SMS-based Smarter Agriculture decision support system for yellow corn farmers in Isabela

Trogo, Rhia; Ebardaloza, Jed Barry; Sabido, D.J.M.; Bagtasa, Gerry; Tongson, Edgardo; Balderama, Orlando

doi:10.1109/ihtc.2015.7238049

Cited by 24 publications

(6 citation statements)

References 2 publications

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“…Accounting for antecedent soil moisture thus enables a more accurate assessment of the risk of critically dry conditions in the days after planting. Finally, previous work has demonstrated the necessity of carefully considering the delivery and context of decision support in the global south (for example, Trogo et al, 2015). Smallholders in Africa have limited access to smartphones and computers.…”

Section: Discussionmentioning

confidence: 99%

“…In particular, the difficulty that farmers in developing countries face when accessing computer-based systems is widely recognized. An SMS-based interface to the DSSAT crop modeling system (Jones et al, 2003) has been developed for a region of the Philippines (Trogo et al, 2015). Churi et al (2013) propose a similar technological framework but incorporating a wider range of climate and market data.…”

Section: Introductionmentioning

confidence: 99%

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Application of TAMSAT-ALERT soil moisture forecasts for planting date decision support in Africa

Black¹,

Asfaw²,

Sananka³

et al. 2023

Front. Clim.

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Deciding when to plant is critical for smallholders in Africa. If they plant too early, farmers risk seedling death if the rains are not established; if they plant too late, there will not be enough rain to sustain the crop through critical development periods. In this study, we present a new decision support tool (DST) that accounts for the trade-off in the risks of early and late planting through advisories based on both short- and long-range forecasts of crop water availability. Unlike most existing operational systems, which are based solely on rainfall, the DST presented here uses ensemble forecasts of soil moisture to estimate the optimal planting date at a local scale. Evaluations using >30,000 observations of planting date and yield in Kenya, Rwanda, Uganda, Zambia and Malawi demonstrate that that planting at the optimal time would increase yield by 7–10% overall, and up to 20% for late planting farmers. The DST has been piloted by One Acre Fund for the 2019–2020, 2020–2021, and 2021–2022 seasons and there is strong demand for the service to be extended further. We conclude from the evaluations and pilots that the planting date DST has the potential to strengthen farmer decision making and hence their resilience to climate variability and change.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of TAMSAT-ALERT soil moisture forecasts for planting date decision support in Africa

Black¹,

Asfaw²,

Sananka³

et al. 2023

Front. Clim.

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“…Trogo et al [20] presented agriculture as a major industry in every country. e use of technology had great influence to increase the yield.…”

Section: Related Workmentioning

confidence: 99%

IOT-Based Cotton Whitefly Prediction Using Deep Learning

Saleem

Kazmi

Bajwa

et al. 2021

Scientific Programming

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Agriculture is suffering from the problem of low fertility and climate hazards such as increased pest attacks and diseases. Early prediction of pest attacks can be very helpful in improving productivity in agriculture. Insect pest (whitefly) attack has a high influence on cotton crop yield. Internet of Things solution is proposed to predict the whitefly attack to take prevention measures. An insect pest prediction system (IPPS) was developed with the help of the Internet of Things and a RBFN algorithm based on environmental parameters such as temperature, humidity, rainfall, and wind speed. Pest Warning and Quality Control of Pesticides proposed an economic threshold level for prediction of whitefly attack. The economic threshold level and RBFN algorithm are used to predict the whitefly attack using temperature, humidity, rainfall, and wind speed. The seven evaluation metrics accuracy, f-measures, precision, recall, Cohen’s kappa, ROC AUC, and confusion matrix are used to determine the performance of the RBFN algorithm. The proposed insect pest prediction system is deployed in the high influenced region of pest that provides pest prediction information to the farmer to take control measures.

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“…The SMS messages were used for health information sharing purpose like communication between patient and health clinics, sexual health education and so on. While, in the agriculture, a research for corn farmers had been implemented [23]. The research focussed on the how to use SMS services to help farmers to be able to identify the best date to start planting, best date to harvest and optimal water availability as well as the projected crop yield.…”

Section: Short Message Service (Sms)mentioning

confidence: 99%

Text Mining for Pest and Disease Identification on Rice Farming with Interactive Text Messaging

Costa

Tjandrasa

Djanali

2018

IJECE

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To overcome pests and diseases of rice farming, farmers always rely on information and knowledge from agricultural experts for decision making. The problem is that experts are not always available when the farmers need and the cost is quite high. Pests and diseases elimination is hard to be done individually since the farmers are lack of knowledge about the pest types that attack the rice fields. The objective of this study is to build a knowledgebased system that can identify pests and diseases interactively based on the information that has been told by the farmers using SMS communication services. The system can provide a convenience way to the farmers in delivering pests and disease problem information using a natural language. The text mining method performs tokenizing, filtering and porter stemming that used to extract important information sent by a SMS service. The method of Jaccard Similarity Coefficient (JSC) was used to calculate similarities of each pest and disease based on symptoms that are sent by the farmers through SMS. The corpus database usedin this study consists of 28.526 root words, 1.309 stop wordsand 180 words list. Pest and disease database reference in this study was obtained from the Ministry of Agriculture and Fisher (MAF) Timor-Leste. The result of the experiment shows that the system is able to identify the symptoms based on the keywords identified with the accuracy of 81%. The result of pest and disease identification has the accuracy of 86%. Keyword:Interactive text messaging Knowledge-based system Rice pests and diseases Text mining Timor Leste

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SMS-based Smarter Agriculture decision support system for yellow corn farmers in Isabela

Cited by 24 publications

References 2 publications

Application of TAMSAT-ALERT soil moisture forecasts for planting date decision support in Africa

Application of TAMSAT-ALERT soil moisture forecasts for planting date decision support in Africa

IOT-Based Cotton Whitefly Prediction Using Deep Learning

Text Mining for Pest and Disease Identification on Rice Farming with Interactive Text Messaging

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