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.
Highlights
Smart farming helps farmers plan their work with the data obtained with agricultural drones, satellites and sensors
Image acquisition using handheld cameras under different lighting conditions; approaches employing image segmentation techniques; identification of features with various descriptors; improving the classification rate with deep learning models; achieving high accuracy and reducing the error rates; the essential challenges to be tackled in the future
deep learning (DL) classifiers were used in a wide range of agricultural applications with an average performance F1 score of 0.8
Summary
Smart farming helps farmers plan their work with the data obtained with agricultural drones, satellites and sensors. To safeguard from the risks inherent to agriculture, the Ministry of Agriculture and Farmers Welfare announced an insurance scheme for crops in 1985. Problems have emerged in the scheme technology to collect data and lessen the delays in responding to insurance claims by the farmers. Crop yield estimation is mandatory for this and are recorded by conducting Crop Cutting Experiments (CCE) conducted in regions of the states by the Government of India. The directorate of Economics and Statistics is presently guiding Crop Cutting Experiments for 13 chief crops under the General Crop Estimation Scheme. To improve the quality of statistics collection of Crop Cutting Experiments, Global Positioning System (GPS) data such as elevation of fields, area, latitude and longitude are being recorded by remote sensing [1,2]. The vegetation indices acquired through the satellite images track the phenological profiles of the crops throughout the year [3,4]
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