Nguyen Van Hung, Tran Ngoc Thach, Nguyen Ngoc Hoang, Nguyen Cao Quan Binh, Dang Minh Tâm, Tran Tan Hau, Duong Thi Tu Anh, Trinh Quang Khuong, Vo Thi Bich Chi, Truong Thi Kieu Lien, Martin Gummert, Tovohery Rakotoson, Kazuki Saito, Virender Kumar
{"title":"Mechanized wet direct seeding for increased rice production efficiency and reduced carbon footprint","authors":"Nguyen Van Hung, Tran Ngoc Thach, Nguyen Ngoc Hoang, Nguyen Cao Quan Binh, Dang Minh Tâm, Tran Tan Hau, Duong Thi Tu Anh, Trinh Quang Khuong, Vo Thi Bich Chi, Truong Thi Kieu Lien, Martin Gummert, Tovohery Rakotoson, Kazuki Saito, Virender Kumar","doi":"10.1007/s11119-024-10163-8","DOIUrl":"https://doi.org/10.1007/s11119-024-10163-8","url":null,"abstract":"<p>Crop establishment is one of the major rice production operations that strongly affects rice production, productivity, and environmental impacts. This research introduced a new technology and provided scientific evidence for the benefits of mechanized wet direct seeding (mDSR) of rice as compared with the other crop establishment practices commonly applied by farmers for wet direct seeded rice in Mekong River Delta in Vietnam, such as seeding in line using drum-seeder (dDSR) and broadcast seeding (bDSR). The experiment was implemented across two consecutive rice cropping seasons that are Winter-Spring season and Summer-Autumn season in 2020–2021. Treatments included (1–3) mDSR with seeding rates of 30, 50, and 70 kg ha<sup>− 1</sup>, (4) dDSR with 80 kg ha<sup>− 1</sup> seed rate, and (5) bDSR as current farmer practice with seeding rate of 180 kg ha<sup>− 1</sup>. The fertilizer application was adjusted as per seeding rate with 80:40:30 kg ha<sup>− 1</sup> N: P<sub>2</sub>O<sub>5</sub>: K<sub>2</sub>O with lower seed rate 30 and 50 kg ha<sup>− 1</sup> in mDSR; 90:40:30 kg ha<sup>− 1</sup> N: P<sub>2</sub>O<sub>5</sub>: K<sub>2</sub>O with medium seed rate of 70 to 80 kg ha<sup>− 1</sup>; and 115:55:40 kg ha<sup>− 1</sup> N: P<sub>2</sub>O<sub>5</sub>: K<sub>2</sub>O with high seed rate of 180 kg ha<sup>− 1</sup> in bDSR. Mechanized wet direct seeding rice with a lower seed rate of 30 to 70 kg ha<sup>− 1</sup> and fertilizer rate by 22–30% reduced variation in seedling density by 40–80% and in yield by 0.1 to 0.3 t ha<sup>− 1</sup> and had similar yield to bDSR. In consequence, N productivity was 27 and 32% higher in mDSR as compared to bDSR during the Winter-Spring season and Summer-Autumn seasons, respectively. The use of lower seed rate and fertilizer in mDSR also led to higher income and lower carbon footprint (GHGe per kg of paddy grains) of rice production than the currently used practices of bDSR. Net income of mDSR was comparable to that of dDSR and higher by 145–220 and 171–248 $US than that of bDSR in Winter-Spring season and Summer-Autumn, respectively. The carbon footprint of mDSR rice production compared to bDSR was lower by 22–25% and 12–20% during the Winter-Spring and Summer-Autumn seasons, respectively. Given the above benefits of farming efficiency, higher income, and low emission, mDSR would be a technology package that strongly supports sustainable rice cultivation transformation for the Mekong River Delta of Vietnam.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"56 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141597655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kushal KC, Matthew Romanko, Andrew Perrault, Sami Khanal
{"title":"On-farm cereal rye biomass estimation using machine learning on images from an unmanned aerial system","authors":"Kushal KC, Matthew Romanko, Andrew Perrault, Sami Khanal","doi":"10.1007/s11119-024-10162-9","DOIUrl":"https://doi.org/10.1007/s11119-024-10162-9","url":null,"abstract":"<p>This study assesses the potential of using multispectral images collected by an unmanned aerial system (UAS) on machine learning (ML) frameworks to estimate cereal rye (<i>Secale cereal</i> L.) biomass. Multispectral images and ground-truth cereal rye biomass data were collected from 15 farmers’ fields up to three times between March and May in northwest Ohio. Images were processed to derive 13 vegetation indices (VIs). Out of 13 VIs, six optimal sets of VIs, including excess green (ExG), normalized green red difference index (NGRDI), soil adjusted vegetation index (SAVI), blue green ratio (B_G_ratio), red-edge triangular vegetation index (RTVI), and normalized difference red-edge (NDRE) were selected using the variance inflation factor (VIF) based feature selection approach. Six regression models including a multiple linear regression (MLR), elastic net (ENET), multivariate adaptive regression splines (MARS), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB) were investigated for estimation of cereal rye biomass based on the VIs. For most of the models, the six selected VIs performed better than or similar to the full set of 13 VIs with R<sup>2</sup> ranging from 0.24 to 0.59 and RMSE ranging from 83.13 to 91.89 g/m<sup>2</sup> during 10-fold cross-validation. During independent accuracy assessment with the selected set of VIs, XGB exhibited the highest R<sup>2</sup> (0.67) and lowest RMSE (83.13 g/m<sup>2</sup>) and MAE (48.13 g/m<sup>2</sup>) followed by RF and ENET. For all the models, the agreement between observed and predicted biomass was high for biomass less than or equal to 200 g/m<sup>2</sup> but decreased for biomass greater than 200 g/m<sup>2</sup>. When field-collected structural features were integrated with the selected VIs, the models showed improved performance, with R<sup>2</sup> and RMSE of the models reaching up to 0.82 and 61.67 g/m<sup>2</sup> respectively. Among the six VIs, SAVI showed the strongest impact on the model prediction for the best-performing RF and XGB regression models. The findings of this study demonstrate the potential of precisely estimating and mapping cereal rye biomass based on UAS-captured multispectral images. Timely information on cover crop growth can facilitate numerous decision-making processes, including planning the planting operations, and management of nutrients, weeds, and soil moisture to improve agronomic and environmental outcomes.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"28 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141553310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. J. Tilse, P. Filippi, B. Whelan, T. F. A. Bishop
{"title":"Downscaling crop production data to fine scale estimates with geostatistics and remote sensing: a case study in mapping cotton fibre quality","authors":"M. J. Tilse, P. Filippi, B. Whelan, T. F. A. Bishop","doi":"10.1007/s11119-024-10161-w","DOIUrl":"https://doi.org/10.1007/s11119-024-10161-w","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>A generalised approach to downscale areal observations of crop production data is illustrated using cotton yield and fibre quality (length and micronaire) data which is measured as a module (areal/block) average.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>Two features of the downscaling algorithm are; (i) to estimate spatial trends in yield and quality using regression with fine resolution predictors such as remote sensing imagery, and (ii) use area-to-point kriging (A2PK) to downscale either the observations in the absence of a useful spatial trend model or the residuals from the trend model (if useful) from areal averages.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Correlations with remote sensing covariates were stronger for cotton fibre yield than for cotton fibre micronaire, and much stronger compared to those for cotton fibre length. Spatial trends in cotton fibre yield and micronaire could be estimated with good model quality using regression with remote sensing covariates with or without A2PK in almost all fields. Conversely, model quality was poorer for cotton fibre length and there was only a small difference in model performance between the null and trend models. When the downscaling approach was tested using fine-resolution yield observations, model performance was poorer at a fine-resolution compared to the module-resolution, which was to be expected.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>This approach enables the creation of high-resolution raster maps of variables of interest with a much finer spatial resolution compared to the areal observations, and can be applied for any areal averaged crop production data in a range of broadacre and horticultural industries (e.g. sugarcane, apples, citrus). The finer spatial resolution may allow growers or agronomists to better understand the drivers of variability within fields, assess management implications, and create management plans at a higher resolution.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"25 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141553464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marco Fiorentini, Calogero Schillaci, Michele Denora, Stefano Zenobi, Paola A. Deligios, Rodolfo Santilocchi, Michele Perniola, Luigi Ledda, Roberto Orsini
{"title":"Fertilization and soil management machine learning based sustainable agronomic prescriptions for durum wheat in Italy","authors":"Marco Fiorentini, Calogero Schillaci, Michele Denora, Stefano Zenobi, Paola A. Deligios, Rodolfo Santilocchi, Michele Perniola, Luigi Ledda, Roberto Orsini","doi":"10.1007/s11119-024-10153-w","DOIUrl":"https://doi.org/10.1007/s11119-024-10153-w","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>This research aims to develop a meta-machine learning model to optimize soil and nitrogen management for durum wheat in Italy. It addresses the challenges of increased food production on limited land amidst rising input costs, geopolitical changes, and climate change. The goal is to aid decision-makers in achieving maximum crop yield and income margins through effective agronomic strategies.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>The study developed a meta-machine learning model, integrating classification and regression models, and tested it at four sites in Marche and Basilicata, Italy, over several years. The model incorporated data from remote sensing, crop phenology, soil chemical properties, weather data, soil management, and nitrogen levels. A Random Forest model was used to classify crop phenology, while a Neural Network model predicted yield. Eleven nitrogen levels were compared across these sites.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The Random Forest model achieved an accuracy of 0.98, kappa of 0.96, and recall of 0.98 for predicting crop phenology. The Neural Network model for yield prediction had an R squared of 0.90 and a Root Mean Square Error of 0.59 t ha-1. Key factors identified for model accuracy were temperature, precipitation, NDVI, and nitrogen input. Simulations of 30 soil management and fertilization combinations revealed that no-tillage management increased grain yield. The Marginal Fertilizer Yield Index determined optimal nitrogen application.</p><h3 data-test=\"abstract-sub-heading\">Conclusions</h3><p>The meta-machine learning model accurately predicted durum wheat yield and identified effective agronomic strategies, demonstrating the potential for broader application in field conditions. The model offers a promising approach to sustainable agriculture and climate change mitigation by utilising publicly available spatial datasets.</p><h3 data-test=\"abstract-sub-heading\">Graphical abstract</h3>\u0000","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"25 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141553309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Integrative approaches in modern agriculture: IoT, ML and AI for disease forecasting amidst climate change","authors":"Payam Delfani, Vishnukiran Thuraga, Bikram Banerjee, Aakash Chawade","doi":"10.1007/s11119-024-10164-7","DOIUrl":"https://doi.org/10.1007/s11119-024-10164-7","url":null,"abstract":"<p>Plant disease forecasting models, driven by concurrent data and advanced technologies, are reliable tools for accurate prediction of disease outbreaks in achieving sustainable and productive agricultural systems. Optimal integration of Internet of Things (IoTs), machine learning (ML) techniques and artificial intelligence (AI), further augment the capabilities of these models in empowering farmers with proactive disease control measures towards modern agriculture manifested by efficient resource management, reduced diseases and higher crop yields. This article summarizes the role of disease forecasting models in crop management, emphasizing the advancements and applications of AI and ML in disease prediction, challenges and future directions in the field via (a) The technological foundations and need for validation testing of models, (b) The advancements in disease forecasting with the importance of high-quality publicly available data and (c) The challenges and future directions for the development of transparent and interpretable open-source AI models. Further improvement of these models needs investment in continuous innovative research with collaboration and data sharing among agricultural stakeholders.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"59 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jyoti S. Jennewein, W. Hively, Brian T. Lamb, Craig S. T. Daughtry, Resham Thapa, Alison Thieme, Chris Reberg-Horton, Steven Mirsky
{"title":"Spaceborne imaging spectroscopy enables carbon trait estimation in cover crop and cash crop residues","authors":"Jyoti S. Jennewein, W. Hively, Brian T. Lamb, Craig S. T. Daughtry, Resham Thapa, Alison Thieme, Chris Reberg-Horton, Steven Mirsky","doi":"10.1007/s11119-024-10159-4","DOIUrl":"https://doi.org/10.1007/s11119-024-10159-4","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Cover crops and reduced tillage are two key climate smart agricultural practices that can provide agroecosystem services including improved soil health, increased soil carbon sequestration, and reduced fertilizer needs. Crop residue carbon traits (i.e., lignin, holocellulose, non-structural carbohydrates) and nitrogen concentrations largely mediate decomposition rates and amount of plant-available nitrogen accessible to cash crops and determine soil carbon residence time. Non-destructive approaches to quantify these important traits are possible using spectroscopy.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>he objective of this study was to evaluate the efficacy of spectroscopy instruments to quantify crop residue biochemical traits in cover crop agriculture systems using partial least squares regression models and a combination of (1) the band equivalent reflectance (BER) of the <i>PRecursore IperSpettrale della Missione Applicativa</i> (PRISMA) imaging spectroscopy sensor derived from laboratory collected Analytical Spectral Devices (ASD) spectra (<i>n</i> = 296) of 11 cover crop species and three cash crop species, and (2) spaceborne PRISMA imagery that coincided with destructive crop residue collections in the spring of 2022 (<i>n</i> = 65). Spectral range was constrained to 1200 to 2400 nm to reduce the likelihood of confounding relationships in wavelengths sensitive to plant pigments or those related to canopy structure for both analytical approaches.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Models using laboratory BER of PRISMA all demonstrated high accuracies and low errors for estimation of nitrogen and carbon traits (adj. <i>R</i><sup><i>2</i></sup> = 0.86 − 0.98; RMSE = 0.24 − 4.25%) and results indicate that a single model may be used for a given trait across all species. Models using spaceborne imaging spectroscopy demonstrated that crop residue carbon traits can be successfully estimated using PRISMA imagery (adj. <i>R</i><sup><i>2</i></sup> = 0.65 − 0.75; RMSE = 2.71 − 4.16%). We found moderate relationships between nitrogen concentration and PRISMA imagery (adj. <i>R</i><sup><i>2</i></sup> = 0.52; RMSE = 0.25%), which is partly related to the range of nitrogen in these senesced crop residues (0.38–1.85%). PRISMA imagery models were also influenced by atmospheric absorption, variability in surface moisture content, and some presence of green vegetation.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>As spaceborne imaging spectroscopy data become more widely available from upcoming missions, crop residue trait estimates could be regularly generated and integrated into decision support tools to calculate decomposition rates and associated nitrogen credits to inform precision field management, as well as to enable measurement, monitoring, reporting, and verification of net carbon benefits from climate smart agricultural practice adoption in an eme","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"2015 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Georgios Kleftodimos, Leonidas Sotirios Kyrgiakos, Stelios Kartakis, Christina Kleisiari, Marios Vasileiou, Marios Dominikos Kremantzis, George Vlontzos
{"title":"Promoting excellence or discouraging mediocrity – a policy framework assessment for precision agriculture technologies adoption","authors":"Georgios Kleftodimos, Leonidas Sotirios Kyrgiakos, Stelios Kartakis, Christina Kleisiari, Marios Vasileiou, Marios Dominikos Kremantzis, George Vlontzos","doi":"10.1007/s11119-024-10160-x","DOIUrl":"https://doi.org/10.1007/s11119-024-10160-x","url":null,"abstract":"<p>Precision Agriculture Technologies (PATs) are providing a great potential in alleviating adverse impacts arising from climate change. This study evaluates the decision-making process of farmers regarding the adoption and implementation of PATs in potato agricultural cooperative in Northern Greece. For this purpose, a bio-economic model utilizing mathematical programming techniques was designed and applied to three different farms producing Protected Geographical Indication (PGI) potato of Kato Nevrokopi. The proposed model aims to incorporate the existing management methods of farming systems and their associated characteristics. Its objective is to analyse the aspirations of farmers to adopt new practices, considering agronomic, environmental, and policy limitations. Special focus was paid to two distinct scenarios: (a) subsiding PATs adopters or (b) penalizing the non-adopters. Results indicated that subsidy provision 594–650€/ha would have a greater impact on PATs profitability. Lastly, based on the results, further explanations of incentives towards promoting the adoption of novel practices, ensuring the long-term viability of agricultural systems, are proposed.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"54 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141448209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jasmine Neupane, Chenggang Wang, Glen L. Ritchie, Fangyuan Zhang, Sanjit K. Deb, Wenxuan Guo
{"title":"Spatial and temporal patterns of cotton profitability in management zones based on soil properties and topography","authors":"Jasmine Neupane, Chenggang Wang, Glen L. Ritchie, Fangyuan Zhang, Sanjit K. Deb, Wenxuan Guo","doi":"10.1007/s11119-024-10158-5","DOIUrl":"https://doi.org/10.1007/s11119-024-10158-5","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Understanding spatial and temporal variability of absolute and relative profit within fields provides a basis for site-specific management of limited agricultural inputs such as water. The objectives of this study were to evaluate the pattern of spatial and temporal variation of cotton profitability and to assess the stability of profit in management zones (MZs) created based on soil properties and topography.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>This study analyzed profitability patterns in eight commercially managed fields in the Southern High Plains from 2000 to 2003. Each field was divided into 30 m grids and soil physical properties, topography, and lint yield were collected for each grid. Based on the input cost and output prices, profit was also calculated for each grid. Clusters or MZs based on soil and topographic properties were created for each field using the partitioning around medoids (PAM) clustering algorithm. ANOVA and Least Significant Difference tests were conducted to determine the difference in profit among the clusters over multiple years.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>In four of the eight fields, the spatial pattern of profit was consistent across multiple years, indicating the potential of using MZs for site-specific input management. For the rest of the fields, the profit pattern in clusters was inconsistent across multiple years, indicating the need for within-season dynamic MZs.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>The variability in soil and topographic properties influenced the profitability of management zones within a field across multiple years. Hence, this study indicates that understanding the variability in profit patterns in management zones can help to determine the best strategy for field-specific and year-specific precision input management. </p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"57 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141430484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Agung B. Santoso, Evawaty S. Ulina, Siti F. Batubara, Novia Chairuman, Sudarmaji, Siti D. Indrasari, Arlyna B. Pustika, Nana Sutrisna, Yanto Surdianto, Rahmini, Vivi Aryati, Erpina D. Manurung, Hendri F. P. Purba, Wasis Senoaji, Noldy R. E. Kotta, Dorkas Parhusip, Widihastuty, Ani Mugiasih, Jeannette M. Lumban Tobing
{"title":"Are Indonesian rice farmers ready to adopt precision agricultural technologies?","authors":"Agung B. Santoso, Evawaty S. Ulina, Siti F. Batubara, Novia Chairuman, Sudarmaji, Siti D. Indrasari, Arlyna B. Pustika, Nana Sutrisna, Yanto Surdianto, Rahmini, Vivi Aryati, Erpina D. Manurung, Hendri F. P. Purba, Wasis Senoaji, Noldy R. E. Kotta, Dorkas Parhusip, Widihastuty, Ani Mugiasih, Jeannette M. Lumban Tobing","doi":"10.1007/s11119-024-10156-7","DOIUrl":"https://doi.org/10.1007/s11119-024-10156-7","url":null,"abstract":"<p>Precision agriculture technologies (PATs) are believed to be able to ensure the sustainability of rice production. However, the adoption of PATs in developing countries is much lower than in developed countries. The basic question of our research is how Indonesian rice farmers are ready to adopt precision agriculture since they are smallholder farmers. Data was collected from 521 rice farmers in five Indonesian provinces, i.e. North Sumatra, West Java, Yogyakarta, South Sulawesi, and East Nusa Tenggara, in 2023. Farmers were interviewed face to face using structured questionnaires. The data were analysed using Partial Least Squares-Structural Equation Modelling (PLS-SEM) through the Python software. The results showed that Indonesian rice farmers have a moderate level of readiness. The mean value of the capabilities and opportunities indicators were 2.54 to 3.8, while the range for the opportunity’s indicator is 3.23 to 4.11, larger than the capabilities indicators. The level of precision agriculture implementation on Indonesian rice farmers was significant influenced by management (β = 0.42, t = 7.11, <i>p</i> < 0.05), environment (β = 0.17, t = 3.63, <i>p</i> < 0.05), readiness (β = 0.14, t = 2.51, <i>p</i> < 0.05), and technology (β = 0.10, t = 2.12, <i>p</i> < 0.05), economy (β = 0.09, t = 3.63, <i>p</i> < 0.05), and technology<sup>2</sup> (β = -0.072, t = 3.5, <i>p</i> < 0.05). Meanwhile, farmer readiness was significantly influenced by opportunity (β = 0.39, t = 6.64, <i>p</i> < 0.05) and capabilities (β = 0.43, t = 6.82, <i>p</i> < 0.05). This research provides information on the status of human resource capacity in exploiting opportunities for implementing precision agriculture and technical policy advice. The Indonesian government should improve farmers’ skills in information technology, Global Positioning Systems (GPS), and sensor technology in agricultural sectors, and facilitate access to technology and resources in order to increase rice farmers’ readiness to adopt PATs. For opportunity indicators, however, further research is needed to determine which components require immediate attention for construction or development.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"182 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mauro Martini, Marco Ambrosio, Alessandro Navone, Brenno Tuberga, Marcello Chiaberge
{"title":"Enhancing visual autonomous navigation in row-based crops with effective synthetic data generation","authors":"Mauro Martini, Marco Ambrosio, Alessandro Navone, Brenno Tuberga, Marcello Chiaberge","doi":"10.1007/s11119-024-10157-6","DOIUrl":"https://doi.org/10.1007/s11119-024-10157-6","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Introduction</h3><p>Service robotics is recently enhancing precision agriculture enabling many automated processes based on efficient autonomous navigation solutions. However, data generation and in-field validation campaigns hinder the progress of large-scale autonomous platforms. Simulated environments and deep visual perception are spreading as successful tools to speed up the development of robust navigation with low-cost RGB-D cameras.</p><h3 data-test=\"abstract-sub-heading\">Materials and methods</h3><p>In this context, the contribution of this work resides in a complete framework to fully exploit synthetic data for a robust visual control of mobile robots. A wide realistic multi-crops dataset is accurately generated to train deep semantic segmentation networks and enabling robust performance in challenging real-world conditions. An automatic parametric approach enables an easy customization of virtual field geometry and features for a fast reliable evaluation of navigation algorithms.</p><h3 data-test=\"abstract-sub-heading\">Results and conclusion</h3><p>The high quality of the generated synthetic dataset is demonstrated by an extensive experimentation with real crops images and benchmarking the resulting robot navigation both in virtual and real fields with relevant metrics.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"71 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141309051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}