Weeds Identification Using Evolutionary Artificial Intelligence Algorithm

Abstract

In a world reached a population of six billion humans increasingly demand it for food, feed with a water shortage and the decline of agricultural land and the deterioration of the climate needs 1.5 billion hectares of agricultural land and in case of failure to combat pests needs about 4 billion hectares. Weeds represent 34% of the whole pests while insects, diseases and the deterioration of agricultural land present the remaining percentage. Weeds Identification has been one of the most interesting classification problems for Artificial Intelligence (AI) and image processing. The most common case is to identify weeds within the field as they reduce the productivity and harm the existing crops. Success in this area results in an increased productivity, profitability and at the same time decreases the cost of operation. On the other hand, when AI algorithms combined with appropriate imagery tools may present the right solution to the weed identification problem. In this study, we introduce an evolutionary artificial neural network to minimize the time of classification training and minimize the error through the optimization of the neuron parameters by means of a genetic algorithm. The genetic algorithm, with its global search capability, finds the optimum histogram vectors used for network training and target testing through a fitness measure that reflects the result accuracy and avoids the trial-and-error process of estimating the network inputs according to the histogram data.

Advancing sustainability in the food and nutrition system: a review of artificial intelligence applications

“…AI and image processing have made significant strides in addressing the challenge of weed identification, as demonstrated in studies ( 36–38 ). These previous studies conclude AI models, particularly Support Vector Machines (SVM), are effective in determining optimal nitrogen application rates and excel at early stress identification during crop growth, highlighting SVM’s potential for enhancing crop yield with timely interventions ( 38 ).…”

Big Data and AI Revolution in Precision Agriculture: Survey and Challenges

“…Even though agriculture practice is broad, some major areas of the agriculture sector where AI finds its application such as supply chain management, soil, crop, diseases, and pest management. [8] summary all the proposed models using AI techniques with their limitations (a) for soil management: Fuzzy-logic based SRC-DSS (Soil Risk Characterization Decision Support System) [9] for soil classification, MOM (Management-oriented modeling) [10] for minimization of nitrate leaching, ANN (artificial neural network) [11] to estimate soil enzyme activity and soil structure classification, etc., (b) for crop management: CALEX [12] to formulate scheduling guidelines, PROLOG [13] to remove redundant tools from the farm, ANN [14] to detect nutrition disorders in crops, ANN [15] to predict rice yield accurately, etc., (c) for disease management: computer vision system (CVS) [16] to detect multiple diseases at high speed, Fuzzy logic based database [16] are accurate in test environments, ANN-GIS [17] has got an accuracy of 90%, the expert system using rule-base in disease detection [18] for faster detection and treatment of disease, etc., (d) for weed control: invasive weed optimization (IWO) [19], big data abased ANN-GA [20], support vector machine [21], etc. All these methods did not consider all the parameters; they are all application-specific towards a particular crop or environmental parameter.…”

A Novel Angular Texture Pattern (ATP) Extraction Method for Crop and Weed Discrimination Using Curvelet Transformation

“…The usage amount of herbicides was reduced, due to the localized spraying of the infected areas and efficient recognition of weeds. Tobal and Mokhtar [21] introduced an evolutionary Artificial NN (ANN) to minimize the time of classification training and error through the optimization of the neuron parameters. The classification accuracy was improved while avoiding the trial-and-error process of estimating the network inputs according to the histogram data.…”