Design and Implementation of Mobile Application for Crop Yield Prediction using Machine Learning

Meeradevi,; Salpekar, Hrishikesh

doi:10.1109/gcat47503.2019.8978315

Cited by 17 publications

(4 citation statements)

References 5 publications

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“…The methods, tools, and models of plant analysis for assessing the vegetation status of agricultural crops and production estimation, in relation to different influencing factors, have devolved over time depending on the knowledge level and available techniques [68][69][70]. Technological and IT facilities have been developed and diversified, so that they have been implemented with increasingly accessible equipment and devices that facilitate real-time evaluation and estimation of crops and yield, or certain quality parameters, based on physiological indices, or based on plant vegetation status (eco-physiological plants' properties) [71,72]. Agricultural crops' yield prediction is of interest and has been studied in relation to climatic and soil conditions, different crops, and agricultural technologies, yields, methods, and techniques of approach, etc.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Different Methods and Models for Grass Cereals’ Production Estimation: Case Study in Wheat

Sala

Herbei²

2023

Agronomy

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Adequate management of agricultural crops requires, among other things, accessible and sufficiently accurate methods for assessing plant nutrition and crop vegetation status and for agricultural production estimation. Sustainable technologies are based on correct decisions, prompt interventions and appropriate works, and correct information in real time, and the obtaining information methods can be simple, accessible, and appropriate in relation to different user categories (e.g., farmers, researchers, decision makers). This study used mineral fertilization (NPK), with 11 experimental variants, to ensure a controlled differentiated nutrition of the wheat plants, “Alex” cultivar. Regression analysis was used to obtain models in estimating wheat production, by methods based on: (a) NPK fertilizers applied (F) in the 11 experimental variants; (b) physiological indices (PI), represented by the chlorophyll content (Chl), and plant nutrition status on the experimental variants, in terms of macroelement content in the leaves, evaluated by foliar diagnosis (Nfd, Pfd, Kfd); (c) imaging analysis (IA) based on digital images of the wheat experimental variants, and calculated indices. A set of models was obtained, with different precision levels and statistical safety: R2 = 0.763, p = 0.013 for the model based on applied fertilizers (NPKF); R2 = 0.883, p < 0.01 for the model based on foliar diagnosis (NPKfd); R2 = 0.857, p < 0.01 for the model based on chlorophyll content (Chl); R2 = 0.975, p < 0.01 for the model based on normalized rgb color parameters (RGB color system); R2 = 0.925, p < 0.01 for the model based on the DGCI calculated index. The model based on applied fertilizers (F model) was tested in relation to wheat production data, for a period of six years, communicated by other studies. Fit degree analysis between predicted yield based on the F model and real yield (six-year average) was confirmed by R2 = 0.717, compared to R2 = 0.763 for the F model in this study. The models obtained in this study, related to the “Alex” wheat cultivar, can be used for other studies, but with a certain margin of error, given the coefficient values, specific to the obtained equations. The approach concept, methods, and models presented can be opportunities for other studies to facilitate their comparative analysis, their adaptation, and/or development in the form of new models that are useful in different studies, research, or agricultural practices, for their integration into crop management strategies.

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Section: Discussionmentioning

confidence: 99%

Evaluation of Different Methods and Models for Grass Cereals’ Production Estimation: Case Study in Wheat

Sala

Herbei²

2023

Agronomy

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“…This system is useful for smallholder farmers because it helps in the evaluation of the thing accomplished and the subsequent decision making. [5] An innovative application has been proposed to help farmers predict crops in specific areas based on climatic result such as rainfall. and temperature.…”

Section: IImentioning

confidence: 99%

Soil Fertility Detection For Crop Recommendation

2024

IJRASET

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Technological advances in agricultural process have changed from traditional methods to automated methods to achieve productive yield. Thus, suitable climatic and soil conditions are tested to maximize performance. Agricultural parameters are monitored to improvise the quality and quantity of agriculture. An important parameter is soil fertility, which is the proportion of nutrients in the soil necessary for cultivation. Determine the average percentage of essential nutrients (e.g. nitrogen, phosphorus, and potassium) and determine the appropriate crops for a given soil type. The proposed system analysis soil nutrient concentrations in real time and prepares a yield forecast. The system predicts humidity, humidity and temperature values based on historical data through sensors. Yield prediction involves many factors, including biological \properties, soil properties, and environmental conditions.

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“…Features: Rainfall, minimu [55] The paper proposes a mobile application that will allow farmers to predict the region's production for a speci c crop.…”

Section: Reference Yearmentioning

confidence: 99%

Machine Learning based Recommender Systems for Crop Selection: A Systematic Literature Review

Ommane

RHANBOURI

CHOUIKH

et al. 2022

Preprint

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Crop selection (CS) is one of the most critical elements that affects the final yield directly. As a result, selecting an appropriate crop is always a critical decision that a farmer must make, considering environmental factors. Choosing an appropriate crop for a given farm is a difficult decision including a plethora of variables that influence the final yield. Experts are frequently consulted to assist farmers with CS; but, as this alternative is time consuming and expensive, it is not available to many farms. The use of recommender systems (RSs) in agricultural management has recently brought some captivating and promising results. We propose a systematic literature review (SLR) in this article to find and provide the most relevant and high-quality publications ad- dressing the crop recommendation (CR) question. The core concept of this SLR is inspired from the guidelines of PRISMA 2020.The different CR approaches are discussed, as well as all the most important input features for recommendation, which are determined and classified. We also identified some of the biggest hurdles to using CR in agriculture. Besides, we made an inventory of the most used techniques for CR. Further, we made an inventory of evaluation criteria and evaluation approaches.

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Design and Implementation of Mobile Application for Crop Yield Prediction using Machine Learning

Cited by 17 publications

References 5 publications

Evaluation of Different Methods and Models for Grass Cereals’ Production Estimation: Case Study in Wheat

Evaluation of Different Methods and Models for Grass Cereals’ Production Estimation: Case Study in Wheat

Soil Fertility Detection For Crop Recommendation

Machine Learning based Recommender Systems for Crop Selection: A Systematic Literature Review

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