Comparing machine learning and deep learning classifiers for enhancing agricultural productivity: case study in Larache Province, Northern Morocco

Abstract

<p><span lang="EN-US">The agriculture sector in the Tangier-Tetouan-Al-Hoceima-Region (Northern Morocco) contributes a significant percentage to the national revenue. The Larache Province is at the regional forefront in agriculture terms due to its large irrigated areas. Golden-Gogi is a biological farm located in the Larache Province, and its objective is to produce organic crops. Besides climate change, this farm suffers from biotic factors such as snails and insects. These problems cause diseases in plants, resulting in massive crop production losses. Early detection of disease and biotic factors in plants is a difficult task for farmers, but it is now possible thanks to artificial intelligence. For that reason, we aim to contribute to this Province by comparing the well-known models in machine learning (ML) and deep learning (DL) used in early plant disease detection to specify the best-classifier in terms of detecting mint plant diseases. Mint plant is a major crop on the Golden-Gogi farm, and its dataset was collected from there. As per findings, DL classifiers outperform ML classifiers in disease detection. The best-classifier is DenseNet201, with high accuracy of 94.12%. Hence, the system using DenseNet201 offers a solution for farmers of this Province in making urgent decisions to avoid mint yield losses.</span></p>

Assessing the advancement of artificial intelligence and drones’ integration in agriculture through a bibliometric study

“…Assessing the advancement of artificial intelligence and drones' integration in … (Hicham Slimani) 879 fields, including agriculture [10], biomedicine [11], and business. However, it also invokes crucial ethical and practical considerations that must be addressed.…”

Predicting Daily Aerobiological Risk Level of Potato Late Blight Using C5.0 and Random Forest Algorithms under Field Conditions

“…In recent years, the agricultural sector was able to adopt the main technological innovations relying on artificial intelligence (AI), artificial neural networks (NN) and machine learning (ML). The goal is to digitize itself and increase the autonomy of many processes by making better data-driven decisions, reducing the workload, inputs and increase the quality of the final product [16][17][18][19][20]. The multi-view spectral information from unmanned aerial vehicles (UAV) based color-infrared images combined with machine learning algorithms was used to improve the estimation of nitrogen nutrition status in winter wheat and optimize the fertilization [17].…”