Prediction of Strawberry Leaf Color Using RGB Mean Values Based on Soil Physicochemical Parameters Using Machine Learning Models

Abstract

Intensively grown strawberries in a greenhouse require frequent and precise soil physicochemical constituents for optimal production. Strawberry leaf color analyses are the most effective way to evaluate soil status and protect against excess environmental nutrients and financial setbacks. Meanwhile, precision agriculture (PA) endorsements have been utilized to mimic solutions to these problems. This research aimed to create machine learning models such as multiple linear regression (MLR) and gradient boost regression (GBR) for simulating strawberry leaf color changes related to soil physicochemical components and plant age using RGB (red, green, and blue) mean values. The soil physicochemical properties of the largest varied colored leaves of strawberry were precisely measured by a multifunctional soil sensor from the rooting zones. Simultaneously, 400 strawberry leaflets were detached in each vegetative and reproductive stage, and individual leaves were captured using a digital imaging system. The RGB mean values of colored images were extracted using the image segmentation algorithms of image processing technique. Consequently, MLR and GBR models were developed to predict leaf RGB mean values based on soil physicochemical measurements and plant age. The GBR model vigorously fitted with RGB mean values throughout the growth stage, with R2 and RMSE values of (R = 0.77, 7.16, G = 0.72, 7.37, and B = 0.70, 5.68), respectively. Furthermore, the MLR model performed moderately with R2 and RMSE values of (R = 0.67, 8.59, G = 0.57, 9.12, and B = 0.56, 6.81) when consecutively predicting RGB mean values in strawberry leaves. Eventually, the GBR model performed more effectively than the MLR model with high-performance metrics. In addition, the leaf color model uses visualization technology to measure growth progress, and it performs well in predicting dynamic changes in strawberry leaf color.

A Crop Growth Prediction Model Using Energy Data Based on Machine Learning in Smart Farms

“…e RF model could well estimate the paprika leaf growth and solar energy value that may relate to the relationship between sensors. Computersensor-based rules have aided the growth of paprika over other processes for estimating development-related attributes, which have yielded promising results [4].…”

“…The RF model could well estimate the paprika leaf growth and solar energy value that may relate to the relationship between sensors. Computer-sensor-based rules have aided the growth of paprika over other processes for estimating development-related attributes, which have yielded promising results [ 4 ]. This research area has two types: first, the models use crop training data; second, the models get energy sensor data in the field, where randomness because of non-linear data and cluttered environments was unavoidable, and the aim is to sectionalize sensor data to retrieve attributes, theoretically lowering the output [ 5 ].…”

Precision Agriculture Using Soil Sensor Driven Machine Learning for Smart Strawberry Production

“…Recent studies tried to predict the crop yield [ 16 , 19 , 20 , 34 ] and classify the quality of the harvest based on computer-vision-developed methods using machine learning algorithms [ 33 , 43 ]. One of the studies by Sim et al (2020) predicted the strawberry yield and strawberry growth stage using some environmental parameters (air temperature, soil temperature, and photosynthetic active radiation) and soil parameters (soil moisture content, EC, relative humidity, and CO 2 concentration) [ 44 ], while Madhavi et al [ 45 ] tried to evaluate the strawberry soil nutrition from the strawberry leaf color and predict the strawberry growth stage. Another study by ElMasry et al [ 43 ] developed multiple linear regression models to predict the sweetness and acidity of the strawberry crop using hyper-spectral imaging in the visible and near-infrared regions.…”

"Smartphone Application Determines Nitrate Concentration in Strawberries Leaves"

“…Leaf analysis is an effective approach to monitor the nutritional status of crop cultures and help to diagnose potential deficiencies [18]. NPK nutrients influence strawberry production, and its deficiencies decrease pigment formation causing subsequent leaf color changes from green to yellowish or purple [19]. As well they avoid fertilizer leaching causing soil contamination.…”