Plant Leaf Disease Detection and Automatic Pesticide Recommendation Using Deep Learning

Abstract: Defoliating plant diseases are critical and still remains a significant threat on the world’s agricultural production,foodsecurityandeconomies.Theoldermodelsfor disease detection and diagnosis are inefficient,error-prone,and labor extensive, allowing for delays in action and mistimed pesticide application. Recent improvements in deep learning (DL)andimageprocessingtechnologiesappeartoallowtheuse ofautomated plant disease detectionsystems that canassist in recommending suitable and more effective pesticides. This work assesses various deep learning methods for their capabilities for plant leaf diseases detection, focusing on their functionality, accuracy and ease of use in real field setting. It also focuses on the methods for implementing the automatic recommendation systems interpolating between CNN and machine learning methodologies. It was shown that the most effective architectures are implemented on the basis of CNN, giving the best results in disease diagnosis precision. Also, the hybrid methods allowcombining the recommendation for the use of pesticides. A number of possible avenues for research aimed at enhancing efficiency of the models in real time field application are outlined.