Novel Image Processing Technique for Feature Detection of Wheat Crops using Python OpenCV

Rashid, Mamoon; Ram, Balwant; Batth, Ranbir Singh; Aḥmad, Naẕīr; Dafallaa, Hatim M. Elhassan Ibrahim; Rehman, Mohammed Burhanur

doi:10.1109/iccike47802.2019.9004432

Cited by 6 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The image processing capabilities of OpenCV were applied in various real applications, mainly in the agricultural field. OpenCV is essential in the various characteristics of any agricultural undertakings [29], [30].…”

Section: Introductionmentioning

confidence: 99%

Soil parameter detection of soil test kit-treated soil samples through image processing with crop and fertilizer recommendation

Montanez¹

2021

IJEECS

View full text Add to dashboard Cite

<span>Standard laboratory soil testing is deemed to be expensive and time-consuming. Utilizing a soil test kit is considered to be a cost-efficient and time-saving way of soil testing. This project study aims to develop a prototype that detects soil parameters (i.e., soil pH, nitrogen, phosphorus, and potassium) and gives crop and fertilizer recommendations after the soil sample has undergone a soil treatment test kit and its acceptability for possible users. The prototype development primarily used image processing to detect the needed parameters that lead to crop and fertilizer recommendations. In the evaluation of the effectiveness of the prototype, 50 trials were conducted per parameter. All of the said parameters were recorded as highly effective except for nitrogen Low, which is interpreted as effective only. There were 30 possible users invited to assess the acceptability of the prototype. A survey based on the technology acceptance model was administered to the 30 respondents garnering a 4.85 weighted mean interpreted as excellent. The prototype was proven effective and accepted as a device that can detect soil pH and primary macronutrient levels. It gives the appropriate crop and fertilizer recommendations based on the gathered data.</span>

show abstract

Section: Introductionmentioning

confidence: 99%