Information Processing in Agriculture最新文献

{"title":"Environmental assessment of industrial aquaponics in arid zones using an integrated dynamic model","authors":"Ze Zhu ,&nbsp;Uri Yogev ,&nbsp;Amit Gross ,&nbsp;Karel J. Keesman","doi":"10.1016/j.inpa.2024.09.005","DOIUrl":"10.1016/j.inpa.2024.09.005","url":null,"abstract":"<div><div>Land desertification, water scarcity, and food security challenges in arid zones are intensifying, driving the need for sustainable agricultural solutions like aquaponics. This study investigated innovative water and energy-saving strategies using an integrated dynamic model for an on-demand industrial aquaponics system in Israel. The model evaluated the performance of a recirculating aquaculture system (RAS), hydroponics system (HPS), and desalination unit (DU) by adjusting physical and operational parameters to optimize water and nutrient use efficiency, energy consumption, and yield. Optimizing the system design resulted in an aquaponics system with approximately 420 m³ RAS, 6.85 ha HPS and 40 m³/d DU, achieving phosphorus use efficiency of 96 %, a water use efficiency of 97 %, freshwater input of 1.5 L/day/m², and energy consumption of 0.56 kWh/day/m². To mitigate the challenges of extreme arid climates, evaporative cooling combined with outdoor shading and mechanical cooling was found to be a feasible option to control temperature and humidity in the greenhouse. Dehumidification technologies further improved system performance by recovering 22 % freshwater from seawater and increasing nitrogen use efficiency by 18 %. Achieving daily energy self-sufficiency required 4500 m² photovoltaic panels and 5000 m² solar heating system. While the system model was initially devised with a specific focus on conditions in Israel, it has been designed with scalability, allowing it to be adapted and applied extensively across diverse peri-urban regions and arid zones globally.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 2","pages":"Pages 260-277"},"PeriodicalIF":7.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning for rice leaf disease detection: A systematic literature review on emerging trends, methodologies and techniques","authors":"Chinna Gopi Simhadri ,&nbsp;Hari Kishan Kondaveeti ,&nbsp;Valli Kumari Vatsavayi ,&nbsp;Alakananda Mitra ,&nbsp;Preethi Ananthachari","doi":"10.1016/j.inpa.2024.04.006","DOIUrl":"10.1016/j.inpa.2024.04.006","url":null,"abstract":"<div><div>Rice is an essential food crop that is cultivated in many countries. Rice leaf diseases can cause significant damage to crop cultivation, leading to reduced yields and economic losses. Traditional disease detection approaches are often time-consuming, labor-intensive, and require expertise. Automatic leaf disease detection approaches help farmers detect diseases without or with less human interference. Most of the earlier studies on rice leaf disease detection depended on image processing and machine learning techniques. Image processing techniques are used to extract features from diseased leaf images, such as the color, texture, vein patterns, and shape of lesions. Machine learning techniques are used to detect diseases based on the extracted features. In contrast, deep learning techniques learn complex patterns from large datasets without explicit feature extraction techniques and are well-suited for disease detection tasks. This systematic review explores various deep learning approaches used in the literature for rice leaf disease detection, such as Transfer Learning, Ensemble Learning, and Hybrid approaches. This review also discusses the effectiveness of these approaches in addressing various challenges. This review discusses the details of various models and hyperparameter settings used, model fine-tuning techniques followed, and performance evaluation metrics utilized in various studies. This review also discusses the limitations of existing studies and presents future directions for further developing more robust and efficient rice leaf disease detection techniques.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 2","pages":"Pages 151-168"},"PeriodicalIF":7.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141038935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification and identification of pest, diseases and nutrient deficiency in paddy using layer based EMD phase features with decision tree","authors":"A. Pushpa Athisaya Sakila Rani ,&nbsp;N. Suresh Singh","doi":"10.1016/j.inpa.2024.09.003","DOIUrl":"10.1016/j.inpa.2024.09.003","url":null,"abstract":"<div><div>Pest attack, disease incidence, and nutrient deficiency are the major factors limiting the yield of paddy. Therefore, the paper proposes a classification system for the identification of pest, disease, and nutrient deficiency classes. This approach initially preprocesses leaf images using entropy filtering followed by a leaf segmentation process. Multiple layers are then constructed on the leaf image through which features are extracted. The Gray Level Co-occurrence Matrix (GLCM) algorithm and Principal Component Analysis (PCA) are used to extract the global texture features of the leaf image. A 1D-signal sequence is constructed on each layer, which is decomposed by the Empirical Mode Decomposition algorithm from which the phase features are estimated. The features are trained/classified using the decision tree classifiers that classify the pest attack, disease incidence, and nutrient deficiency categories. The proposed approach provides a precision, accuracy, specificity, sensitivity, and F1-score of 97 %, 97.88 %, 96.52 %, 96.7 %, and 96.7 % respectively.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 2","pages":"Pages 232-244"},"PeriodicalIF":7.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vine yield estimation from block to regional scale employing remote sensing, weather, and management data","authors":"Pedro C. Towers ,&nbsp;Sean E. Roulet ,&nbsp;Carlos Poblete-Echeverría","doi":"10.1016/j.inpa.2024.06.001","DOIUrl":"10.1016/j.inpa.2024.06.001","url":null,"abstract":"<div><div>Knowledge of the spatial variation in vine yield at different scales is crucial for the wine business, and combined with estimations of vine size variability enables within-block mapping of vegetative-reproductive balance. Remote sensing combined with other data that excludes field sampling appears as an optimal approach for yield estimation for a broad range of scales. In this study, mean yield and factors known to affect yield components were collected for over 8000 blocks, over 18 seasons, in the western oasis of Mendoza, Argentina. Partial Least Squares (PLS) and Random Forest (RF) models were used to analyse the relationship between these factors and yield. The PLS model delivered very poor results, with coefficients of determination lower than 0.08. RF models with 49 to 19 variables produced predictions with coefficients of determination of 0.96 to 0.90, respectively. Some factors traditionally considered important in yield determination, such as trellis, frost occurrence, or planting density had limited influence, whereas location weighed heavily. Results suggest a successful approach to spatial prediction of yield that requires no fieldwork and indicates VRB mapping at block-scale may be possible with these tools. Several improvements to inputs are proposed.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 2","pages":"Pages 195-208"},"PeriodicalIF":7.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"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":"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":"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":"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":"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}