An Artificial Intelligence-Based Framework for Fruits Disease Recognition Using Deep Learning

Fruit infections have an impact on both the yield and the quality of the crop.As a result,an automated recognition system for fruit leaf diseases is important.In artificial intelligence(AI)applications,especially in agriculture,deep learning shows promising disease detection and classification results.The recent AI-based techniques have a few challenges for fruit disease recognition,such as low-resolution images,small datasets for learning models,and irrelevant feature extraction.This work proposed a new fruit leaf leaf leaf disease recognition framework using deep learning features and improved pathfinder optimization.Three fruit types have been employed in this work for the validation process,such as apple,grape,and Citrus.In the first step,a noisy dataset is prepared by employing the original images to learn the designed framework better.The EfficientNet-B0 deep model is fine-tuned on the next step and trained separately on the original and noisy data.After that,features are fused using a serial concatenation approach that is later optimized in the next step using an improved Path Finder Algorithm(PFA).This algorithm aims to select the best features based on the fitness score and ignore redundant information.The selected features are finally classified using machine learning classifiers such as Medium Neural Network,Wide Neural Network,and Support Vector Machine.The experimental process was conducted on each fruit dataset separately and obtained an accuracy of 100%,99.7%,99.7%,and 93.4%for apple,grape,Citrus fruit,and citrus plant leaves,respectively.A detailed analysis is conducted and also compared with the recent techniques,and the proposed framework shows improved accuracy.

Fruit Leaf Diseases Classification: A Hierarchical Deep Learning Framework

Manual inspection of fruit diseases is a time-consuming and costly because it is based on naked-eye observation.The authors present computer vision techniques for detecting and classifying fruit leaf diseases.Examples of computer vision techniques are preprocessing original images for visualization of infected regions,feature extraction from raw or segmented images,feature fusion,feature selection,and classification.The following are the major challenges identified by researchers in the literature:(i)lowcontrast infected regions extract irrelevant and redundant information,which misleads classification accuracy;(ii)irrelevant and redundant information may increase computational time and reduce the designed model’s accuracy.This paper proposed a framework for fruit leaf disease classification based on deep hierarchical learning and best feature selection.In the proposed framework,contrast is first improved using a hybrid approach,and then data augmentation is used to solve the problem of an imbalanced dataset.The next step is to use a pre-trained deep model named Darknet53 and fine-tune it.Next,deep transfer learning-based training is carried out,and features are extracted using an activation function on the average pooling layer.Finally,an improved butterfly optimization algorithm is proposed,which selects the best features for classification using machine learning classifiers.The experiment was carried out on augmented and original fruit datasets,yielding a maximum accuracy of 99.6%for apple diseases,99.6%for grapes,99.9%for peach diseases,and 100%for cherry diseases.The overall average achieved accuracy is 99.7%,higher than previous techniques.

An Integrated Deep Learning Framework for Fruits Diseases Classification

:Agriculture has been an important research area in the field of image processing for the last five years.Diseases affect the quality and quantity of fruits,thereby disrupting the economy of a country.Many computerized techniques have been introduced for detecting and recognizing fruit diseases.However,some issues remain to be addressed,such as irrelevant features and the dimensionality of feature vectors,which increase the computational time of the system.Herein,we propose an integrated deep learning framework for classifying fruit diseases.We consider seven types of fruits,i.e.,apple,cherry,blueberry,grapes,peach,citrus,and strawberry.The proposed method comprises several important steps.Initially,data increase is applied,and then two different types of features are extracted.In the first feature type,texture and color features,i.e.,classical features,are extracted.In the second type,deep learning characteristics are extracted using a pretrained model.The pretrained model is reused through transfer learning.Subsequently,both types of features are merged using the maximum mean value of the serial approach.Next,the resulting fused vector is optimized using a harmonic threshold-based genetic algorithm.Finally,the selected features are classified using multiple classifiers.An evaluation is performed on the PlantVillage dataset,and an accuracy of 99%is achieved.A comparison with recent techniques indicate the superiority of the proposed method.

Chemical Application for Prevention and Control of Post-harvest Citrus Diseases

[ Objective] To study the control efficiency of 45% Prochloraz amines ME and 500 g/L Triabendazole SC for the post-harvest diseases of citrus fruits, and the economic and efficient dosage and application techniques. [ Method] Carry out prevention and control test of common post-harvest diseases of tangerines, such as anthracnose, green mold and penicillium disease. [ Result] The experiment agent 45% Prochloraz amines ME showed excellent effect in controlling post- harvest anthracnose of citrus fruits, the 45t^-day control efficiency was above 73% ; the 45ts-day control efficiency of 45% Prochloras amines ME and 500 g/L Tri- abendazole SC for green mold and penicillium disease was above 72%. [ Conclusion] 45% Prochloraz amines ME and 500 g/L Triabendazole SC are two ideal agents for preventing and controlling post-harvest diseases and safeguarding quality of citrus fruits.

A Cascaded Design of Best Features Selection for Fruit Diseases Recognition

Fruit diseases seriously affect the production of the agricultural sector,which builds financial pressure on the country’s economy.The manual inspection of fruit diseases is a chaotic process that is both time and cost-consuming since it involves an accurate manual inspection by an expert.Hence,it is essential that an automated computerised approach is developed to recognise fruit diseases based on leaf images.According to the literature,many automated methods have been developed for the recognition of fruit diseases at the early stage.However,these techniques still face some challenges,such as the similar symptoms of different fruit diseases and the selection of irrelevant features.Image processing and deep learning techniques have been extremely successful in the last decade,but there is still room for improvement due to these challenges.Therefore,we propose a novel computerised approach in this work using deep learning and featuring an ant colony optimisation(ACO)based selection.The proposed method consists of four fundamental steps:data augmentation to solve the imbalanced dataset,fine-tuned pretrained deep learning models(NasNetMobile andMobileNet-V2),the fusion of extracted deep features using matrix length,and finally,a selection of the best features using a hybrid ACO and a Neighbourhood Component Analysis(NCA).The best-selected features were eventually passed to many classifiers for final recognition.The experimental process involved an augmented dataset and achieved an average accuracy of 99.7%.Comparison with existing techniques showed that the proposed method was effective.

Occurrence Regularity of Fruit Physiological Disease Spongy Tissue in Zihua Mango(Mangifera indica L.)

[ Objective ] The aim was to study the occurrence regularity of fruit physiological disease spongy tissue in Zihua mango （Mangifera indica L. ）. [ Meth. od] Main features of disease symptoms of Zihua mango fruit spongy tissue were investigated from 2002 to 2005 ,and the correlation between the incidence of Zihua mango fruit spongy tissue and its external factors （ fruit maturity, fruit size and fruit yield per plant） was analyzed comprehensively. [Result] The main features of disease symptoms appeared depressed cavity in the middle or lower part of fruit, forming spongy-like cavity. Immature fruits basically had no incidence. The dis- ease began to appear before 10 d of maturity. The disease incidence rate had extremely positive correlation with fruit weight, fruit vertical diameter or cross diame- ter. [ Conclusion] The research provides reference for field diagnoses, identification, preharvest and postharvest uninjurous test of fruit physiological disease suonaw tissue.

Pathogenesis, Prevention and Control of Coffee Black Fruit Disease ( Fusarium solani )

Coffee black fruit disease causes great harm to the yield and quality of coffee. Multiple factors, such as climate factors, insect pests, pathogen infection, physiological disorders, improper planting density, etc. , can cause coffee black fruit disease, which can be categorized into three types: pathogen infection type, physiological disorder type and insect pest type. Through the analysis of pathogenesis, the prevention and control methods and techniques of the disease corresponding to different types are put forward.