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"Plants use resources, i.e. carbon, nutrients, water and energy, either for growth or to defend themselves from biotic and abiotic stresses. This volume provides a timely understanding of resource allocation and its regulation in plants, linking the molecular with biochemical and physiological-level processes. Ecological scenarios covered include competitors, pathogens, herbivores, mycorrhizae, soil microorganisms, carbon dioxide/ozone regimes, nitrogen and light availabilities. The validity of the “Growth-differentiation balance hypothesis” is examined and novel theoretical concepts and approaches to modelling plant resource allocation are discussed. "

"This book provides a comprehensive account of interaction of host and its pathogens, induced host resistance, development of biological control agents for practical applications, the underlying mechanism and signal transduction. The book is useful to all those working in academia or industry related to crop protection."

"Tugas akhir ini merupakan penelitian yang ditujukan untuk mengembangkan model dinamik pertumbuhan tanaman dengan metode Artificial Neural Network (ANN), dimana model ini memetakan hubungan antara input (massa tanaman sebelum, nutrisi, usia, serta lingkungan) dan output (pertambahan massa tanaman periode berikutnya). Metode ini dipilih berdasar pertimbangan bahwa tanaman bisa dilihat sebagai satu sistem, dimana sistem ini cukup rumit karena bersifat dinamik, non-linear, dan time-variant. Penelitian yang akan dilakukan meliputi penanaman tanaman dengan metode deep water culture (DWC), pengambilan data tanaman dan lingkungan baik secara manual atau dengan sensor yang dikirim ke server, dan pelatihan ANN untuk menemukan model yang paling tepat. Data-data yang diambil selanjutnya diolah dan dipilah menjadi data pelatihan dan validasi. Data-data pelatihan dikumpulkan dalam database yang terdiri dari input dan output yang digunakan untuk melatih model. Terdapat beberapa model yang memiliki variasi gaya, arsitektur, dan kedalaman pelatihan (skor cost). Hasil akhir menunjukkan bahwa pemodelan pertumbuhan tanaman dengan ANN dapat dilakukan dan memiliki performa yang lebih baik daripada dengan pendekatan persamaan linear. Performa terbaik ditunjukkan oleh arsitektur residual dua sisi dengan rerata error mutlak 7.7634%.

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This final project is a research aimed at developing a dynamic model of plant growth using the Artificial Neural Network (ANN) method, where this model maps the relationship between inputs (prior plant mass, nutrition, age, and environment) and output (increase in plant mass for the next period) . This method was chosen based on the consideration that plants can be seen as a system, where the system is quite complicated because it is dynamic, non-linear, and time-variant. The research that will be carried out includes planting plants with a deep water culture (DWC) method, taking plant and environmental data either manually or with sensors sent to the server, and ANN training to find the most appropriate model.

The data taken is then processed and sorted into training and validation data. Training data is collected in a database consisting of inputs and outputs used to train the model. There are several models that have variations in style, architecture, and depth of training (cost score). The final results show that modeling of plant growth with ANN can be done and has better performance than the linear equation approach. The best performance is shown by the two-sided residual architecture with an average absolute error of 7.7634%."

"Tujuan dari proyek skripsi tersebut adalah untuk mengimplementasikan Sensor Warna RGB ke dalam the University of Queensland Farmbot dan meningkatkan kapabilitasnya yang mengontrol pertumbuhan tanaman panen dalam lingkungan terkontrol. Caranya, dengan menganalisasi dan mengukur kondisi tumbuhan dari warna yang terefleksi dari permukaan daun tumbuhan. Tujuan langsung dari proyek ini adalah untuk melihat apakah sensor RGB, dengan konsumsi dayanya yang rendah, penggunaan yang simpel, dan harga produksi yang rendah untuk digunakan sebagai alternatif untuk alat pengukuran kamera yang sudah tersedia dalam Farmbot. Saat ini, penggunaan dari sensor RGB untuk mengukur pertumbuhan daun sebagai indikator kesehatan sanggatlah terbatas yang diteliti. Jika terbukti sukses, sensor RGB dapat menyediakan sarana yang layak untuk pengukuran kesehatan tumbuhan yang non-destruktif atau non-invasi. Proyek ini memeriksa dan membandingkan hasil yang diterima dari sensor dengan database yang tersedia untuk menentukan kondisi tumbuhan. Untuk dapat meningkatkan kapabilitas sensor RGB, sensor tambahan akan digunakan. Sensor tersebut adalah sensor infrared thermal dengan mengambil ukuran temperatur permukaan daun, dan 6-channel spectrometer untuk membandingkan hasil keluaran sensor yang telah di kalibrasi dengan hasil sensor RGB. Perbandingan tersebut dapat meningkatkan keandalan dan akurasi data. Proyek tersebut juga membandingkan hasil dari sensor RGB, spectrometer, dan thermal sensor dengan macam-macam senyawa kimia yang terdapat pada daun tumbuhan. Hasil proyek telah menunjukkan bahwa sensor RGB bekerja cukup baik jika mengukur intensitas warna, tepat jika sensor di kalibrasi, dan keluaran hasil respons yang cepat. Dari hasil tersebut telah memenuhi tujuan dari penggunaan sensor RGB yang dapat di implementasikan ke dalam Farmbot untuk deteksi warna yang akurat. Tetapi, hasil indeks RGB tidaklah cukup untuk menentukan klasifikasi kesehatan atau kondisi tumbuhan tanpa menggunakan sensor yang lain. Saat ini, belum ada klasifikasi empiris ataupun relasi dengan aspek matematika yang dapat digunakan dalam penentuan kondisi tumbuhan dengan RGB secara langsung. Tetapi dengan inklusi sensor thermal dan spectrometer, fungsionalitas RGB meningkat secara signifikan.

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The purpose of this thesis project is the implementation of RGB Colour Sensor into the University of Queensland Farmbot and improve its capability of controlled crop growth by analysing and measuring plants conditions using colours that is reflected of the surface of crop leaves. The immediate goal of this project is to see whether RGB Colour Sensor, with its low power consumption, usage simplicity, and low production cost to be used as an alternative measurement tool to an already existing image sensor in the Farmbot. Currently, the use of colour sensor to measure plants leave colour as an indicator for health has been limitedly research. If prove to be successful, RGB Colour Sensor could provide as a viable mean of non destructive or non invasive measurement of plants health status.

The project examines and compares the data achieved through the sensor and compares with existing databases to determine the plants condition. Additional sensors are also used to help increase the capability of the RGB Sensor. These sensors include infrared thermal sensor for getting temperature, and 6 channel visible light spectrometer to compare its calibrated output with the RGB Colour sensor to increase data reliability and accuracy. This project also compares result of the RGB sensor, spectrometer, and temperature with different chemical compounds that are found in leaves of plants.

Results shows that RGB sensor works quite well when measuring colour intensities, it is accurate when calibrated, and quick output response. This meets the objective where RGB sensor can be implemented in the Farmbot for accurate colour detection. However, RGB index alone is not enough to determine any classification of health or plants condition without the use of other complimentary sensors. There are not yet any empirical classifications or mathematical relations that can be used to determine plants condition with RGB. However, with the inclusion of the thermal and spectrometer, RGB sensor functionality increases significantly."

"Bacteria in agrobiology : stress management, covers the major aspects on PGPR in amelioration of both abiotic and biotic stresses. PGPR mediated in priming of plant defense reactions, nutrient availability and management in saline and cold environment, hormonal signaling, ACC deaminase and its role in ethylene regulation under harsh conditions are suitably described."

"Soil fungi occurred in the rhizosphere in many cultivated crops and wild grasses in the sandy soil were effective in enhancing the growth promoting of plant. A field experiment to study the effect of soil fungi from sandy soil as plant growth promoting fungi (PGPF). The experiment was conducted in Clinical Laboratory of Plant Pathology and glass house Faculty of Agriculture Gadjah Mada University in Yogyakarta. The sandy soil was located in Samas and Bugel Yogyakarta. The treatment was conducted in Complete Randomize Design (CRD) with 5 replications. Among 33 rhizosphere fungi tested, 26 fungal isolates were hypovirulent isolate and showed the ability as PGPF i.e. Trichoderma spp. (SB32 , CB32, SB33, CB33 and CB21). They enchanced significantly the height and dry biomass of plants compare with control."