Artificial Intelligence Approach for Tomato Detection and Mass Estimation in Precision Agriculture

Lee, Jae-Su; Nazki, Haseeb; Baek, Jeonghyun; Hong, Youngsin; Lee, Meonghun

doi:10.3390/su12219138

Cited by 52 publications

(32 citation statements)

References 40 publications

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“…Therefore, this paper also conducted a research on the relationship between photo number n and model accuracy, hoping to get satisfactory results with smaller n value. The volume values of tomato samples numbered 101~200 were measured again, due to the particularity of the wireframe model, n was set to n [5,15], and the data source of the BPNN came from tomato samples numbered 1~90 when n [1,15], and the obtained model accuracy is shown in Fig. 10.…”

Section: Influence Of Photo Number On the Prediction Results Of The Two Modelsmentioning

confidence: 99%

“…Automatic grading of fruits and vegetables can reduce work intensity, improve grading accuracy, and increase the value of agricultural products. It is an important link in the commercialization of agricultural products [1]. Tomatoes are one of the world's most important mass production and consumption agricultural products [2].…”

Section: Introductionmentioning

confidence: 99%

“…In China, approximately 60 million tons of tomatoes are produced each year. Since consumers often judge the quality of tomatoes by surface characteristics such as color, size, and shape [3][4], many experts and scholars have carried out relevant research on automatic tomato grading, and most of their studies focus on using machine vision to detect the ripeness, color, size, and surface defects of tomatoes [1,5]. Among them, the size of tomatoes is an important factor affecting consumers to buy tomatoes, and the volume of tomatoes is the most intuitive way to describe the size of tomatoes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Tomato Volume Measurement Method based on Machine Vision

Sun

Liu

et al. 2021

Teh. vjesn.

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Density is one of the auxiliary indicators for judging the internal quality of tomatoes. However, in the density measurement process, it is often difficult to measure the volume of the tomatoes accurately. To solve this problem, first, this study proposed a novel tomato volume measurement method based on machine vision. The proposed method uses machine vision to measure the geometric feature parameters of tomatoes, and inputs them into the LabVIEW software to convert the calculation of irregular tomato volume into a BP neural network (BPNN) model that calculates the plane pixel area and pixel volume, thereby realizing the modeling, analysis, design and simulation of tomato volume; then, an experimental platform was constructed to compare the results of the proposed method with the results predicted by the 3D wireframe model. When the number of photos taken was n = 5, the average error of the tomato volume prediction results of the 3D wireframe model was 8.22%, and the highest accuracy was 92.93%; while the average error of the tomato volume prediction results of the BPNN was 4.60%, and the highest accuracy was 95.60%. Increasing the number of orthographic projections can improve the accuracy of the model, but when the number of photos was more than 7, the accuracy improvement was not significant. Also, increasing the number of nodes in the hidden layer can improve the accuracy of the model, however, considering that increasing the number of nodes will increase the host operating cost, it is suggested to choose a node number of 12 for the tomato volume measurement. In the end, the final experimental results showed that the proposed method achieved better measurement results. However, the volume measured by the two models is larger than the real volume of tomatoes. For this reason, we added a correction coefficient to the BPNN model, and its highest accuracy has increased by 1.3%.

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Section: Influence Of Photo Number On the Prediction Results Of The Two Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Tomato Volume Measurement Method based on Machine Vision

Sun

Liu

et al. 2021

Teh. vjesn.

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“…The InceptionV3 model assists as a conventional image feature extractor to classify fruit and background pixels in an image. The classifier localizes the fruits to count the quantities of fruit present [31,92] and classify the species of tomato [93]. A K-nearest neighbour (KNN) classifier was employed to classify the fruit pixels in trained datasets with a threshold pixel value set as a fruit pixel.…”

Section: Deep Architectures In Smart Farmingmentioning

confidence: 99%

Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

et al. 2021

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Precision agriculture is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in the agricultural industry moderates the usage of resources and can increase the quality of food in the post-pandemic world. Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and harvesting. Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation. Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation. The automation in image analysis with computer vision and deep learning models provides precise field and yield maps. In this review, it has been observed that the application of deep learning techniques has provided a better accuracy for smart farming. The crops taken for the study are fruits such as grapes, apples, citrus, tomatoes and vegetables such as sugarcane, corn, soybean, cucumber, maize, wheat. The research works which are carried out in this research paper are available as products for applications such as robot harvesting, weed detection and pest infestation. The methods which made use of conventional deep learning techniques have provided an average accuracy of 92.51%. This paper elucidates the diverse automation approaches for crop yield detection techniques with virtual analysis and classifier approaches. Technical hitches in the deep learning techniques have progressed with limitations and future investigations are also surveyed. This work highlights the machine vision and deep learning models which need to be explored for improving automated precision farming expressly during this pandemic.

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“…In a work by Ganjloo et al [2], image processing techniques are used for mass estimation and shape through geometrical features and decision treebased analysis. An investigation based on image processing and artificial intelligence models is proposed by Lee et al [3] for mass estimation of fruits, bagged ensemble tree regressors were adopted for prediction via correlation study of fruit image. Regression-based models are developed by Okinda et al [4] for the prediction of egg volume, and shape-based information extracted are employed as Regression model inputs.…”

Section: Introductionmentioning

confidence: 99%

Mass estimation of citrus limetta using distance based hand crafted features and regression analysis

Rani

Krishna

Laxmisagara

et al. 2022

IJECE

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<span>Sorting and grading are qualitative operational tasks performed in food processing industries. Realization of higher accuracy in mass estimation is the key inclination of this work. In this work, an automated technique for mass estimation of citrus limetta is devised based on the geometrical features derived from pre-processed images. Dataset includes 250 data samples of citrus limetta, whose images are acquired in different orientations. Two novel handcrafted distance-based geometrical features along with four conventional geometrical features were employed for regression analysis. Predictive modeling is conducted with configuration of 150 training and 100 testing data samples and subject to regression analysis for mass estimation. Multiple linear and support vector regression models with linear, polynomial and radial basis function (RBF) kernels were employed for mass estimation with two different model configurations, conventional and conventional with handcrafted features, for which an R2 score of 0.9815, root mean squared error (RMSE) of 10.94 grams, relative averages of accuracy and error of 96.61% and 3.39% respectively is achieved for the proposed model and configuration which was validated using k-fold cross-validation. Through comparison with performance of model with conventional and conventional with handcrafted features configurations, it was established that inclusion of handcrafted features was able to increase the performance of the models.</span>

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Artificial Intelligence Approach for Tomato Detection and Mass Estimation in Precision Agriculture

Cited by 52 publications

References 40 publications

A Novel Tomato Volume Measurement Method based on Machine Vision

A Novel Tomato Volume Measurement Method based on Machine Vision

Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

Mass estimation of citrus limetta using distance based hand crafted features and regression analysis

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