Artificial Neural Networks and Computer Vision’s-Based Phytoindication Systems for Variable Rate Irrigation Improving

Abstract

The article proposes a methodology for optimizing the process of irrigation of crops using a phytoindication system based on computer vision methods. We have proposed an algorithm and developed a system for obtaining a map of irrigation for maize in low latency mode. The system can be installed on a center pivot irrigation and consists of 8 IP cameras connected to a DVR connected to a laptop. The algorithm consists of three stages. Image preprocessing stage -applying an integrated excess green and excess red difference (ExGR) index. The classification stage is the application of the method that we choose depending on the operating conditions of the system. At the final stage, a neural network trained using the Resilient Propagation method is used, which determines the rate of watering of plants in the current sector of the location of the sprinkler. The selected methods of pretreatment and classification made it possible to achieve an accuracy of plant identification up to 93%, growth stages -up to 92% (with unconsolidated maize sowing and good lighting). System performance up to 100 plants in one second, which exceeds the performance of similar systems. The neural network showed an accuracy of 92% on the training set and 87% on the test set. Dynamic analysis of spatial and temporal variability leads to an increase in productivity and efficiency of water use. In addition, given the ubiquitous distribution of agribusiness management systems, this approach is quite simple to implement in the farm's conditions.

A Study on the Impact of Digital Finance on Regional Productivity Growth Based on Artificial Neural Networks

“…The interaction term between digital finance and technology level difference is negative (−0.0000984) and statistically significant at the 5% level; i.e., technology level difference shows a negative effect in the effect of digital finance on total factor productivity growth; the greater the technology level difference, the more negative the effect of the utility of digital finance on total factor productivity growth, but this negative effect is relatively small; it can be seen that the use of digital finance in China is relatively inefficient and the ability to learn new technologies is weak. The estimated coefficient of the effect of financial size on TFP growth is −0.177, which is statistically significant at the 1% level, suggesting that every 1 increase in financial size will contribute to a 0.177 decrease in TFP, possibly because state-owned enterprises receive most of the incremental financial resources due to implicit government guarantees, but do not use them effectively or even have idle funds, while the private economy, which operates more efficiently, faces a chronic shortage of capital, and the expansion of credit is also inflationary, leading to a recession [ 28 , 29 ].…”

Design of an efficient IoT based irrigation platform via fuzzy bioinspired multisensory analysis of on-field parameter sets

“…The models that suggest using Genetic Algorithm (GA) to improve Back Propagation Neural Network [36], and ANN with computer vision [37] for smart irrigation, are highly optimized for a variety of different crop type situations. However, these models have a poor irrigation accuracy, which makes it difficult to use them to real-time use scenarios [38].…”

Deep Learning Method for Recognition and Classification of Images from Video Recorders in Difficult Weather Conditions

“…Then, we needed to somehow score our algorithm. We decided to use the intersectionover-union metric [59][60][61][62][63] (Figure 3). However, we had "bad" photos, on which it was impossible to detect area, even for a human being.…”

“…Then, we needed to somehow score our algorithm. We decided to use the intersec tion-over-union metric [59][60][61][62][63] (Figure 3). The IoU is defined as .…”