Deep Learning-Based Growth Prediction System: A Use Case of China Agriculture

Abstract

Agricultural advancements have significantly impacted people’s lives and their surroundings in recent years. The insufficient knowledge of the whole agricultural production system and conventional ways of irrigation have limited agricultural yields in the past. The remote sensing innovations recently implemented in agriculture have dramatically revolutionized production efficiency by offering unparalleled opportunities for convenient, versatile, and quick collection of land images to collect critical details on the crop’s conditions. These innovations have enabled automated data collection, simulation, and interpretation based on crop analytics facilitated by deep learning techniques. This paper aims to reveal the transformative patterns of old Chinese agrarian development and fruit production by focusing on the major crop production (from 1980 to 2050) taking into account various forms of data from fruit production (e.g., apples, bananas, citrus fruits, pears, and grapes). In this study, we used production data for different fruits grown in China to predict the future production of these fruits. The study employs deep neural networks to project future fruit production based on the statistics issued by China’s National Bureau of Statistics on the total fruit growth output for this period. The proposed method exhibits encouraging results with an accuracy of 95.56% calculating by accuracy formula based on fruit production variation. Authors further provide recommendations on the AGR-DL (agricultural deep learning) method being helpful for developing countries. The results suggest that the agricultural development in China is acceptable but demands more improvement and government needs to prioritize expanding the fruit production by establishing new strategies for cultivators to boost their performance.

Spatial heterogeneity of soil nutrients in black soil areas of Northeast China

“…The CV values could be used to assign the parameters to one of three levels of variability (B. Liu et al., 2021). Namely low (CV < 10%), moderate (10% ≤ CV < 100%), or high (100% ≤ CV) (Khan et al., 2021; J. Zhang et al., 2018). The CV of nutrients in study ranged from 5 to 80% (Table 1).…”

“…Liu et al, 2021). Namely low (CV < 10%), moderate (10% ≤ CV < 100%), or high (100% ≤ CV) (Khan et al, 2021;J. Zhang et al, 2018).…”

Suitability Evaluation of Crop Variety via Graph Neural Network

“…They propose AgroAVNET, a hybrid model based on AlexNet and VGGNET, with a extensive performance improvement compared to existing methods. Literature [ 14 ] is dedicated to using past agricultural production data to predict future agricultural production. The authors propose a deep learning model AGR-DL based on CNN and RNN.…”

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

“…Fruit plantation is one of the most important agricultural activities. The production and protection of fruit per capita has recently been considered an essential indicator of a country's growth and quality of life [1]. Population growth of 7.2 to 9.6 billion people is expected by 2100.…”