Information Processing in Agriculture最新文献

{"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":"2024-06-26","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":"Detection of cucumber downy mildew spores based on improved YOLOv5s","authors":"Chen Qiao ,&nbsp;Kaiyu Li ,&nbsp;Xinyi Zhu ,&nbsp;Jiaping Jing ,&nbsp;Wei Gao ,&nbsp;Lingxian Zhang","doi":"10.1016/j.inpa.2024.05.002","DOIUrl":"10.1016/j.inpa.2024.05.002","url":null,"abstract":"<div><div>Cucumber downy mildew is caused by the infection of leaves with downy mildew spores. However, research on the prevention and control of cucumber downy mildew often focuses on the stage after symptoms have appeared on the leaves, that is, once disease spots have already formed. Since the occurrence of downy mildew is closely related to the quantity of spores, early-stage research on the quantity of downy mildew spores is of great significance for the prevention and control of cucumber downy mildew. Consequently, developing a rapid, accurate, and efficient method for detecting cucumber downy mildew spores is critical for advancing disease control. This study introduces an improved YOLOv5s model for spore detection. The model incorporates a transformer module into YOLOv5s’s backbone, enhancing global feature information extraction. It also adds a small object detection head to counter YOLOv5s’s extensive down-sampling and difficulty in learning features of small objects. Integration with the Convolutional Block Attention Module (CBAM) further refines detection precision for small objects like mildew spores. Upon evaluation with an image dataset collected through a microscope, the improved YOLOv5s model demonstrated superior performance metrics across various resolutions. At a resolution of 1440px × 1440px, it achieved the highest mean Average Precision ([email protected]) of 95.4 %, a precision (P) score of 89.1 %, and a recall (R) rate of 90.3 %. These metrics surpassed the original YOLOv5s model at the same 1440px × 1440px resolution by 1.6 % in [email protected], 1.6 % in P, and 0.5 % in R. Additionally, the model’s [email protected] across various resolution scales indicates superior detection precision compared to other leading models like YOLOv7. In the context of microscopic images with small spores and complex backgrounds, the improved YOLOv5s model effectively detects cucumber downy mildew spores, offering valuable insights and technical support for advancing the prevention and control measures against cucumber downy mildew.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 2","pages":"Pages 179-194"},"PeriodicalIF":7.7,"publicationDate":"2024-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115958","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":"Disturbance rejection control of the agricultural quadrotor based on adaptive neural network","authors":"Wenxin Le ,&nbsp;Pengyang Xie ,&nbsp;Jian Chen","doi":"10.1016/j.inpa.2024.05.001","DOIUrl":"10.1016/j.inpa.2024.05.001","url":null,"abstract":"<div><div>In order to solve the problem of stability of agricultural quadrotor working, its controller designing is the first priority. Therefore, this paper makes an attempt to use the Radial Basis Function (RBF) neural network adaptive method combined with sliding mode control to control its height channel. Validation of the efficacy of the RBF neural network in control is conducted through simulation experiments utilizing quadrotor parameters. The application of the method to the control of agricultural quadrotor has laid a theoretical foundation. At the same time, through simulation experiments, it is concluded in theory that the RBF neural network can have a good prediction and elimination effect on the interference during the flight, and the change of the time constant will not affect the control effect of the aircraft. Notably, abrupt changes in time constant indicate UAV motor malfunction. Simulation results affirm the efficacy of the proposed control method in regulating UAV altitude and addressing sudden faults. Real-world experimentation (vegetable field including bean, pepper, eggplant, tomoto, etc.) reveals that even when UAV propellers sustain damage to a certain extent, altitude control and hover capabilities remain intact. These findings provide a solid groundwork for subsequent altitude control endeavors in agricultural quadrotor operations, while also offering innovative avenues for advancing the field.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 2","pages":"Pages 169-178"},"PeriodicalIF":7.7,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141023570","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":"2024-05-08","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":"GIS spatial optimization for agricultural crop allocation using NSGA-II","authors":"Tipaluck Krityakierne ,&nbsp;Pornpimon Sinpayak ,&nbsp;Noppadon Khiripet","doi":"10.1016/j.inpa.2024.04.005","DOIUrl":"10.1016/j.inpa.2024.04.005","url":null,"abstract":"<div><div>This study focuses on the shift from traditional farming methods, reliant on farmer intuition and manual processes, to modern, automated approaches crucial for Thailand’s agricultural sustainability. Despite its vital role in the country’s economy, outdated practices lead to supply imbalances and perpetuate poverty among smallholder farmers. Using geographic information systems (GIS) and mathematical optimization, the present study aims to determine optimal agricultural crop allocation. A multi-objective optimization crop spatial allocation model leverages geospatial data, including crop, soil and climate suitability, to enhance the accuracy of our model. Additionally, we incorporate agricultural economics data, such as market price, crop yield, production cost, distances to secondary producers, production budget limitations, and minimum crop production requirements. To speedup the convergence of the algorithm, we introduce more suitable crossover and mutation operators in NSGA-II, aiming to direct the search towards the Pareto optimal solutions. We demonstrate the effectiveness of our approach in a case study of the agricultural area in Chiang Mai province, Thailand, focusing on three major industrial crops: corn, cane, and rice. Our model suggests land allocation that adheres to both the budget constraint and the minimum production requirements, while retaining only a small surplus for each crop. The successful implementation of this approach in our case study marks a significant advancement in Thai agricultural research, paving the way for long-term economic and environmental sustainability.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 2","pages":"Pages 139-150"},"PeriodicalIF":7.7,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140783666","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":"External defects and severity level evaluation of potato using single and multispectral imaging in near infrared region","authors":"Dimas Firmanda Al Riza ,&nbsp;Slamet Widodo ,&nbsp;Kazuya Yamamoto ,&nbsp;Kazunori Ninomiya ,&nbsp;Tetsuhito Suzuki ,&nbsp;Yuichi Ogawa ,&nbsp;Naoshi Kondo","doi":"10.1016/j.inpa.2022.09.001","DOIUrl":"10.1016/j.inpa.2022.09.001","url":null,"abstract":"<div><p>Non-invasive potato defects detection has been demanded for sorting and grading purpose. Researches on the classification of the defects has been available, however, investigation on the severity level calculation is limited. For the detection of the common scab, it has been found that imaging in the infrared region provide an interesting characteristic that could distinguish defected area to normal area. Thus, investigations on this wavelength range is interesting to add more knowledge and for applications. In this research, the multispectral image has been obtained and investigated especially at three wavelengths (950, 1 150, 1 600 nm). Image pre-processing and pseudo-color conversion techniques were explored to enhance the contrast between defects, normal background skin area and soil deposits. Results show that external defects, such as common scab and some mechanical damage types, appear brighter in the near infrared region, especially at 1 600 nm against the normal skin background. It has been found that pseudo-color images conversion provides more information regarding type if surface characteristics compared to grayscale single imaging. Image segmentation using pseudo-color images after multiplication operation pre-processing could be used for common scab and mechanical damage detection excluding soil deposits with a Dice Sorensen coefficient of 0.64. In addition, image segmentation using single image at 1 600 nm shown relatively better results with Dice Sorensen coefficient of 0.72 with note that thick soil deposits will also be segmented. Defect severity level evaluation had an R² correlation of 0.84 against standard measurements of severity.</p></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 1","pages":"Pages 80-90"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214317322000725/pdfft?md5=4f99b9e0d9f62df98198e21f91cbdeca&pid=1-s2.0-S2214317322000725-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44156487","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":"Modeling and optimization of non-isothermal convective drying process of Lavandula × allardii","authors":"Vasileios Chasiotis,&nbsp;Konstantinos-Stefanos Nikas,&nbsp;Andronikos Filios","doi":"10.1016/j.inpa.2022.06.001","DOIUrl":"https://doi.org/10.1016/j.inpa.2022.06.001","url":null,"abstract":"<div><p>Non-isothermal convective drying schemes were examined for <em>Lavandula × allardii</em> leaves and inflorescences. Drying process parameters were optimized using response surface methodology (RSM) to ensure the peak operational performance. The effects of temperature increase rate (2–4 °C/h) and the airflow velocity (1–3 m/s) on the essential oil yield, drying duration and consumption, were investigated. A face-centered central composite design was deployed and the experimental data was adapted to the most suitable polynomial models, as determined by the regression analysis. Analysis of variance was applied to assess the effects of the process variables, their interactions and the statistical significance of the examined models. Both factors of temperature increase rate and airflow velocity had a significant impact on the drying duration. Airflow velocity had a greater effect on leaves’ essential oil yield and inflorescences’ process energy consumption, whereas the rates of temperature increase had a greater influence on the inflorescences’ essential oil yield and leaves’ energy consumption. The minimum drying duration and energy consumption were obtained for the maximum temperature increasing rate at 3 and 1 m/s airflow velocities respectively; and the highest essential oil yield was obtained for the least rate of temperature increase and airflow velocity for both leaves and inflorescences. Numerical optimization was performed for minimizing drying duration and energy consumption by maximizing the essential oil yield. The rate of temperature increases of 4 °C/h and the airflow velocity of 1 m/s, were proposed as the optimum non-isothermal drying conditions for both leaves and inflorescences of <em>Lavandula × allardii</em>. Predicted values of essential oil content have been 1.387/3.05 mL/g, 4.21/4.18 h drying time and 0.809/0.732 kWh energy consumption at the optimum operation point for leaves and inflorescences, respectively. The resulted optimized non-stationary temperature scheme considerably improved the drying kinetics and the process consumption by achieving a similar essential oil recovery with the standard low-temperature convective drying. The present study aimed to eliminate the preexisting gap of the optimum selection of the process parameters for the particular type of the examined non-isothermal drying schemes. Previous findings could be utilized for designing dryers and drying schedules aiming to retain the qualitative attributes, by reducing the cost and duration of the drying operations.</p></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 1","pages":"Pages 1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214317322000567/pdfft?md5=818a897cc9ceff236aac7c274146ad29&pid=1-s2.0-S2214317322000567-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139992398","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":"Foundations of Programmable Process Structures for the unified modeling and simulation of agricultural and aquacultural systems","authors":"Monika Varga,&nbsp;Bela Csukas","doi":"10.1016/j.inpa.2022.10.001","DOIUrl":"10.1016/j.inpa.2022.10.001","url":null,"abstract":"<div><p>This research paper defines the theoretical foundations and computational implementation of a non-conventional modeling and simulation methodology, inspired by the needs of problem solving for biological, agricultural, aquacultural and environmental systems. The challenging practical problem is to develop a framework for automatic generation of causally right and balance-based, unified models that can also be applied for the effective coupling amongst the various (sophisticated field-specific, sensor data processing-based, upper level optimization-driven, etc.) models. The scientific problem addressed in this innovation is to develop Programmable Process Structures (PPS) by combining functional basis of systems theory, structural approach of net theory and computational principles of agent based modeling. PPS offers a novel framework for the automatic generation of easily extensible and connectible, unified models for the underlying complex systems. PPS models can be generated from one state and one transition meta-prototypes and from the transition oriented description of process structure. The models consist of unified state and transition elements. The local program containing prototype elements, derived also from the meta-prototypes, are responsible for the case-specific calculations. The integrity and consistency of PPS architecture are based on the meta-prototypes, prepared to distinguish between the conservation-laws-based measures and the signals. The simulation is based on data flows amongst the state and transition elements, as well as on the unification based data transfer between these elements and their calculating prototypes. This architecture and its AI language-based (Prolog) implementation support the integration of various field- and task-specific models, conveniently. The better understanding is helped by a simple example. The capabilities of the recently consolidated general methodology are discussed on the basis of some preliminary applications, focusing on the recently studied agricultural and aquacultural cases.</p></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 1","pages":"Pages 91-108"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214317322000737/pdfft?md5=d9d3dbf2df68ae15a8175599e80f60b2&pid=1-s2.0-S2214317322000737-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48721718","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 novel low-cost visual ear tag based identification system for precision beef cattle livestock farming","authors":"Andrea Pretto ,&nbsp;Gianpaolo Savio ,&nbsp;Flaviana Gottardo ,&nbsp;Francesca Uccheddu ,&nbsp;Gianmaria Concheri","doi":"10.1016/j.inpa.2022.10.003","DOIUrl":"10.1016/j.inpa.2022.10.003","url":null,"abstract":"<div><p>The precision livestock farming (PLF) has the objective to maximize each animal's performance while reducing the environmental impact and maintaining the quality and safety of meat production. Among the PLF techniques, the personalised management of each individual animal based on sensors systems, represents a viable option. It is worth noting that the implementation of an effective PLF approach can be still expensive, especially for small and medium-sized farms; for this reason, to guarantee the sustainability of a customized livestock management system and encourage its use, plug and play and cost-effective systems are needed. Within this context, we present a novel low-cost method for identifying beef cattle and recognizing their basic activities by a single surveillance camera. By leveraging the current state-of-the-art methods for real-time object detection, (i.e., YOLOv3) cattle's face areas, we propose a novel mechanism able to detect the ear tag as well as the water ingestion state when the cattle is close to the drinker. The cow IDs are read by an Optical Character Recognition (OCR) algorithm for which, an ad hoc error correction algorithm is here presented to avoid numbers misreading and correctly match the IDs to only actually present IDs. Thanks to the detection of the tag position, the OCR algorithm can be applied only to a specific region of interest reducing the computational cost and the time needed. Activity times for the areas are outputted as cattle activity recognition results. Evaluation results demonstrate the effectiveness of our proposed method, showing a [email protected] of 89%.</p></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 1","pages":"Pages 117-126"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S221431732200083X/pdfft?md5=7cfaf05969ff7b29f8fe80e9ab1fe516&pid=1-s2.0-S221431732200083X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43588057","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":"The use of Vis-NIR-SWIR spectroscopy in the prediction of soil available ions after application of rock powder","authors":"Marlon Rodrigues ,&nbsp;Josiane Carla Argenta ,&nbsp;Everson Cezar ,&nbsp;Glaucio Leboso Alemparte Abrantes dos Santos ,&nbsp;Önder Özal ,&nbsp;Amanda Silveira Reis ,&nbsp;Marcos Rafael Nanni","doi":"10.1016/j.inpa.2022.07.001","DOIUrl":"10.1016/j.inpa.2022.07.001","url":null,"abstract":"<div><p>Some of the problems attributed to traditional laboratory analyses that limit the correct assessment of the nutrient contents in the soil are time requirements and high cost of the soil nutrient determinations. To solve these problems, a study was carried out to evaluate the use of visible, near-infrared, and short-wave infrared (Vis-NIR-SWIR) spectroscopy in the prediction of soil available ions submitted to the application of rock powders. The study was carried out on an Arenosol in Paranavaí City/Brazil. Treatments (rock powders) were arranged within a split-plot system designed in randomized blocks with four repetitions. Sugarcane was cultivated for 14 months after the application of rock powders. Later, 96 soil samples were collected for measuring the pH and available ions P, K⁺, Ca²⁺, Mg²⁺, S-SO₄^2-, Si, Cu²⁺, Fe²⁺, Mn²⁺, and Zn²⁺ as well as spectral reading through a Vis-NIR-SWIR spectroradiometer to predict the soil chemical attributes through the partial least square regression (PLS) technique. The results showed that the elements K⁺, Ca²⁺, Mg²⁺, Cu²⁺, and Fe²⁺ could be predicted with a reasonable rightness degree (R²_p &gt; 0.50, RPD_p &gt; 1.40) from spectral models. However, for the attributes pH, P, S-SO₄^2-, Si, Mn²⁺, and Zn²⁺, there were no satisfactory models (R²_p &lt; 0.50, RPD_p &lt; 1.40). Thus, the application of rock powder changed the spectral curves and, because of that, allows the building of PLS models to predict the elements K⁺, Ca²⁺, Mg²⁺, Cu²⁺, and Fe²⁺. Therefore, Vis-NIR-SWIR spectroscopy is a promising alternative to the routine analyses of soil fertility since it has advantages such as fast analytical speed, low cost, easy to operate, non-destructive, and environmentally friendly, because it does not use harmful chemicals.</p></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 1","pages":"Pages 26-44"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S221431732200066X/pdfft?md5=f113d0de72823c7c8e0aa16e6172ef63&pid=1-s2.0-S221431732200066X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48374091","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}