AgriEngineering

Research

16 pages, 5361 KiB

Open AccessArticle

TFEMRNet: A Two-Stage Multi-Feature Fusion Model for Efficient Small Pest Detection on Edge Platforms

by Junlin Mu, Linlin Sun, Bo Ma, Ruofei Liu, Shuangxi Liu, Xianliang Hu, Hongjian Zhang and Jinxing Wang

AgriEngineering 2024, 6(4), 4688-4703; https://doi.org/10.3390/agriengineering6040268 - 5 Dec 2024

Viewed by 607

Abstract

Currently, intelligent pest monitoring systems transmit entire monitoring images to cloud servers for analysis. This approach not only consumes significant bandwidth and increases monitoring costs, but also struggles with accurately recognizing small-target and overlapping pests. To overcome these challenges, this paper introduces a [...] Read more.

Currently, intelligent pest monitoring systems transmit entire monitoring images to cloud servers for analysis. This approach not only consumes significant bandwidth and increases monitoring costs, but also struggles with accurately recognizing small-target and overlapping pests. To overcome these challenges, this paper introduces a two-stage multi-feature fusion small-target pest detection algorithm based on edge computing devices, termed TFEMRNet. The algorithm initially conducts semantic segmentation on an edge processor, followed by uploading the segmented images to a cloud server for target identification. Specifically, the semantic segmentation model TFENet incorporates a Multi-Attention Channel Aggregation (MACA) module, which integrates semantic features from EfficientNet-Pest and Swin Transformer, thereby enhancing the model’s ability to extract features of small-target pests. Experimental results demonstrate that TFEMRNet surpasses models such as YOLOv11, Fast R-CNN, and Mask R-CNN on small-target pest datasets, achieving precision of 96.75%, recall of 96.45%, and an F1 score of 95.60%. Notably, the TFENet model within TFEMRNet attains an IoU of 91.63% in semantic segmentation accuracy, outperforming other segmentation models such as U-Net and PSPNet. These findings affirm TFEMRNet’s superior efficacy in small-target pest detection, offering an effective solution for agricultural pest monitoring. Full article

(This article belongs to the Special Issue Application of Artificial Neural Network in Agriculture)

► Show Figures

Figure 1

19 pages, 30871 KiB

Open AccessArticle

Comparative Analysis of YOLO Models for Bean Leaf Disease Detection in Natural Environments

by Diana-Carmen Rodríguez-Lira, Diana-Margarita Córdova-Esparza, José M. Álvarez-Alvarado, Julio-Alejandro Romero-González, Juan Terven and Juvenal Rodríguez-Reséndiz

AgriEngineering 2024, 6(4), 4585-4603; https://doi.org/10.3390/agriengineering6040262 - 30 Nov 2024

Viewed by 688

Abstract

This study presents a comparative analysis of YOLO detection models for the accurate identification of bean leaf diseases caused by Coleoptera pests in natural environments. By using a manually collected dataset of healthy and infected bean leaves in natural conditions, we labeled at [...] Read more.

This study presents a comparative analysis of YOLO detection models for the accurate identification of bean leaf diseases caused by Coleoptera pests in natural environments. By using a manually collected dataset of healthy and infected bean leaves in natural conditions, we labeled at the leaf level and evaluated the performance of the YOLOv5, YOLOv8, YOLOv9, YOLOv10, and YOLOv11 models. Mean average precision (mAP) was used to assess the performance of the models. Among these, YOLOv9e exhibited the best performance, effectively balancing precision and recall for datasets with limited size and variability. In addition, we integrated the Sophia optimizer and PolyLoss function into YOLOv9e and enhanced it, providing even more accurate detection results. This paper highlights the potential of advanced deep learning models, optimized with second-order optimizers and custom loss functions, in improving pest detection, crop management, and overall agricultural yield. Full article

(This article belongs to the Special Issue Application of Artificial Neural Network in Agriculture)

► Show Figures

Figure 1

11 pages, 2679 KiB

Open AccessArticle

Multispectral Sensors and Machine Learning as Modern Tools for Nutrient Content Prediction in Soil

by Rafael Felippe Ratke, Paulo Roberto Nunes Viana, Larissa Pereira Ribeiro Teodoro, Fábio Henrique Rojo Baio, Paulo Eduardo Teodoro, Dthenifer Cordeiro Santana, Carlos Eduardo da Silva Santos, Alan Mario Zuffo and Jorge González Aguilera

AgriEngineering 2024, 6(4), 4384-4394; https://doi.org/10.3390/agriengineering6040248 - 21 Nov 2024

Viewed by 584

Abstract

The combination of multispectral data and machine learning provides effective and flexible monitoring of the soil nutrient content, which consequently positively impacts plant productivity and food security, and ultimately promotes sustainable agricultural development overall. The aim of this study was to investigate the [...] Read more.

The combination of multispectral data and machine learning provides effective and flexible monitoring of the soil nutrient content, which consequently positively impacts plant productivity and food security, and ultimately promotes sustainable agricultural development overall. The aim of this study was to investigate the associations between spectral variables and soil physicochemical attributes, as well as to predict these attributes using spectral variables as inputs in machine learning models. One thousand soil samples were selected from agricultural areas 0–20 cm deep and collected from Northeast Mato Grosso do Sul state of Brazil. A total of 20 g of the dried and homogenized soil sample was added to the Petri dish to perform spectral measurements. Reflectance spectra were obtained by CROP CIRCLE ACS-470 using three spectral bands: green (532–550 nm), red (670–700 nm), and red-edge (730–760 nm). The models were developed with the aid of the Weka environment to predict the soil chemical attributes via the obtained dataset. The models tested were linear regression, random forest (RF), reptree M5P, multilayer preference neural network, and decision tree algorithms, with the correlation coefficient (r) and mean absolute error (MAE) used as accuracy parameters. According to our findings, sulfur exhibited a correlation greater than 0.6 and a reduced mean absolute error, with better performance for the M5P and RF algorithms. On the other hand, the macronutrients S, Ca, Mg, and K presented modest r values (approximately 0.3), indicating a moderate correlation with actual observations, which are not recommended for use in soil analysis. This soil analysis technique requires more refined correlation models for accurate prediction. Full article

(This article belongs to the Special Issue Application of Artificial Neural Network in Agriculture)

► Show Figures

Figure 1

19 pages, 2455 KiB

Open AccessArticle

Climate-Based Prediction of Rice Blast Disease Using Count Time Series and Machine Learning Approaches

by Meena Arumugam Gopalakrishnan, Gopalakrishnan Chellappan, Santhosh Ganapati Patil, Santosha Rathod, Kamalakannan Ayyanar, Jagadeeswaran Ramasamy, Sathyamoorthy Nagaranai Karuppasamy and Manonmani Swaminathan

AgriEngineering 2024, 6(4), 4353-4371; https://doi.org/10.3390/agriengineering6040246 - 19 Nov 2024

Viewed by 648

Abstract

Magnaporthe oryzae, the source of the rice blast, is a serious threat to the world’s rice supply, particularly in areas like Tamil Nadu, India. In this study, weather-based models were developed based on count time series and machine learning techniques like INGARCHX, [...] Read more.

Magnaporthe oryzae, the source of the rice blast, is a serious threat to the world’s rice supply, particularly in areas like Tamil Nadu, India. In this study, weather-based models were developed based on count time series and machine learning techniques like INGARCHX, Artificial Neural Networks (ANNs), and Support Vector Regression (SVR), to forecast the incidence of rice blast disease. Between 2015 and 2023, information on rice blast occurrence was gathered weekly from three locations (Thanjavur, Tirunelveli, and Coimbatore), together with relevant meteorological data like temperature, humidity, rainfall, sunshine, evaporation, and sun radiation. The associations between the occurrence of rice blast and environmental factors were investigated using stepwise regression analysis, descriptive statistics, and correlation. Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) were used to assess the model’s prediction ability. The best prediction accuracy was given by the ANN, which outperformed SVR and INGARCHX in every location, according to the results. The complicated and non-linear relationships between meteorological variables and disease incidence were well-represented by the ANN model. The Diebold–Mariano test further demonstrated that ANNs are more predictive than other models. This work shows how machine learning algorithms can improve the prediction of rice blast, offering vital information for early disease management. The application of these models can help farmers make timely decisions to minimize crop losses. The findings suggest that machine learning models offer promising potential for accurate disease forecasting and improved rice management. Full article

(This article belongs to the Special Issue Application of Artificial Neural Network in Agriculture)

► Show Figures

Figure 1

20 pages, 6846 KiB

Open AccessArticle

Nutritional Monitoring of Rhodena Lettuce via Neural Networks and Point Cloud Analysis

by Alfonso Ramírez-Pedraza, Sebastián Salazar-Colores, Juan Terven, Julio-Alejandro Romero-González, José-Joel González-Barbosa and Diana-Margarita Córdova-Esparza

AgriEngineering 2024, 6(3), 3474-3493; https://doi.org/10.3390/agriengineering6030198 - 23 Sep 2024

Cited by 1 | Viewed by 1075

Abstract

In traditional farming, fertilizers are often used without precision, resulting in unnecessary expenses and potential damage to the environment. This study introduces a new method for accurately identifying macronutrient deficiencies in Rhodena lettuce crops. We have developed a four-stage process. First, we gathered [...] Read more.

In traditional farming, fertilizers are often used without precision, resulting in unnecessary expenses and potential damage to the environment. This study introduces a new method for accurately identifying macronutrient deficiencies in Rhodena lettuce crops. We have developed a four-stage process. First, we gathered two sets of data for lettuce seedlings: one is composed of color images and the other of point clouds. In the second stage, we employed the interactive closest point (ICP) method to align the point clouds and extract 3D morphology features for detecting nitrogen deficiencies using machine learning techniques. Next, we trained and compared multiple detection models to identify potassium deficiencies. Finally, we compared the outcomes with traditional lab tests and expert analysis. Our results show that the decision tree classifier achieved 90.87% accuracy in detecting nitrogen deficiencies, while YOLOv9c attained an mAP of 0.79 for identifying potassium deficiencies. This innovative approach has the potential to transform how we monitor and manage crop nutrition in agriculture. Full article

(This article belongs to the Special Issue Application of Artificial Neural Network in Agriculture)

► Show Figures

Figure 1

11 pages, 2090 KiB

Open AccessArticle

Performance of Neural Networks in the Prediction of Nitrogen Nutrition in Strawberry Plants

by Jamile Raquel Regazzo, Thiago Lima da Silva, Marcos Silva Tavares, Edson José de Souza Sardinha, Caroline Goulart Figueiredo, Júlia Luna Couto, Tamara Maria Gomes, Adriano Rogério Bruno Tech and Murilo Mesquita Baesso

AgriEngineering 2024, 6(2), 1760-1770; https://doi.org/10.3390/agriengineering6020102 - 18 Jun 2024

Viewed by 1265

Abstract

Among the technological tools used in precision agriculture, the convolutional neural network (CNN) has shown promise in determining the nutritional status of plants, reducing the time required to obtain results and optimizing the variable application rates of fertilizers. Not knowing the appropriate amount [...] Read more.

Among the technological tools used in precision agriculture, the convolutional neural network (CNN) has shown promise in determining the nutritional status of plants, reducing the time required to obtain results and optimizing the variable application rates of fertilizers. Not knowing the appropriate amount of nitrogen to apply can cause environmental damage and increase production costs; thus, technological tools are required that identify the plant’s real nutritional demands, and that are subject to evaluation and improvement, considering the variability of agricultural environments. The objective of this study was to evaluate and compare the performance of two convolutional neural networks in classifying leaf nitrogen in strawberry plants by using RGB images. The experiment was carried out in randomized blocks with three treatments (T1: 50%, T2: 100%, and T3: 150% of recommended nitrogen fertilization), two plots and five replications. The leaves were collected in the phenological phase of floral induction and digitized on a flatbed scanner; this was followed by processing and analysis of the models. ResNet-50 proved to be superior compared to the personalized CNN, achieving accuracy rates of 78% and 48% and AUC of 76%, respectively, increasing classification accuracy by 38.5%. The importance of this technique in different cultures and environments is highlighted to consolidate this approach. Full article

(This article belongs to the Special Issue Application of Artificial Neural Network in Agriculture)

► Show Figures

Figure 1

17 pages, 7466 KiB

Open AccessArticle

A Performance Comparison of CNN Models for Bean Phenology Classification Using Transfer Learning Techniques

by Teodoro Ibarra-Pérez, Ramón Jaramillo-Martínez, Hans C. Correa-Aguado, Christophe Ndjatchi, Ma. del Rosario Martínez-Blanco, Héctor A. Guerrero-Osuna, Flabio D. Mirelez-Delgado, José I. Casas-Flores, Rafael Reveles-Martínez and Umanel A. Hernández-González

AgriEngineering 2024, 6(1), 841-857; https://doi.org/10.3390/agriengineering6010048 - 18 Mar 2024

Cited by 6 | Viewed by 1745

Abstract

The early and precise identification of the different phenological stages of the bean (Phaseolus vulgaris L.) allows for the determination of critical and timely moments for the implementation of certain agricultural activities that contribute in a significant manner to the output and [...] Read more.

The early and precise identification of the different phenological stages of the bean (Phaseolus vulgaris L.) allows for the determination of critical and timely moments for the implementation of certain agricultural activities that contribute in a significant manner to the output and quality of the harvest, as well as the necessary actions to prevent and control possible damage caused by plagues and diseases. Overall, the standard procedure for phenological identification is conducted by the farmer. This can lead to the possibility of overlooking important findings during the phenological development of the plant, which could result in the appearance of plagues and diseases. In recent years, deep learning (DL) methods have been used to analyze crop behavior and minimize risk in agricultural decision making. One of the most used DL methods in image processing is the convolutional neural network (CNN) due to its high capacity for learning relevant features and recognizing objects in images. In this article, a transfer learning approach and a data augmentation method were applied. A station equipped with RGB cameras was used to gather data from images during the complete phenological cycle of the bean. The information gathered was used to create a set of data to evaluate the performance of each of the four proposed network models: AlexNet, VGG19, SqueezeNet, and GoogleNet. The metrics used were accuracy, precision, sensitivity, specificity, and F1-Score. The results of the best architecture obtained in the validation were those of GoogleNet, which obtained 96.71% accuracy, 96.81% precision, 95.77% sensitivity, 98.73% specificity, and 96.25% F1-Score. Full article

(This article belongs to the Special Issue Application of Artificial Neural Network in Agriculture)

► Show Figures

Figure 1

21 pages, 13276 KiB

Open AccessArticle

Enhanced Deep Learning Architecture for Rapid and Accurate Tomato Plant Disease Diagnosis

by Shahab Ul Islam, Shahab Zaib, Giampaolo Ferraioli, Vito Pascazio, Gilda Schirinzi and Ghassan Husnain

AgriEngineering 2024, 6(1), 375-395; https://doi.org/10.3390/agriengineering6010023 - 12 Feb 2024

Cited by 3 | Viewed by 1908

Abstract

Deep neural networks have demonstrated outstanding performances in agriculture production. Agriculture production is one of the most important sectors because it has a direct impact on the economy and social life of any society. Plant disease identification is a big challenge for agriculture [...] Read more.

Deep neural networks have demonstrated outstanding performances in agriculture production. Agriculture production is one of the most important sectors because it has a direct impact on the economy and social life of any society. Plant disease identification is a big challenge for agriculture production, for which we need a fast and accurate technique to identify plant disease. With the recent advancement in deep learning, we can develop a robust and accurate system. This research investigated the use of deep learning for accurate and fast tomato plant disease identification. In this research, we have used individual and merged datasets of tomato plants with 10 diseases (including healthy plants). The main aim of this work is to check the accuracy of the existing convolutional neural network models such as Visual Geometry Group, Residual Net, and DenseNet on tomato plant disease detection and then design a custom deep neural network model to give the best accuracy in case of the tomato plant. We have trained and tested our models with datasets containing over 18,000 and 25,000 images with 10 classes. We achieved over 99% accuracy with our custom model. This high accuracy was achieved with less training time and lower computational cost compared to other CNNs. This research demonstrates the potential of deep learning for efficient and accurate tomato plant disease detection, which can benefit farmers and contribute to improved agricultural production. The custom model’s efficient performance makes it promising for practical implementation in real-world agricultural settings. Full article

(This article belongs to the Special Issue Application of Artificial Neural Network in Agriculture)

► Show Figures

Figure 1

16 pages, 16871 KiB

Open AccessArticle

Alpha-EIOU-YOLOv8: An Improved Algorithm for Rice Leaf Disease Detection

by Dong Cong Trinh, Anh Tuan Mac, Khanh Giap Dang, Huong Thanh Nguyen, Hoc Thai Nguyen and Thanh Dang Bui

AgriEngineering 2024, 6(1), 302-317; https://doi.org/10.3390/agriengineering6010018 - 4 Feb 2024

Cited by 15 | Viewed by 4500

Abstract

Early detection of plant leaf diseases is a major necessity for controlling the spread of infections and enhancing the quality of food crops. Recently, plant disease detection based on deep learning approaches has achieved better performance than current state-of-the-art methods. Hence, this paper [...] Read more.

Early detection of plant leaf diseases is a major necessity for controlling the spread of infections and enhancing the quality of food crops. Recently, plant disease detection based on deep learning approaches has achieved better performance than current state-of-the-art methods. Hence, this paper utilized a convolutional neural network (CNN) to improve rice leaf disease detection efficiency. We present a modified YOLOv8, which replaces the original Box Loss function by our proposed combination of EIoU loss and

α

-IoU loss in order to improve the performance of the rice leaf disease detection system. A two-stage approach is proposed to achieve a high accuracy of rice leaf disease identification based on AI (artificial intelligence) algorithms. In the first stage, the images of rice leaf diseases in the field are automatically collected. Afterward, these image data are separated into blast leaf, leaf folder, and brown spot sets, respectively. In the second stage, after training the YOLOv8 model on our proposed image dataset, the trained model is deployed on IoT devices to detect and identify rice leaf diseases. In order to assess the performance of the proposed approach, a comparative study between our proposed method and the methods using YOLOv7 and YOLOv5 is conducted. The experimental results demonstrate that the accuracy of our proposed model in this research has reached up to 89.9% on the dataset of 3175 images with 2608 images for training, 326 images for validation, and 241 images for testing. It demonstrates that our proposed approach achieves a higher accuracy rate than existing approaches. Full article

(This article belongs to the Special Issue Application of Artificial Neural Network in Agriculture)

► Show Figures

Graphical abstract

20 pages, 2985 KiB

Open AccessArticle

Predicting the Power Requirement of Agricultural Machinery Using ANN and Regression Models and the Optimization of Parameters Using an ANN–PSO Technique

by Ganesh Upadhyay, Neeraj Kumar, Hifjur Raheman and Rashmi Dubey

AgriEngineering 2024, 6(1), 185-204; https://doi.org/10.3390/agriengineering6010012 - 18 Jan 2024

Cited by 1 | Viewed by 1207

Abstract

Optimizing the design and operational parameters for tillage tools is crucial for improved performance. Recently, artificial intelligence approaches, like ANN with learning capabilities, have gained attention for cost-effective and timely problem solving. Soil-bin experiments were conducted and data were used to develop ANN [...] Read more.

Optimizing the design and operational parameters for tillage tools is crucial for improved performance. Recently, artificial intelligence approaches, like ANN with learning capabilities, have gained attention for cost-effective and timely problem solving. Soil-bin experiments were conducted and data were used to develop ANN and regression models using gang angle, velocity ratio, soil CI, and depth as input parameters, while tractor equivalent PTO (PTO_eq) power was used as an output. Both models were trained with a randomly selected 90% of the data, reserving 10% for testing purposes. In regression, models were iteratively fitted using nonlinear least-squares optimization. The ANN model utilized a multilayer feed-forward network with a backpropagation algorithm. The comparative performance of both models was evaluated in terms of R² and mean square error (MSE). The ANN model outperformed the regression model in the training, testing, and validation phases. A well-trained ANN model was integrated with the particle-swarm optimization (PSO) technique for optimization of the operational parameters. The optimized configuration featured a 36.6° gang angle, 0.50 MPa CI, 100 mm depth, and 3.90 velocity ratio for a predicted tractor PTO_eq power of 3.36 kW against an actual value of 3.45 kW. ANN–PSO predicted the optimal parameters with a variation between the predicted and the actual tractor PTO_eq power within ±6.85%. Full article

(This article belongs to the Special Issue Application of Artificial Neural Network in Agriculture)

► Show Figures

Figure 1

16 pages, 3660 KiB

Open AccessArticle

Dynamic Behavior Forecast of an Experimental Indirect Solar Dryer Using an Artificial Neural Network

by Angel Tlatelpa Becerro, Ramiro Rico Martínez, Erick César López-Vidaña, Esteban Montiel Palacios, César Torres Segundo and José Luis Gadea Pacheco

AgriEngineering 2023, 5(4), 2423-2438; https://doi.org/10.3390/agriengineering5040149 - 14 Dec 2023

Cited by 1 | Viewed by 1622

Abstract

This research presents the prediction of temperatures in the chamber of a solar dryer using artificial neural networks (ANN). The dryer is a forced-flow type and indirect. Climatic conditions, temperatures, airflow, and geometric parameters were considered to build the ANN model. The model [...] Read more.

This research presents the prediction of temperatures in the chamber of a solar dryer using artificial neural networks (ANN). The dryer is a forced-flow type and indirect. Climatic conditions, temperatures, airflow, and geometric parameters were considered to build the ANN model. The model was a feed-forward network trained using a backpropagation algorithm and Levenberg–Marquardt optimization. The configuration of the optimal neural network to carry out the verification and validation processes was nine neurons in the input layer, one in the output layer, and two hidden layers of thirteen and twelve neurons each (9-13-12-1). The percentage error of the predictive model was below 1%. The predictive model has been successfully tested, achieving a predictor with good capabilities. This consistency is reflected in the relative error between the predicted and experimental temperatures. The error is below 0.25% for the model’s verification and validation. Moreover, this model could be the basis for developing a powerful real-time operation optimization tool and the optimal design for indirect solar dryers to reduce cost and time in food-drying processes. Full article

(This article belongs to the Special Issue Application of Artificial Neural Network in Agriculture)

► Show Figures

Figure 1

14 pages, 661 KiB

Open AccessArticle

A Transfer Learning-Based Deep Convolutional Neural Network for Detection of Fusarium Wilt in Banana Crops

by Kevin Yan, Md Kamran Chowdhury Shisher and Yin Sun

AgriEngineering 2023, 5(4), 2381-2394; https://doi.org/10.3390/agriengineering5040146 - 11 Dec 2023

Cited by 6 | Viewed by 2304

Abstract

During the 1950s, the Gros Michel species of bananas were nearly wiped out by the incurable Fusarium Wilt, also known as Panama Disease. Originating in Southeast Asia, Fusarium Wilt is a banana pandemic that has been threatening the multi-billion-dollar banana industry worldwide. The [...] Read more.

During the 1950s, the Gros Michel species of bananas were nearly wiped out by the incurable Fusarium Wilt, also known as Panama Disease. Originating in Southeast Asia, Fusarium Wilt is a banana pandemic that has been threatening the multi-billion-dollar banana industry worldwide. The disease is caused by a fungus that spreads rapidly throughout the soil and into the roots of banana plants. Currently, the only way to stop the spread of this disease is for farmers to manually inspect and remove infected plants as quickly as possible, which is a time-consuming process. The main purpose of this study is to build a deep Convolutional Neural Network (CNN) using a transfer learning approach to rapidly identify Fusarium wilt infections on banana crop leaves. We chose to use the ResNet50 architecture as the base CNN model for our transfer learning approach owing to its remarkable performance in image classification, which was demonstrated through its victory in the ImageNet competition. After its initial training and fine-tuning on a data set consisting of 600 healthy and diseased images, the CNN model achieved near-perfect accuracy of 0.99 along with a loss of 0.46 and was fine-tuned to adapt the ResNet base model. ResNet50’s distinctive residual block structure could be the reason behind these results. To evaluate this CNN model, 500 test images, consisting of 250 diseased and healthy banana leaf images, were classified by the model. The deep CNN model was able to achieve an accuracy of 0.98 and an F-1 score of 0.98 by correctly identifying the class of 492 of the 500 images. These results show that this DCNN model outperforms existing models such as Sangeetha et al., 2023’s deep CNN model by at least 0.07 in accuracy and is a viable option for identifying Fusarium Wilt in banana crops. Full article

(This article belongs to the Special Issue Application of Artificial Neural Network in Agriculture)

► Show Figures

Figure 1

15 pages, 2049 KiB

Open AccessArticle

A Convolutional Neural Network Algorithm for Pest Detection Using GoogleNet

by Intan Nurma Yulita, Muhamad Farid Ridho Rambe, Asep Sholahuddin and Anton Satria Prabuwono

AgriEngineering 2023, 5(4), 2366-2380; https://doi.org/10.3390/agriengineering5040145 - 8 Dec 2023

Cited by 6 | Viewed by 3863

Abstract

The primary strategy for mitigating lost productivity entails promptly, accurately, and efficiently detecting plant pests. Although detection by humans can be useful in detecting certain pests, it is often slower compared to automated methods, such as machine learning. Hence, this study employs a [...] Read more.

The primary strategy for mitigating lost productivity entails promptly, accurately, and efficiently detecting plant pests. Although detection by humans can be useful in detecting certain pests, it is often slower compared to automated methods, such as machine learning. Hence, this study employs a Convolutional Neural Network (CNN) model, specifically GoogleNet, to detect pests within mobile applications. The technique of detection involves the input of images depicting plant pests, which are subsequently subjected to further processing. This study employed many experimental methods to determine the most effective model. The model exhibiting a 93.78% accuracy stands out as the most superior model within the scope of this investigation. The aforementioned model has been included in a smartphone application with the purpose of facilitating Indonesian farmers in the identification of pests affecting their crops. The implementation of an Indonesian language application is a contribution to this research. Using this local language makes it easier for Indonesian farmers to use it. The potential impact of this application on Indonesian farmers is anticipated to be significant. By enhancing pest identification capabilities, farmers may employ more suitable pest management strategies, leading to improved crop yields in the long run. Full article

(This article belongs to the Special Issue Application of Artificial Neural Network in Agriculture)

► Show Figures

Figure 1

14 pages, 2254 KiB

Open AccessArticle

Artificial Neural Networks for Modeling and Optimizing Egg Cost in Second-Cycle Laying Hens Based on Dietary Intakes of Essential Amino Acids

by Walter Morales-Suárez, Luis Daniel Daza and Henry A. Váquiro

AgriEngineering 2023, 5(4), 1832-1845; https://doi.org/10.3390/agriengineering5040112 - 12 Oct 2023

Viewed by 1300

Abstract

Egg production is a significant source of animal protein for human consumption. Feed costs significantly impact the profitability of egg production, representing more than 70% of the variable costs. This study evaluated the effect of dietary intakes of three essential amino acids (EAAs) [...] Read more.

Egg production is a significant source of animal protein for human consumption. Feed costs significantly impact the profitability of egg production, representing more than 70% of the variable costs. This study evaluated the effect of dietary intakes of three essential amino acids (EAAs) on the egg cost for H&N Brown second-cycle laying hens. The hens were fed for 20 weeks with 23 diets that varied in their lysine, methionine + cystine, and threonine contents. These amino acids were derived from both dietary and synthetic sources. Zootechnical results were used to calculate the feed cost per kilogram of egg (FCK), considering the cost of raw materials and the diet composition. Multivariate polynomial models and artificial neural networks (ANNs) were validated to predict FCK as a function of the EAAs and time. The EAA intakes that minimize FCK over time were optimized using the best model, a cascade-forward ANN with a softmax transfer function. The optimal scenario for FCK (0.873 USD/kg egg) at 20 weeks was achieved at 943.7 mg lysine/hen-day, 858.3 mg methionine + cystine/hen-day, and 876.8 mg threonine/hen-day. ANNs could be a valuable tool for predicting the egg cost of laying hens based on the nutritional requirements. This could help improve economic efficiency and reduce the feed costs in poultry companies. Full article

(This article belongs to the Special Issue Application of Artificial Neural Network in Agriculture)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Application of Artificial Neural Network in Agriculture

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (14 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI