Information Processing in Agriculture最新文献

{"title":"Fuzzy PID control system optimization and verification for oxygen-supplying management in live fish waterless transportation","authors":"Yongjun Zhang ,&nbsp;Xinqing Xiao","doi":"10.1016/j.inpa.2023.06.001","DOIUrl":"10.1016/j.inpa.2023.06.001","url":null,"abstract":"<div><div>Live fish waterless transportation could be recognized as an essential supplement for water-based transportation due to its low oxygen consumption and less waste water pollution. The critical problem to maintaining the fish survival quality under such a unique transport strategy is accurately controlling the oxygen concentration in the container to be constantly at stable and high levels. This paper aims to propose an improved fuzzy PID control system based on the grey model with residual rectification by improved particle swarm optimized Gated Recurrent Unit (GM-IPSO-GRU) to realize advanced oxygen level control. In addition, it is also reinforced by adopting the improved grey wolf optimization (IGWO) for the majorization of control parameters (quantization factors, scale factors) with full consideration of fish size features. In this study, Turbot (Scophthalmus maximus) is taken as the test subject to verify the integrated control performance of the optimized fuzzy PID controller through simulated waterless live transportation under low-temperature conditions. The proposed control system is validated as more efficient than the traditional proportional integral derivative (PID) and fuzzy PID algorithms for handling its nonlinear, time-varying, and time lag problems well. In summary, the control group experiment shows that the newly-designed control system has the advantages of shorter stabilization time, minor overshoot, and strong anti-interference ability for oxygen level adjustment. Finally, applying this novel control technology can effectively improve oxygen adjustment efficiency and provide feasible quality control support for the deep optimization of the live fish circulation industry.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 421-437"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42570678","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":"Fusion of RetinaFace and improved FaceNet for individual cow identification in natural scenes","authors":"Lingling Yang,&nbsp;Xingshi Xu,&nbsp;Jizheng Zhao,&nbsp;Huaibo Song","doi":"10.1016/j.inpa.2023.09.001","DOIUrl":"10.1016/j.inpa.2023.09.001","url":null,"abstract":"<div><div>Cows’ posture change is the fatal influencing factor for accurate identification of individual cows. To achieve non-contact, high-precision detection and identification of individual cows in farm environment, a cow individual identification method by the fusion of RetinaFace and improved FaceNet was proposed. MobileNet-enhanced RetinaFace was applied to ameliorate the impact of output channel quantity and convolution kernel dynamics using depthwise convolution combined with pointwise convolution. Regression predictions of bovine facial features and keypoints were generated under varying distances, scales and sizes. FaceNet's core feature network was enhanced through MobileNet integration, and the loss function was jointly optimized with Cross Entropy Loss and Triplet Loss to achieve a quicker and more stable convergence curve. The distances between the generated embedding vectors of cow facial features were corresponding to the similarity between cow faces, enabling accurate matching. RetinaFace exhibited detection false negative rates of 2.67%, 0.66%, 2.67%, and 3.33% under conditions of occlusion, no occlusion, low light, and bright light for cow facial detection. For cow facial pattern detection, the false negative rates for black and white patterns, pure black and pure white were 1.33%, 6.00% and 8.00%, respectively. Regarding cow facial posture changes, the false negative rates for face upward, bowing down, profile, and normal posture were 1.33%, 1.33%, 4.00% and 0.66%, respectively. Improved FaceNet model achieved an accuray of 99.50% on training set and 83.60% on test set. In comparison to YOLOX, the recognition model presented in this research demonstrated increased accuracy in cow facial detection under occlusion, no occlusion and strong lighting conditions by 2.67%, 0.40%, and 0.40%, respectively. Moreover, the accuracy for patterns with pure black and pure white tones surpassed that of YOLOX by 1.06% and 5.71%, correspondingly. Additionally, the accuracy rates for face upward, bowing down, profile and normal posture were higher than YOLOX by 2.00%, 3.34%, 2.66% and 0.40%, respectively. The proposed model demonstrates the proficiency in accurately identifying individual cows in natural scenes.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 512-523"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42302982","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":"Attention-based generative adversarial networks for aquaponics environment time series data imputation","authors":"Keyang Zhong ,&nbsp;Xueqian Sun ,&nbsp;Gedi Liu ,&nbsp;Yifeng Jiang ,&nbsp;Yi Ouyang ,&nbsp;Yang Wang","doi":"10.1016/j.inpa.2023.10.001","DOIUrl":"10.1016/j.inpa.2023.10.001","url":null,"abstract":"<div><div>Environmental parameter data collected by sensors for monitoring the environment of agricultural facility operations are usually incomplete due to external environmental disturbances and device failures. And the missing of collected data is completely at random. In practice, missing data could create biased estimations and make multivariate time series predictions of environmental parameters difficult, leading to imprecise environmental control. A multivariate time series imputation model based on generative adversarial networks and multi-head attention (ATTN-GAN) is proposed in this work to reducing the negative consequence of missing data. ATTN-GAN can capture the temporal and spatial correlation of time series, and has a good capacity to learn data distribution. In the downstream experiments, we used ATTN-GAN and baseline models for data imputation, and predicted the imputed data, respectively. For the imputation of missing data, over the 20%, 50% and 80% missing rate, ATTN-GAN had the lowest RMSE, 0.1593, 0.2012 and 0.2688 respectively. For water temperature prediction, data processed with ATTN-GAN over MLP, LSTM, DA-RNN prediction methods had the lowest MSE, 0.6816, 0.8375 and 0.3736 respectively. Those results revealed that ATTN-GAN outperformed all baseline models in terms of data imputation accuracy. The data processed by ATTN-GAN is the best for time series prediction.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 542-551"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136009786","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":"IoT based Agriculture (Ag-IoT): A detailed study on Architecture, Security and Forensics","authors":"Santoshi Rudrakar,&nbsp;Parag Rughani","doi":"10.1016/j.inpa.2023.09.002","DOIUrl":"10.1016/j.inpa.2023.09.002","url":null,"abstract":"<div><div>IoT based agriculture (Ag-IoT) is an emerging communication technology that is widely adopted by agricultural entrepreneurs and farmers to perform agricultural agro-chores in the farm to improve productivity, for better monitoring, and to reduce labor costs. However, the use of the Internet in Ag-IoT facilitates real-time functionality in an agriculture system, it can increase the risk of security breaches and cyber attacks that would cause the Ag-IoT system to malfunction and can affect its productivity. Ag-IoT is overlooked in cyber security parameters, which can have severe impacts on its trustworthiness and adoption by agricultural communities. To address this gap, this article presents a systematic study of the literature published between 2001 and 2023 that discusses advances in Ag-IoT technology. The subjects included in the study on Ag-IoT are emerging applications, different IoT architectures, suspected cyber attacks and cyber crimes, and challenges in incident response and digital forensics. The findings of this study encourage the reader to explore future potential research avenues related to the security risks and challenges of Ag-IoT, as well as the readiness for incident response and forensic investigation in the smart agricultural sector. The main conclusion of this study is that security must be ensured in Ag-IoT environments to offer uninterrupted services and also there is a need for forensic readiness for effective investigation in the event of unanticipated security incidents.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 524-541"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42340537","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":"Evaluation of the applicability of a metal oxide semiconductor gas sensor for methane emissions from agriculture","authors":"Bastiaan Molleman ,&nbsp;Enrico Alessi ,&nbsp;Fabio Passaniti ,&nbsp;Karen Daly","doi":"10.1016/j.inpa.2023.11.001","DOIUrl":"10.1016/j.inpa.2023.11.001","url":null,"abstract":"<div><div>This work investigated the potential of metal oxide semiconductor (MOS) gas sensors for environmental monitoring of methane. Calibrations were performed under controlled conditions in the lab, and under semi-controlled conditions in the field, using a modified head space chamber set-up. Concentrations up to ±300 ppm methane were tested. The relationship between sensor conductance and methane concentrations could be very well described using principles from adsorption theory. The adjustable parameters were background conductance G₀, a sensitivity constant S and a non-ideality coefficient n, where n has a non-rational value between 0 and 1. Sensor behaviour was very different in dry air than in humid air, with the background conductance increasing approximately tenfold and sensitivity decreasing between 20 fold and 80 fold, while the non-ideality coefficient increased from ±0.4 to ±0.6. Nevertheless, at high methane concentrations comparable conductance values were recorded in dry and humid air. The standard deviation of predicted values was 1.6 μS.for the least well described dataset. Using the corresponding calibration curve, a detection limit of 11 ppm is calculated for humid ambient air. This values suggests that MOS sensor are adequately sensitive to be used for methane detection in an agricultural context.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 573-580"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135454758","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":"Machine learning enabled assessment of the vacuum freeze-drying of the kiwifruit","authors":"Uzair Sajjad ,&nbsp;Farzana Bibi ,&nbsp;Imtiyaz Hussain ,&nbsp;Naseem Abbas ,&nbsp;Muhammad Sultan ,&nbsp;Hafiz Muhammad Asfahan ,&nbsp;Muhammad Aleem ,&nbsp;Wei-Mon Yan","doi":"10.1016/j.inpa.2024.09.004","DOIUrl":"10.1016/j.inpa.2024.09.004","url":null,"abstract":"<div><div>Drying technologies have been essential for extending the shelf-life of perishable fruits and vegetables for over a century. Vacuum freeze-drying (VFD), though invented over a hundred years ago, remains one of the most advanced drying techniques, known for sustainably drying perishable products while maintaining quality indices and morphological properties comparable to their fresh state. The performance of the VFD system is sensitive to the operating conditions and features of the drying product which is assessed using experimental and/or numerical methods. However, the qualitative aspects of the dried product are not predictable. In this context, the present study aims to create a deep neural framework (DNF) that predicts the performance of a Vacuum Freeze Drying (VFD) system for kiwifruit, based on its morphology and nutritional value under varying conditions. This involves translating the fruit’s morphological features into trainable data and using a Generative Adversarial Network (GAN) to create diverse, unlabeled datasets. The framework is optimized using Gaussian Process (GP) for hyper-parameter tuning, focusing on minimizing errors like mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The maximum MSE of 1.243 is found in the prediction of rehydration rate, followed by color (0.725), energy consumption (0.426), moisture content (0.379), texture (0.320), sensory (0.250), and Brix (0.215), respectively. The maximum MAE and MAPE values are recorded 0.833 and 32.99 % while the minimum is observed 0.368 and 7.019 % in the case of rehydration rate and Brix, respectively. Overall, the R² value was computed 0.863 which is reasonable for the quality assessment of kiwifruit dried by the VFD system.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 2","pages":"Pages 245-259"},"PeriodicalIF":7.7,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115864","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":"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":"2024-09-19","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":"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":"2024-09-10","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":"Maintaining environmental context and geoprivacy protection in agriculture","authors":"Parvaneh Nowbakht ,&nbsp;Lilian O’Sullivan ,&nbsp;David P. Wall ,&nbsp;Paul Holloway","doi":"10.1016/j.inpa.2024.09.001","DOIUrl":"10.1016/j.inpa.2024.09.001","url":null,"abstract":"<div><div>To achieve sustainable agriculture and food security there is an urgent need to share agricultural data with a range of relevant stakeholders; however, to reduce the risk of identification, spatial data must be obfuscated prior to sharing. To-date, most obfuscation methods that have been developed do not consider a) the areal nature of field-level data and b) the differing environmental conditions at the original and obfuscated sites. To address these issues, we developed the Polygon-based Environmental Similarity Obfuscation Method (PESOM) to provide geoprivacy protection and guarantee that obfuscated data will retain the same environmental conditions as the original data. PESOM was developed using an unsupervised clustering algorithm and seasonal climate data, before being applied to the Nutrient Management Plan (NMP) online in Ireland. PESOM satisfied high level of geoprivacy protection and absolute environmental clustering preservation, with no false-identification and non-unique obfuscation risk. It provided a low level of distribution preservation and correlation preservation, large location displacement and subsequently low local analytical accuracy. PESOM is a significant advance on existing obfuscation techniques in agriculture data and will allow the sharing of data to be used widely for agri-environmental purposes, a current limitation of existing methods. The results of this research should be of wide interest to those working in agri-environmental research and computer science, and be of relevance to researchers, data managers, and practitioners.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 2","pages":"Pages 209-220"},"PeriodicalIF":7.7,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115960","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":"A novel random forest-based approach for the non-destructive and explainable estimation of ammonia and chlorophyll in fresh-cut rocket leaves","authors":"Stefano Polimena ,&nbsp;Gianvito Pio ,&nbsp;Maria Cefola ,&nbsp;Michela Palumbo ,&nbsp;Michelangelo Ceci ,&nbsp;Giovanni Attolico","doi":"10.1016/j.inpa.2024.09.002","DOIUrl":"10.1016/j.inpa.2024.09.002","url":null,"abstract":"<div><div>The perceived visual quality of fruits and vegetables plays a central role in the choices made by retail customers. Machine learning (ML) approaches based on image analysis have been recently proposed to overcome the poor efficiency and subjectivity of human visual evaluation as well as the expensiveness and destructiveness of physical and chemical methods that measure internal indicators. In this paper, we propose a ML method based on Random Forests for estimating the chlorophyll and ammonia contents (considered, in the literature, reliable indicators of product freshness) from images of fresh-cut rocket leaves. Our approach copes with specific issues raised by (i) the non-uniform distributions of ammonia and chlorophyll values and (ii) the need to provide insights into the features that produce a particular model outcome, aiming to enhance its trustworthiness. Our experiments, performed on real images of fresh-cut rocket leaves, proved that the proposed approach significantly outperforms 7 competitor methods, obtaining an improvement of the RSE results of 6.6% for the prediction of the ammonia and of 10.4% for the prediction of the chlorophyll over its best competitor. Moreover, a specific analysis of the explainability of the predictions showed that the learned models are based on reasonable features, empowering their acceptance in real-world applications.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 2","pages":"Pages 221-231"},"PeriodicalIF":7.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115961","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}