article · Journal of Food Quality
Visual inspection of crop diseases by eye is frequently imprecise and time-consuming. To address this, computer vision and artificial intelligence techniques were developed to classify plant leaf diseases using images from the PlantVillage dataset covering apple, corn, potato, tomato, and rice. The investigation compared two computational approaches. The first method augmented image data, extracted deep features using a convolutional neural network, and classified them with a Bayesian optimised support vector machine. The second method combined deep features with colour moments and texture metrics from grey-level co-occurrence matrices and histograms of oriented gradients. A binary particle swarm optimisation algorithm then reduced the hybrid feature set to the most relevant indicators before final classification with a random forest model. Both approaches were evaluated and compared across precision, sensitivity, f-score, and accuracy metrics.
Traditional manual disease inspection of crops is slow and prone to error, which can cause severe harvest losses. Applying automated computer vision to identify infections across multiple crop varieties provides a method to recognise damage early. This supports timely interventions, helping prevent irreversible crop failure and protecting agricultural yield from substantial economic harm.
The methodologies are aimed at digital agriculture tools that alert farmers to crop infections early. Tested entirely via simulations on standard benchmark dataset imagery for five crops, the research sits at an early, algorithmic stage. Substantial engineering, including deployment into mobile or edge-based imaging hardware and field trials, would be necessary before real-world adoption is viable.
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Plant diseases are unfavourable factors that cause a significant decrease in the quality and quantity of crops. Experienced biologists or farmers often observe plants with the naked eye for disease, but this method is often imprecise and can take a long time. In this study, we use artificial intelligence and computer vision techniques to achieve the goal of designing and developing an intelligent classification mechanism for leaf diseases. This paper follows two methodologies and their simulation outcomes are compared for performance evaluation. In the first part, data augmentation is performed on the PlantVillage data set images (for apple, corn, potato, tomato, and rice plants), and their deep features are extracted using convolutional neural network (CNN). These features are classified by a Bayesian optimized support vector machine classifier and the results attained in terms of precision, sensitivity, f-score, and accuracy. The above-said methodologies will enable farmers all over the world to take early action to prevent their crops from becoming irreversibly damaged, thereby saving the world and themselves from a potential economic crisis. The second part of the methodology starts with the preprocessing of data set images, and their texture and color features are extracted by histogram of oriented gradient (HoG), GLCM, and color moments. Here, the three types of features, that is, color, texture, and deep features, are combined to form hybrid features. The binary particle swarm optimization is applied for the selection of these hybrid features followed by the classification with random forest classifier to get the simulation results. Binary particle swarm optimization plays a crucial role in hybrid feature selection; the purpose of this Algorithm is to obtain the suitable output with the least features. The comparative analysis of both techniques is presented with the use of the above-mentioned evaluation parameters.
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DOI: 10.1155/2022/2845320
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