Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review

In Agriculture, orchards are the deciding factor in the country’s economy. There are many orchards, and citrus and sugarcane will cover 60 percent of them. These citrus orchards satisfy the necessity of citrus fruits and citrus products, and these citrus fruits contain more vitamin C. The citrus orchards have had some problems generating good yields and quality products. Pathogenic diseases, pests, and water shortages are the three main problems that plants face. Farmers can find these problems early on with the support of machine learning and deep learning, which may also change how they feel about technology.  By doing this in agriculture, the farmers can cut off the major issues of yield and quality losses. This review gives enormous methods for identifying and classifying plant pathogens, pests, and water stresses using image-based work. In this review, the researchers present detailed information about citrus pathogens, pests, and water deficits. Methods and techniques that are currently available will be used to validate the problem. These will include pre-processing for intensification, segmentation, feature extraction, and selection processes, machine learning-based classifiers, and deep learning models. In this work, researchers thoroughly examine and outline the various research opportunities in the field. This review provides a comprehensive analysis of citrus plants and orchards; Researchers used a systematic review to ensure comprehensive coverage of this topic

A Survey on Sugarcane Leaf Disease Identification Using Deep Learning Technique(CNN)

The management of plant diseases is vital for the economical production of food and poses important challenges to the employment of soil, water, fuel and alternative inputs for agricultural functions. In each natural and cultivated populations, plants have inherent sickness tolerance, however there also are reports of devastating impacts of plant diseases. The management of diseases, however, within reason effective for many crops. sickness management is allotted through the employment of plants that square measure bred permanently resistance to several diseases and thru approaches to plant cultivation, like crop rotation, the employment of pathogen-free seeds, the given planting date and plant density, field wetness management, and therefore the use of pesticides. so as to enhance sickness management and to stay up with changes within the impact of diseases iatrogenic by the continued evolution and movement of plant pathogens and by changes in agricultural practices, continued progress within the science of soil science is required. Plant diseases cause tremendous economic losses for farmers globally. it's calculable that in additional developed settings across massive regions and lots of crop species, diseases usually cut back plant yields by ten percent per annum, however yield loss for diseases usually exceeds twenty percent in less developed settings. Around twenty-five percent of crop losses square measure caused by pests and diseases, the Food and Agriculture Organization estimates. to unravel this, new strategies for early detection of diseases and pests square measure required, like novel sensors that sight plant odours and spectrographic analysis and bio photonics that may diagnose plant health and metabolism. In artificial neural networks, deep learning is an element of a broader family of machine learning approaches supported realistic learning. Learning is often controlled, semi-supervised or unmonitored. to handle several real-world queries, Deep Learning Approaches are normally used. so as to differentiate pictures and acknowledge their options, coevolutionary neural networks have had a larger result. This article will do a Leaf Disease Identification Survey with Deep Learning Methods. It takes Sugarcane leaf as an instance to our paper

Optimizing DUS testing for Chimonanthus praecox using feature selection based on a genetic algorithm

Chimonanthus praecox is a famous traditional flower in China with high ornamental value. It has numerous varieties, yet its classification is highly disorganized. The distinctness, uniformity, and stability (DUS) test enables the classification and nomenclature of various species; thus, it can be used to classify the Chimonanthus varieties. In this study, flower traits were quantified using an automatic system based on pattern recognition instead of traditional manual measurement to improve the efficiency of DUS testing. A total of 42 features were quantified, including 28 features in the DUS guidelines and 14 new features proposed in this study. Eight algorithms were used to classify wintersweet, and the random forest (RF) algorithm performed the best when all features were used. The classification accuracy of the outer perianth was the highest when the features of the different parts were used for classification. A genetic algorithm was used as the feature selection algorithm to select a set of 22 reduced core features and improve the accuracy and efficiency of the classification. Using the core feature set, the classification accuracy of the RF model improved to 99.13%. Finally, K-means was used to construct a pedigree cluster tree of 23 varieties of wintersweet; evidently, wintersweet was clustered into a single class, which can be the basis for further study of genetic relationships among varieties. This study provides a novel method for DUS detection, variety identification, and pedigree analysis

Methods for Detecting and Classifying Weeds, Diseases and Fruits Using AI to Improve the Sustainability of Agricultural Crops: A Review

The rapid growth of the world’s population has put significant pressure on agriculture to meet the increasing demand for food. In this context, agriculture faces multiple challenges, one of which is weed management. While herbicides have traditionally been used to control weed growth, their excessive and random use can lead to environmental pollution and herbicide resistance. To address these challenges, in the agricultural industry, deep learning models have become a possible tool for decision-making by using massive amounts of information collected from smart farm sensors. However, agriculture’s varied environments pose a challenge to testing and adopting new technology effectively. This study reviews recent advances in deep learning models and methods for detecting and classifying weeds to improve the sustainability of agricultural crops. The study compares performance metrics such as recall, accuracy, F1-Score, and precision, and highlights the adoption of novel techniques, such as attention mechanisms, single-stage detection models, and new lightweight models, which can enhance the model’s performance. The use of deep learning methods in weed detection and classification has shown great potential in improving crop yields and reducing adverse environmental impacts of agriculture. The reduction in herbicide use can prevent pollution of water, food, land, and the ecosystem and avoid the resistance of weeds to chemicals. This can help mitigate and adapt to climate change by minimizing agriculture’s environmental impact and improving the sustainability of the agricultural sector. In addition to discussing recent advances, this study also highlights the challenges faced in adopting new technology in agriculture and proposes novel techniques to enhance the performance of deep learning models. The study provides valuable insights into the latest advances and challenges in process systems engineering and technology for agricultural activities

Classification of Citrus Plant Diseases Using Deep Transfer Learning

In recent years, the field of deep learning has played an important role towards automatic detection and classification of diseases in vegetables and fruits. This in turn has helped in improving the quality and production of vegetables and fruits. Citrus fruits are well known for their taste and nutritional values. They are one of the natural and well known sources of vitamin C and planted worldwide. There are several diseases which severely affect the quality and yield of citrus fruits. In this paper, a new deep learning based technique is proposed for citrus disease classification. Two different pre-trained deep learning models have been used in this work. To increase the size of the citrus dataset used in this paper, image augmentation techniques are used. Moreover, to improve the visual quality of images, hybrid contrast stretching has been adopted. In addition, transfer learning is used to retrain the pre-trained models and the feature set is enriched by using feature fusion. The fused feature set is optimized using a meta-heuristic algorithm, the Whale Optimization Algorithm (WOA). The selected features are used for the classification of six different diseases of citrus plants. The proposed technique attains a classification accuracy of 95.7% with superior results when compared with recent techniques

Automated assessment for early and late blight leaf diseases using extended segmentation and optimized features

Early and late blight diseases lead to substantial damage to vegetable crop productions and economic losses. As a modern solution, machine learning-based plant disease assessment aims to assess the disease incidence and severity through the disease region of interest (ROI) and its extracted features. In the case of existing conventional classifier methods, extracting the features involves generalized ROI segmentation that loosely follows the disease inference. As a result, accuracy is reduced, and the fuzzy boundary region that carries potential properties for improving feature characterization capability is truncated from the ROI. Besides, most of the existing practices extract only the global features, This leads to redundant and extensive feature vector, which causes increased complexity and underperformance. Furthermore, individual lesion severity is not considered in the assessment. This thesis addresses the issue of the ROI segmentation by using color thresholding based on ratios of leaf green color intensity to incorporate the fuzzy boundary region, denoted as extended ROI (EROI). Secondly, the issue of the feature extraction is addressed by the proposed localized feature extraction method to reduce complexity and improve disease classification performance. Based on the color and texture morphological properties of the individual lesions within the EROI, color coherence vector and local binary patterns features are extracted. As a result, a pathologically optimized feature vector is obtained, which is used to build a support vector machine classifier to classify between the disease types of early blight, late blight, and healthy leaves. lastly, a 2-tier assessment is proposed. The disease type classification is given as the first tier, while the leaf lesion area ratios of the individual lesions are given as severity quantification for the second tier. Overall, the proposed EROI segmentation method reduced under-segmentation by up to 80%. The proposed optimized feature reduced the execution run-time by up to 50% and achieved an average classification performance of up to 99%. Finally, the quantified severity is in close agreement with the ground truth by achieving an average accuracy of 93%

Finding spectral features for the early identification of biotic stress in plants

Early detection of biotic stress in plants is vital for precision crop protection, but hard to achieve. Prediction of plant diseases or weeds at an early stage has significant influence on the extent and effectiveness of crop protection measures. The precise measure depends on specific weeds and plant diseases and their economic thresholds. Weeds and plant diseases at an early stage, however, are difficult to identify. Non-invasive optical sensors with high resolution are promising for early detection of biotic stress. The data of these sensors, e.g. hyperspectral or fluorescence signatures, contain relevant information about the occurrence of pathogens. Shape parameters, derived from bispectral images, have enormous potential for an early identification of weeds in crops. The analysis of this high dimensional data for an identification of weeds and pathogens as early as possible is demanding as the sensor signal is affected by many influencing factors. Nevertheless, advanced methods of machine learning facilitate the interpretation of these signals. Whereas traditional statistics estimate the posterior probability of the class by probability distribution, machine learning methods provide algorithms for optimising prediction accuracy by the discriminant function. Machine learning methods with robust training algorithms play a key role in handling non-linear classification problems. This thesis presents an approach which integrates modern sensor techniques and advanced machine learning methods for an early detection and differentiation of plant diseases and weeds. Support vector machines (SVMs) equipped with non-linear kernels prove as effective and robust classifiers. Furthermore, it is shown that even a presymptomatic identification based on the combination of spectral vegetation indices is realised. Using well-established data analysis methods of this scientific field, this has not achieved so far. Identifying disease specific features from the underlying original high dimensional sensor data selection is conducted. The high dimensionality of data affords a careful selection of relevant and non-redundant features depending on classification problem and feature properties. In the case of fluorescence signatures an extraction of new features is necessary. In this context modelling of signal noise by an analytical description of the spectral signature improves the accuracy of classification substantially. In the case of weed discrimination accuracy is improved by exploiting the hierarchy of weed species. This thesis outlines the potential of SVMs, feature construction and feature selection for precision crop protection. A problem-specific extraction and selection of relevant features, in combination with task-oriented classification methods, is essential for robust identification of pathogens and weeds as early as possible.Früherkennung von biotischem Pflanzenstress ist für den Präzisionspflanzenschutz wesentlich, aber schwierig zu erreichen. Die Vorhersage von Pflanzenkrankheiten und Unkräutern in einem frühen Entwicklungsstadium hat signifikanten Einfluss auf das Ausmaß und die Effektivität einer Pflanzenschutzmaßnahme. Aufgrund der Abhängigkeit einer Maßnahme von der Art der Pflanzenkrankheit oder des Unkrauts und ihrer ökonomischer Schadschwelle ist eine präzise Identifizierung der Schadursache essentiell, aber gerade im Frühstadium durch die Ähnlichkeit der Schadbilder problematisch. Nicht-invasive optische Sensoren mit hoher Auflösung sind vielversprechend für eine Früherkennung von biotischem Pflanzenstress. Daten dieser Sensoren, beispielsweise Hyperspektral- oder Fluoreszenzspektren, enthalten relevante Informationen über das Auftreten von Pathogenen; Formparameter, abgeleitet aus bispektralen Bildern, zeigen großes Potential für die Früherkennung von Unkräutern in Kulturpflanzen. Die Analyse dieser hochdimensionalen Sensordaten unter Berücksichtigung vielfältiger Faktoren ist eine anspruchsvolle Herausforderung. Moderne Methoden des maschinellen Lernens bieten hier zielführende Möglichkeiten. Während die traditionelle Statistik die a-posteriori Wahrscheinlichkeit der Klasse basierend auf Wahrscheinlichkeitsverteilungen schätzt, verwenden maschinelle Lernverfahren Algorithmen für eine Optimierung der Vorhersagegenauigkeit auf Basis diskriminierender Funktionen. Grundlage zur Bearbeitung dieser nicht-linearen Klassi kationsprobleme sind robuste maschinelle Lernverfahren. Die vorliegende Dissertationsschrift zeigt, dass die Integration moderner Sensortechnik mit fortgeschrittenen Methoden des maschinellen Lernens eine Erkennung und Differenzierung von Pflanzenkrankheiten und Unkräutern ermöglicht. Einen wesentlichen Beitrag für eine effektive und robuste Klassifikation leisten Support Vektor Maschinen (SVMs) mit nicht-linearen Kernels. Weiterhin wird gezeigt, dass SVMs auf Basis spektraler Vegetationsindizes die Detektion von Pflanzenkrankheiten noch vor Auftreten visuell wahrnehmbarer Symptome ermöglichen. Dies wurde mit bekannten Verfahren noch nicht erreicht. Zur Identifikation krankheitsspezifischer Merkmale aus den zugrunde liegenden originären hochdimensionalen Sensordaten wurden Merkmale konstruiert und selektiert. Die Selektion ist sowohl vom Klassifikationsproblem als auch von den Eigenschaften der Merkmale abhängig. Im Fall von Fluoreszenzspektren war eine Extraktion von neuen Merkmalen notwendig. In diesem Zusammenhang trägt die Modellierung des Signalrauschens durch eine analytische Beschreibung der spektralen Signatur zur deutlichen Verbesserung der Klassifikationsgenauigkeit bei. Im Fall der Differenzierung von unterschiedlichen Unkräutern erhöht die Ausnutzung der Hierarchie der Unkrautarten die Genauigkeit signifikant. Diese Arbeit zeigt das Potential von Support Vektor Maschinen, Merkmalskonstruktion und Selektion für den Präzisionspflanzenschutz. Eine problemspezifische Extraktion und Selektion relevanter Merkmale in Verbindung mit sachbezogenen Klassifikationsmethoden ermöglichen eine robuste Identifikation von Pathogenen und Unkräutern zu einem sehr frühen Zeitpunkt

Non-Destructive Technologies for Detecting Insect Infestation in Fruits and Vegetables under Postharvest Conditions: A Critical Review

In the last two decades, food scientists have attempted to develop new technologies that can improve the detection of insect infestation in fruits and vegetables under postharvest conditions using a multitude of non-destructive technologies. While consumers\u27 expectations for higher nutritive and sensorial value of fresh produce has increased over time, they have also become more critical on using insecticides or synthetic chemicals to preserve food quality from insects\u27 attacks or enhance the quality attributes of minimally processed fresh produce. In addition, the increasingly stringent quarantine measures by regulatory agencies for commercial import-export of fresh produce needs more reliable technologies for quickly detecting insect infestation in fruits and vegetables before their commercialization. For these reasons, the food industry investigates alternative and non-destructive means to improve food quality. Several studies have been conducted on the development of rapid, accurate, and reliable insect infestation monitoring systems to replace invasive and subjective methods that are often inefficient. There are still major limitations to the effective in-field, as well as postharvest on-line, monitoring applications. This review presents a general overview of current non-destructive techniques for the detection of insect damage in fruits and vegetables and discusses basic principles and applications. The paper also elaborates on the specific post-harvest fruit infestation detection methods, which include principles, protocols, specific application examples, merits, and limitations. The methods reviewed include those based on spectroscopy, imaging, acoustic sensing, and chemical interactions, with greater emphasis on the noninvasive methods. This review also discusses the current research gaps as well as the future research directions for non-destructive methods\u27 application in the detection and classification of insect infestation in fruits and vegetables

KLASIFIKASI PENYAKIT DAUN APEL MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK

: Apel adalah salah satu buah paling produktif di dunia. Namun, berbagai penyakit sering terjadi dalam skala besar dalam produksi apel, sehingga menyebabkan kerugian ekonomi yang cukup besar. Oleh karena itu akan dibahas sebuah penelitian yang dimaksudkan untuk mengklasifikasikan penyakit daun apel dimana akan membantu para petani dalam menganalisa dan penanganan penyakit pada daun apel. Pengamatan manual adalah metode diagnosis tradisional yang kurang efektif,sehingga diusulkan metode otomatis menggunakan computer, yaitu menggunakan teknik pengolahan citra. Penelitian ini dilakukan untuk klasifikasi penyakit pada daun apel. Metode yang digunakan yaitu Convolutional Neural Network (CNN) untuk mengklasifikasi. Dataset sebanyak 3171 citra yang terdiri dari 4 kelas, yaitu Scab, Rust, Healthy, dan Blackrot. Hasil dari klasifikasi penyakit daun apel ini mendapatkan akurasi 98.73% yang artinya CNN adalah metode yang baik dan tepat untuk mengklasifikasikan penyakit daun apel

Nondestructive Multivariate Classification of Codling Moth Infested Apples Using Machine Learning and Sensor Fusion

Apple is the number one on the list of the most consumed fruits in the United States. The increasing market demand for high quality apples and the need for fast, and effective quality evaluation techniques have prompted research into the development of nondestructive evaluation methods. Codling moth (CM), Cydia pomonella L. (Lepidoptera: Tortricidae), is the most devastating pest of apples. Therefore, this dissertation is focused on the development of nondestructive methods for the detection and classification of CM-infested apples. The objective one in this study was aimed to identify and characterize the source of detectable vibro-acoustic signals coming from CM-infested apples. A novel approach was developed to correlate the larval activities to low-frequency vibro-acoustic signals, by capturing the larval activities using a digital camera while simultaneously registering the signal patterns observed in the contact piezoelectric sensors on apple surface. While the larva crawling was characterized by the low amplitude and higher frequency (around 4 Hz) signals, the chewing signals had greater amplitude and lower frequency (around 1 Hz). In objective two and three, vibro-acoustic and acoustic impulse methods were developed to classify CM-infested and healthy apples. In the first approach, the identified vibro-acoustic patterns from the infested apples were used for the classification of the CM-infested and healthy signal data. The classification accuracy was as high as 95.94% for 5 s signaling time. For the acoustic impulse method, a knocking test was performed to measure the vibration/acoustic response of the infested apple fruit to a pre-defined impulse in comparison to that of a healthy sample. The classification rate obtained was 99% for a short signaling time of 60-80 ms. In objective four, shortwave near infrared hyperspectral imaging (SWNIR HSI) in the wavelength range of 900-1700 nm was applied to detect CM infestation at the pixel level for the three apple cultivars reaching an accuracy of up to 97.4%. In objective five, the physicochemical characteristics of apples were predicted using HSI method. The results showed the correlation coefficients of prediction (Rp) up to 0.90, 0.93, 0.97, and 0.91 for SSC, firmness, pH and moisture content, respectively. Furthermore, the effect of long-term storage (20 weeks) at three different storage conditions (0 °C, 4 °C, and 10 °C) on CM infestation and the detectability of the infested apples was studied. At a constant storage temperature the detectability of infested samples remained the same for the first three months then improved in the fourth month followed by a decrease until the end of the storage. Finally, a sensor data fusion method was developed which showed an improvement in the classification performance compared to the individual methods. These findings indicated there is a high potential of acoustic and NIR HSI methods for detecting and classifying CM infestation in different apple cultivars