Tomato production is affected by various threats,including pests,pathogens,and nutritional deciencies during its growth process.If control is not timely,these threats affect the plant-growth,fruit-yield,or even loss o...Tomato production is affected by various threats,including pests,pathogens,and nutritional deciencies during its growth process.If control is not timely,these threats affect the plant-growth,fruit-yield,or even loss of the entire crop,which is a key danger to farmers’livelihood and food security.Traditional plant disease diagnosis methods heavily rely on plant pathologists that incur high processing time and huge cost.Rapid and cost-effective methods are essential for timely detection and early intervention of basic food threats to ensure food security and reduce substantial economic loss.Recent developments in Articial Intelligence(AI)and computer vision allow researchers to develop image-based automatic diagnostic tools to quickly and accurately detect diseases.In this work,we proposed an AI-based approach to detect diseases in tomato plants.Our goal is to develop an end-to-end system to diagnose essential crop problems in real-time,ensuring high accuracy.This paper employs various deep learning models to recognize and predict different diseases caused by pathogens,pests,and nutritional deciencies.Various Convolutional Neural Networks(CNNs)are trained on a large dataset of leaves and fruits images of tomato plants.We compared the performance of ShallowNet(a shallow network trained from scratch)and the state-of-theart deep learning network(models are ne-tuned via transfer learning).In our experiments,DenseNet consistently achieved high performance with an accuracy score of 95.31%on the test dataset.The results verify that deep learning models with the least number of parameters,reasonable complexity,and appropriate depth achieve the best performance.All experiments are implemented in Python,utilizing the Keras deep learning library backend with TensorFlow.展开更多
Unicellular micro-alga Chlamydomonas reinhardtii has been recognized as a promising host for expressing recombinant proteins albeit its limited utility due to low levels of heterologous protein expression. Here, trans...Unicellular micro-alga Chlamydomonas reinhardtii has been recognized as a promising host for expressing recombinant proteins albeit its limited utility due to low levels of heterologous protein expression. Here, transcription of the 3.4-kb mosquito-larvicidal cry4Ba gene from Bacillus thuringiensis in transgenic C. reinhardtii chloroplasts under control of the promoter and 5’-untranslated region of photosynthetic psbA gene was accomplished. Inverted repeats in chloroplast genomes of the host strain with deleted endogenous psbA genes were selected as recombination targets. Two transformant lines were obtained by dual-phenotypic screening via exhibition of resistance to spectinomycin and restoration of photosynthetic activity. Stable and site-specific integration of intact cry4Ba and psbA genes into chloroplast genomes found in both transgenic lines implied homoplasmy of organelle populations. Achievement in cotranscription of cry4Ba and psbA transgenes revealed by RT-PCR and Northern blot analyses demonstrates the sufficiency of this system’s transcription machinery, offering the further innovation for insecticidal protein production.展开更多
基金The authors extend their appreciation to the Deputyship for Research &Innovation, Ministry of Education in Saudi Arabia, for funding this research work through the Project No.IFT20065。
文摘Tomato production is affected by various threats,including pests,pathogens,and nutritional deciencies during its growth process.If control is not timely,these threats affect the plant-growth,fruit-yield,or even loss of the entire crop,which is a key danger to farmers’livelihood and food security.Traditional plant disease diagnosis methods heavily rely on plant pathologists that incur high processing time and huge cost.Rapid and cost-effective methods are essential for timely detection and early intervention of basic food threats to ensure food security and reduce substantial economic loss.Recent developments in Articial Intelligence(AI)and computer vision allow researchers to develop image-based automatic diagnostic tools to quickly and accurately detect diseases.In this work,we proposed an AI-based approach to detect diseases in tomato plants.Our goal is to develop an end-to-end system to diagnose essential crop problems in real-time,ensuring high accuracy.This paper employs various deep learning models to recognize and predict different diseases caused by pathogens,pests,and nutritional deciencies.Various Convolutional Neural Networks(CNNs)are trained on a large dataset of leaves and fruits images of tomato plants.We compared the performance of ShallowNet(a shallow network trained from scratch)and the state-of-theart deep learning network(models are ne-tuned via transfer learning).In our experiments,DenseNet consistently achieved high performance with an accuracy score of 95.31%on the test dataset.The results verify that deep learning models with the least number of parameters,reasonable complexity,and appropriate depth achieve the best performance.All experiments are implemented in Python,utilizing the Keras deep learning library backend with TensorFlow.
文摘Unicellular micro-alga Chlamydomonas reinhardtii has been recognized as a promising host for expressing recombinant proteins albeit its limited utility due to low levels of heterologous protein expression. Here, transcription of the 3.4-kb mosquito-larvicidal cry4Ba gene from Bacillus thuringiensis in transgenic C. reinhardtii chloroplasts under control of the promoter and 5’-untranslated region of photosynthetic psbA gene was accomplished. Inverted repeats in chloroplast genomes of the host strain with deleted endogenous psbA genes were selected as recombination targets. Two transformant lines were obtained by dual-phenotypic screening via exhibition of resistance to spectinomycin and restoration of photosynthetic activity. Stable and site-specific integration of intact cry4Ba and psbA genes into chloroplast genomes found in both transgenic lines implied homoplasmy of organelle populations. Achievement in cotranscription of cry4Ba and psbA transgenes revealed by RT-PCR and Northern blot analyses demonstrates the sufficiency of this system’s transcription machinery, offering the further innovation for insecticidal protein production.
