agricultural Diseases and Pests

by Xuchao Guo, Xia Hao, Zhan Tang, Lei Diao, Zhao Bai, Shuhan Lu and Lin Li

Agriculture 2021, 11(10), 912; https://doi.org/10.3390/agriculture11100912 - 24 Sep 2021

Cited by 7 | Viewed by 2127

Abstract

Entity recognition tasks, which aim to utilize the deep learning-based models to identify the agricultural diseases and pests-related nouns such as the names of diseases, pests, and drugs from the texts collected on the internet or input by users, are a fundamental component [...] Read more.

Entity recognition tasks, which aim to utilize the deep learning-based models to identify the agricultural diseases and pests-related nouns such as the names of diseases, pests, and drugs from the texts collected on the internet or input by users, are a fundamental component for agricultural knowledge graph construction and question-answering, which will be implemented as a web application and provide the general public with solutions for agricultural diseases and pest control. Nonetheless, there are still challenges: (1) the polysemous problem needs to be further solved, (2) the quality of the text representation needs to be further enhanced, (3) the performance for rare entities needs to be further improved. We proposed an adversarial contextual embeddings-based model named ACE-ADP for named entity recognition in Chinese agricultural diseases and pests domain (CNER-ADP). First, we enhanced the text representation and overcame the polysemy problem by using the fine-tuned BERT model to generate the contextual character-level embedded representation with the specific knowledge. Second, adversarial training was also introduced to enhance the generalization and robustness in terms of identifying the rare entities. The experimental results showed that our model achieved an F₁ of 98.31% with 4.23% relative improvement compared to the baseline model (i.e., word2vec-based BiLSTM-CRF) on the self-annotated corpus named Chinese named entity recognition dataset for agricultural diseases and pests (AgCNER). Besides, the ablation study and discussion demonstrated that ACE-ADP could not only effectively extract rare entities but also maintain a powerful ability to predict new entities in new datasets with high accuracy. It could be used as a basis for further research on other domain-specific named entity recognition. Full article

(This article belongs to the Special Issue Internet and Computers for Agriculture)

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16 pages, 2084 KiB

Open AccessArticle

Fused Deep Features-Based Grape Varieties Identification Using Support Vector Machine

by Yun Peng, Shenyi Zhao and Jizhan Liu

Agriculture 2021, 11(9), 869; https://doi.org/10.3390/agriculture11090869 - 10 Sep 2021

Cited by 12 | Viewed by 1998

Abstract

Proper identification of different grape varieties by smart machinery is of great importance to modern agriculture production. In this paper, a fast and accurate identification method based on Canonical Correlation Analysis (CCA), which can fuse different deep features extracted from Convolutional Neural Network [...] Read more.

Proper identification of different grape varieties by smart machinery is of great importance to modern agriculture production. In this paper, a fast and accurate identification method based on Canonical Correlation Analysis (CCA), which can fuse different deep features extracted from Convolutional Neural Network (CNN), plus Support Vector Machine (SVM) is proposed. In this research, based on an open dataset, three types of state-of-the-art CNNs, seven species of deep features, and a multi-class SVM classifier were studied. First, the images were resized to meet the input requirements of a CNN. Then, the deep features of the input images were extracted by a specific deep features layer of the CNN. Next, two kinds of deep features from different networks were fused by CCA to increase the effective classification feature information. Finally, a multi-class SVM classifier was trained with the fused features. When applied to an open dataset, the model outcome shows that the fused deep features with any combination can obtain better identification performance than by using a single type of deep feature. The fusion of fc6 (in AlexNet network) and Fc1000 (in ResNet50 network) deep features obtained the best identification performance. The average F1 Score of 96.9% was 8.7% higher compared to the best performance of a single deep feature, i.e., Fc1000 of ResNet101, which was 88.2%. Furthermore, the F1 Score of the proposed method is 2.7% higher than the best performance obtained by using a CNN directly. The experimental results show that the method proposed in this paper can achieve fast and accurate identification of grape varieties. Based on the proposed algorithm, the smart machinery in agriculture can take more targeted measures based on the different characteristics of different grape varieties for further improvement of the yield and quality of grape production. Full article

(This article belongs to the Special Issue Internet and Computers for Agriculture)

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Review

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20 pages, 1124 KiB

Open AccessReview

Potential Role of Technology Innovation in Transformation of Sustainable Food Systems: A Review

by Nawab Khan, Ram L. Ray, Hazem S. Kassem, Sajjad Hussain, Shemei Zhang, Muhammad Khayyam, Muhammad Ihtisham and Simplice A. Asongu

Agriculture 2021, 11(10), 984; https://doi.org/10.3390/agriculture11100984 - 09 Oct 2021

Cited by 40 | Viewed by 9780

Abstract

Advanced technologies and innovation are essential for promoting sustainable food systems (SFSs) because these technologies can be used to answer some of the critical questions needed to transform SFSs and help us better understand global food security and nutrition. The main objective of [...] Read more.

Advanced technologies and innovation are essential for promoting sustainable food systems (SFSs) because these technologies can be used to answer some of the critical questions needed to transform SFSs and help us better understand global food security and nutrition. The main objective of this study is to address the question of whether technological innovations have an impact on the transformation of SFSs. There are certain innovations including agricultural land utilization, food processing, production systems, improvement in diets according to people’s needs, and management of waste products. This study provides an overview of new technologies and innovations being used with potential to transform SFSs. Applications of emerging technologies in digital agriculture, including the Internet of Things (IoT), artificial intelligence and machine learning, drones, use of new physical systems (e.g., advanced robotics, autonomous vehicles, advanced materials), and gene technology (e.g., biofortified crops, genome-wide selection, genome editing), are discussed in this study. Additionally, we suggest eight action initiatives, which are transforming mindsets, enabling social licensing, changing policies and regulations, designing market incentives, safeguarding against undesirable effects, ensuring stable finance, building trust, and developing transition pathways that can hasten the transition to more SFSs. We conclude that appropriate incentives, regulations, and social permits play a critical role in enhancing the adoption of modern technologies to promote SFSs. Full article

(This article belongs to the Special Issue Internet and Computers for Agriculture)

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Other

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19 pages, 2551 KiB

Open AccessCase Report

Leisure Agricultural Park Selection for Traveler Groups Amid the COVID-19 Pandemic

by Hsin-Chieh Wu, Yu-Cheng Lin and Tin-Chih Toly Chen

Agriculture 2022, 12(1), 111; https://doi.org/10.3390/agriculture12010111 - 13 Jan 2022

Cited by 13 | Viewed by 1962

Abstract

With the widespread vaccination against COVID-19, people began to resume regional tourism. Outdoor attractions, such as leisure agricultural parks, are particularly attractive because they are well ventilated and can prevent the spread of COVID-19. However, during the COVID-19 pandemic, the considerations around choosing [...] Read more.

With the widespread vaccination against COVID-19, people began to resume regional tourism. Outdoor attractions, such as leisure agricultural parks, are particularly attractive because they are well ventilated and can prevent the spread of COVID-19. However, during the COVID-19 pandemic, the considerations around choosing a leisure agricultural park are different from usual, and will be affected by uncertainty. Therefore, this research proposes a fuzzy collaborative intelligence (FCI) approach to help select leisure agricultural parks suitable for traveler groups during the COVID-19 pandemic. The proposed FCI approach combines asymmetrically calibrated fuzzy geometric mean (acFGM), fuzzy weighted intersection (FWI), and fuzzy Vise Kriterijumska Optimizacija I Kompromisno Resenje (fuzzy VIKOR), which is a novel attempt in this field. The effectiveness of the proposed FCI approach has been verified by a case study in Taichung City, Taiwan. The results of the case study showed that during the COVID-19 pandemic, travelers (especially traveler groups) were very willing to go to leisure agricultural parks. In addition, the most important criterion for choosing a suitable leisure agricultural park was the ease of maintaining social distance, while the least important criterion was the distance from a leisure agricultural park. Further, the successful recommendation rate using the proposed methodology was as high as 90%. Full article

(This article belongs to the Special Issue Internet and Computers for Agriculture)

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