Information Processing in Agriculture最新文献

{"title":"An automatic method for estimating insect defoliation with visual highlights of consumed leaf tissue regions","authors":"Gabriel S. Vieira ,&nbsp;Afonso U. Fonseca ,&nbsp;Naiane Maria de Sousa ,&nbsp;Julio C. Ferreira ,&nbsp;Juliana Paula Felix ,&nbsp;Christian Dias Cabacinha ,&nbsp;Fabrizzio Soares","doi":"10.1016/j.inpa.2024.03.001","DOIUrl":"10.1016/j.inpa.2024.03.001","url":null,"abstract":"<div><div>As an essential component of the architecture of a plant, leaves are crucial to sustaining decision-making in cultivars and effectively support agricultural processes. When the leaf area is constantly monitored, a plant’s health and productive capacity can be assessed to foment proactive and reactive strategies. Because of that, one of the most critical tasks in agricultural processes is estimating foliar damage. In this sense, we present an automatic method to estimate leaf stress caused by insect herbivory, including damage in border regions. As a novelty, we present a method with well-defined processing steps suitable for numerical analysis and visual inspection of defoliation severity. We describe the proposed method and evaluate its performance concerning 12 different plant species. Experimental results show high assertiveness in estimating leaf area loss with a concordance correlation coefficient of 0.98 for grape, soybean, potato, and strawberry leaves. A classic pattern recognition approach, named template matching, is at the core of the method whose performance is compared to cutting-edge techniques. Results demonstrated that the method achieves foliar damage quantification with precision comparable to deep learning models. The code prepared by the authors is publicly available.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 1","pages":"Pages 40-53"},"PeriodicalIF":7.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140084130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Technologies, Protocols, and applications of Internet of Things in greenhouse Farming: A survey of recent advances","authors":"Khalid M. Hosny ,&nbsp;Walaa M. El-Hady ,&nbsp;Farid M. Samy","doi":"10.1016/j.inpa.2024.04.002","DOIUrl":"10.1016/j.inpa.2024.04.002","url":null,"abstract":"<div><div>Greenhouse farming is considered one of the precision and sustainable forms of smart agriculture. Although greenhouse gases can support off-season crops inside the indoor environment, monitoring, controlling, and managing crop parameters at greenhouse farms more precisely and securely is necessary, even in harsh climate regions. The evolving Internet of Things (IoT) technologies, including smart sensors, devices, network topologies, big data analytics, and intelligent decision-making, are thought to be the solution for automating greenhouse farming parameters like internal atmosphere control, irrigation control, crop growth monitoring, and so on. This paper introduces a comprehensive survey of recent advances in IoT-based greenhouse farming. We summarize the related review articles. The classification of greenhouse farming based on IoT (smart greenhouse, hydroponics greenhouse, and vertical farming) is introduced. Also, we present a detailed architecture for the components of greenhouse agriculture applications based on IoT, including physical devices, communication protocols, and cloud/fog computing technologies. We also present a classification of IoT applications of greenhouse farming, including monitoring, controlling, tracking, and predicting. Furthermore, we present the technical and resource management challenges for optimal greenhouse farming. Moreover, countries already applying IoT in greenhouse farming have been presented. Lastly, future suggestions related to IoT-based greenhouse farming have been introduced.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 1","pages":"Pages 91-111"},"PeriodicalIF":7.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140773497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A deep learning framework for prediction of crop yield in Australia under the impact of climate change","authors":"Haydar Demirhan","doi":"10.1016/j.inpa.2024.04.004","DOIUrl":"10.1016/j.inpa.2024.04.004","url":null,"abstract":"<div><div>Accurate prediction of crop yields is essential to ensure food security. In this study, a new deep neural networks framework is developed to predict crop yields in Australia, considering the impact of climate change, fertilizer use, and crop area. It is implemented for oats, corn, rice, and wheat crops, and its forecasting performance is benchmarked against five statistical and machine learning methods. All the software codes for the implementation of the proposed framework are freely available. The proposed framework shows the highest forecasting performance for all the considered crop types. It provides 23%, 38%, 39%, and 40% lower average mean absolute error than the benchmark methods for oat, corn, rice, and wheat crops, respectively. The reductions in average root mean squared error are 19%, 25%, 37%, and 29% over the benchmark methods. Then, it is used to predict yields of the considered crops in Australia towards 2025 under six different climate change scenarios. It is observed that although climate change has some boosting impact on crop yield, it is not sustainable to meet the demand. However, it is possible to keep crop yields rising while mitigating climate change.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 1","pages":"Pages 125-138"},"PeriodicalIF":7.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140761172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Society 5.0 enabled agriculture: Drivers, enabling technologies, architectures, opportunities, and challenges","authors":"Kossi Dodzi Bissadu,&nbsp;Salleh Sonko,&nbsp;Gahangir Hossain","doi":"10.1016/j.inpa.2024.04.003","DOIUrl":"10.1016/j.inpa.2024.04.003","url":null,"abstract":"<div><div>The existing agriculture practices faced many challenges and fail to address some of the most critical needs of the growing population. Food insecurity, high initial cost of smart farming, severe farm labor shortage worldwide, economic, social, and political crises related to famines, poverty, climate change, and the technology focus of Agriculture 4.0 calls for rethinking the agriculture paradigm. Moreover, the idea of Society 5.0 promoted by Japanese government triggered many position reactions from policymakers, governments, private institutions, academicians, and researchers. The idea of human centered society where individuals live their lives to the fullest with shared vision of happiness, social harmony, sustainability, and resilience recently caught scholars’ attention. Several researchers investigated the society 5.0 and its critical components including Agriculture 5.0. Agriculture 5.0 not only could be leveraged to address many existing issues, but could become a major driving force for achieving Society 5.0’s goals. This paper follows a systematic literature review approach to investigate the major drivers, enabling cutting-edge technologies, various opportunities and challenges for developing, adopting, and implementation Agriculture 5.0. It also highlighted the overall and holistic architectural framework based on 12 layers of Agriculture 5.0 paradigm. Though Agriculture 5.0 is promising with many opportunities, such as creating new job opportunities for young generations, and boosting mass customization, it will face many potential challenges. Some challenges include cybersecurity and privacy issues, difficulties for an effective legal, regulatory and compliance system due to high automation and mass personalization, standardization issues, and adapting agricultural production strategies and models to constantly changing customer preferences.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 1","pages":"Pages 112-124"},"PeriodicalIF":7.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140777764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Few-shot cow identification via meta-learning","authors":"Xingshi Xu,&nbsp;Yunfei Wang,&nbsp;Yuying Shang,&nbsp;Guangyuan Yang,&nbsp;Zhixin Hua,&nbsp;Zheng Wang,&nbsp;Huaibo Song","doi":"10.1016/j.inpa.2024.04.001","DOIUrl":"10.1016/j.inpa.2024.04.001","url":null,"abstract":"<div><div>Cow identification is a prerequisite for precision livestock farming. Biometric-based methods have made significant progress in cow identification. However, substantial labelling costs and frequent identification task changes are still hamper model application. In this work, a novel method called “MFCI” was proposed to achieve accurate cow identification under few-shot and task-changing conditions. Specifically, the proposed method comprises two components: cow location and cow identification. First, an improved YOLOv5n with Ghost module was adopted to quickly detect cow locations in images. Then, the Model-Agnostic Meta-Learning (MAML) framework was introduced for accurate identification under few-shot conditions and for fast adaptation to frequent changes in individual cows. Moreover, an autoencoder was adopted to allow Base-Learner learn more generalized features by combining both supervised and unsupervised approaches. The experimental results showed that the proposed cow location model achieved a mAP of 99.5 %. The proposed cow identification model attained an accuracy of 90.43 % with only five samples per cow for 20 cows, outperforming other state-of-the-art methods. The results demonstrate the broad applicability and significant value of the proposed method.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 1","pages":"Pages 80-90"},"PeriodicalIF":7.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140767863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Supervised and unsupervised machine learning approaches for prediction and geographical discrimination of Iranian saffron ecotypes based on flower-related and phytochemical attributes","authors":"Seid Mohammad Alavi-Siney ,&nbsp;Jalal Saba ,&nbsp;Alireza Fotuhi Siahpirani ,&nbsp;Jaber Nasiri","doi":"10.1016/j.inpa.2023.12.002","DOIUrl":"10.1016/j.inpa.2023.12.002","url":null,"abstract":"<div><div>A two-year field experiment (2014–2016; Zanjan, Iran) was conducted to monitor potential diversity pattern and adaptability power among 18 Iranian saffron ecotypes under Zanjan climatological conditions using seven flower-related and three qualitative traits (crocin, picrocrocin, and safranal, determined by UV–visible spectra), and analyzed by supervised and unsupervised approaches. A range of variability was recorded among the ecotypes, and despite some exceptions, overall, saffron corms produced higher amounts of studied features across the second year. The Feizabad ecotype was recommended to acquire maximum qualitative criteria (category I; based on ISO Normative 3632 grading system), while for flower-related parameters several ecotypes (e.g., Ghaien, Bardeskan, Torbat-Jam, and Gonabad) could be applied for Zanjan climatological conditions. Based on the results of Leave-One-Out Cross-Validation (LOOCV), various prediction values were computed for all 10 classifiers of LDA, QDA, FDA, MDA, RDA, Naive Bayes, Decision Tree, Linear SVM, Radial SVM, and Random Forest in terms of accuracy, sensitivity and specificity parameters. Among which, Random Forest and LDA with the values of 0.91 and 0.78 possessed the highest and the lowest amounts of accuracy, respectively. Finally, considering the highest accuracy value of the superior classification model of Random forest, both feature subsets of “FFW, FDW, Picrocrocin, Safranal, and Crocin” and “SFW, FDW, Picrocrocin, Safranal, and Crocin” were nominated as the most powerful elements (comparing to the remaining 1021 feature subsets) to make accurate discrimination between Khorasan and non-Khorasan saffron ecotypes. The results, overall, revealed that saffron ecotypes followed different responses under Zanjan climatological circumstances, and Random Forest is more suitable for accurately predicting saffron corms from different provenances.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 1","pages":"Pages 1-16"},"PeriodicalIF":7.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138609531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Innovative deep learning approach for cross-crop plant disease detection: A generalized method for identifying unhealthy leaves","authors":"Imane Bouacida ,&nbsp;Brahim Farou ,&nbsp;Lynda Djakhdjakha ,&nbsp;Hamid Seridi ,&nbsp;Muhammet Kurulay","doi":"10.1016/j.inpa.2024.03.002","DOIUrl":"10.1016/j.inpa.2024.03.002","url":null,"abstract":"<div><div>One of the most serious threats to global food security is plant diseases compromising agricultural productivity and threatening the livelihoods of millions. These diseases can decimate crops, disrupt food supply chains, and escalate the risk of food shortages, underscoring the urgency of implementing robust strategies to safeguard the world’s food sources. Deep learning methods have revolutionized the field of plant disease detection, offering advanced and accurate solutions for early identification and management. However, a recurring problem in deep learning models is their susceptibility to a lack of robustness and generalization when facing novel crop and disease types that were not included in the training dataset. In this paper, we address this issue by proposing a novel deep learning-based system capable of recognizing diseased and healthy leaves across different crops, even if the system was not trained on them. The key idea is to focus on recognizing the diseased small leaf regions rather than the overall appearance of the diseased leaf, along with determining the disease’s prevalence rate on the entire leaf. For efficient classification and to leverage the excellence of the Inception model in disease recognition, we employ a small Inception model architecture, which is suitable for processing small regions without compromising performance. To confirm the effectiveness of our method, we trained and tested it using the widely acclaimed PlantVillage dataset, recognized as the most utilized dataset for its comprehensive and diverse coverage. Our method achieved an accuracy rate of 94.04%. Furthermore, when tested on new datasets, it achieved an accuracy rate of 97.13%. This innovative approach not only enhances the accuracy of plant disease detection but also addresses the critical challenge of model generalization to diverse crops and diseases. In addition, it outperformed the existing methods in its ability to identify any disease across any crop type, showcasing its potential for broad applicability and contribution to global food security initiatives.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 1","pages":"Pages 54-67"},"PeriodicalIF":7.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140085028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving crop image recognition performance using pseudolabels","authors":"Pengfei Deng,&nbsp;Zhaohui Jiang,&nbsp;Huimin Ma,&nbsp;Yuan Rao,&nbsp;Wu Zhang","doi":"10.1016/j.inpa.2024.02.001","DOIUrl":"10.1016/j.inpa.2024.02.001","url":null,"abstract":"<div><div>In crop image recognition, when faced with a large quantity of unlabeled data, the traditional manual labeling method requires a large amount of human and material resources. To solve this problem, this study proposes an image recognition method based on a pseudolabeling technique. First, the data are divided into labeled and unlabeled data. The initial network model is trained on labeled data. Then, pseudolabeling of the unlabeled data is predicted, and only the data that satisfy the confidence threshold are regarded as valid pseudolabeling. To convert the unlabeled data into supervised training data, the two types of data are mixed. The training is terminated when the number of remaining unlabeled data satisfies the end condition and when the fivefold cross-validation method is used to evaluate model performance. Compared with the traditional semisupervised method, the experimental method is simpler and more applicable. Experiments were conducted on rice growth stage recognition and crop weed seedling recognition tasks. The results showed that the proposed method achieved 99.17% accuracy in rice growth stage recognition and a high AUC value of 99.93% in crop weed seedling recognition, which demonstrated excellent performance. Compared with the traditional model, this method not only improves in accuracy but also has better stability and wider applicability and is expected to provide an efficient, accurate and scalable solution for crop image recognition.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 1","pages":"Pages 17-26"},"PeriodicalIF":7.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Security analysis of agricultural energy internet considering electricity load control for dragon fruit cultivation","authors":"Xueqian Fu ,&nbsp;Lingxi Ma ,&nbsp;Huaichang Ge ,&nbsp;Jiahui Zhang","doi":"10.1016/j.inpa.2024.02.002","DOIUrl":"10.1016/j.inpa.2024.02.002","url":null,"abstract":"<div><div>With the increasing emphasis on sustainable energy and the advancements in modern agriculture, flexible agricultural power loads present challenges to the reliable operation of the agricultural energy internet. However, research on the coupling of energy system security with agricultural security is insufficient and fails to consider the impacts of meteorological elements on agricultural power loads. To address these gaps, this paper establishes load models for irrigation and light supplementation based on the actual cultivation demands of winter dragon fruit in Guangxi province. A static security index system is developed to analyze the security, considering the unique features of agricultural power demands. The condition of the distribution network is assessed by comparing the indexes with predefined limits, using a China 41-bus distribution network. Finally, the optimal scheme for nocturnal supplemental lighting treatment and irrigation is determined based on the method for maintaining secure operation of the distribution network. This study serves as a guide for simulating current farming power loads and demonstrates how security analysis of the agricultural energy internet contributes to the large-scale and sophisticated development of modern agriculture.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 1","pages":"Pages 27-39"},"PeriodicalIF":7.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140466733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A severity estimation method for lightweight cucumber leaf disease based on DM-BiSeNet","authors":"Kaiyu Li ,&nbsp;Yuzhaobi Song ,&nbsp;Xinyi Zhu ,&nbsp;Lingxian Zhang","doi":"10.1016/j.inpa.2024.03.003","DOIUrl":"10.1016/j.inpa.2024.03.003","url":null,"abstract":"<div><div>Accurately estimating the severity of cucumber diseases is crucial for improving cucumber quality and minimizing economic losses. Deep learning techniques have shown promising results in automatically extracting disease image features for severity estimation. However, existing methods still face challenges in accurately estimating disease severity under complex backgrounds and achieving real-time performance.This paper presents a lightweight severity estimation method called DM-BiSeNet to address these challenges. The proposed method utilizes BiSeNet V2 as the base network and incorporates depthwise separable convolutional blocks to optimize the detail branch. A simplified MobileNet V3 network is also constructed to optimize the semantic branch. The model training process is accelerated using the AdamW optimizer. To evaluate the performance of DM-BiSeNet, a dataset consisting of cucumber powdery mildew and downy mildew disease images collected in natural scenes is utilized. Experimental results demonstrate that DM-BiSeNet achieves higher accuracy in severity estimation, with an <em>R²</em> value of 0.9407 and an RMSE of 1.0680, outperforming the comparison methods. Moreover, DM-BiSeNet exhibits a complexity of 1.54 GFLOPs and is capable of reasoning 94 disease images per second.The proposed DM-BiSeNet model offers a lightweight and effective solution for accurate and rapid severity estimation of cucumber diseases under complex backgrounds. It provides a valuable technical tool for quantitative disease estimation, offering significant potential for practical applications.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 1","pages":"Pages 68-79"},"PeriodicalIF":7.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}