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

Abstract

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.

Technologies for Forecasting Tree Fruit Load and Harvest Timing—From Ground, Sky and Time

“…Machine vision has been used in the estimation of flowering level in several crop types, including apple, almond and mango [38,40,45,[113][114][115][116]. Darwin et al [117] provide a short review of the deep learning models used in the recognition of flowers across all crop types. Considerable work has been undertaken for the estimation of flowering load in apple, although the focus of this work is the control of flower thinning methods, which is undertaken to regulate fruit size, rather than as input to a harvest maturation time model.…”

Improving Wheat Yield Prediction Accuracy Using LSTM-RF Framework Based on UAV Thermal Infrared and Multispectral Imagery

“…With the rapid development of sensors, the volume of data acquired has become larger, requiring powerful tools to establish relationships between remote sensing data and actual plant parameters. Machine learning algorithms have developed rapidly in recent years and are widely used in precision agriculture for the evaluation of crop parameters with desirable model performance [13][14][15][16]. Random forest (RF) is an integrated treebased algorithm that achieves high prediction accuracy in the evaluation of parameters such as crop chlorophyll and biomass [17,18].…”

Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review

“…Various implementations of deep learning in agriculture approaches have been extensively reviewed in recent years as proposed in [5,37,[76][77][78][79]. Among those, Koirala et al [77] reviewed the application of deep learning in fruit detection and yield estimation, Zhang et al [80] explore dense scene analysis of the application deep learning in agriculture and Moazzam et al [79] emphasized the challenges of weed and crop classification using deep learning.…”